D6.1.2-Toolsets for Enabling ADE Detection on EHRs based on … · 2017-04-17 · FP7-287800 SALUS...

SALUS “Scalable, Standard based Interoperability Framework for

Sustainable Proactive Post Market Safety Studies”

SPECIFIC TARGETED RESEARCH PROJECT PRIORITY Objective ICT-2011.5.3b) Tools and environments enabling the re-use of electronic health records

SALUS D6.1.2 Toolsets for Enabling ADE Detection on EHRs based on temporal patterns

Due Date: March 31, 2014 Actual Submission Date: March 31, 2014 Project Dates: Project Start Date : February 01, 2012

Project End Date : January 31, 2015 Project Duration : 36 months

Deliverable Leader: OFFIS

Project co-funded by the European Commission within the Seventh Framework Programme (2007-2013)

Dissemination Level

PU Public PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) X CO Confidential, only for members of the consortium (including the Commission Services)

FP7-287800 SALUS

SALUS-FP7-287800• D6.1.2 • Final Version, dated March 31, 2014 of 90

Document History: Version Date Changes From Review

V0.1 September 28, 2012

initial template OFFIS All Consortium

V0.2 October 29. 2012

TUD input for ADE Detection Rules TUD SRDC, OFFIS

V0.3 January 11, 2013 OFFIS input for ADE Detection Rules OFFIS All Consortium

V0.4 January 25, 2013 - Minor changes regarding ADE Detection Rules

- first draft of chapter 3

TUD, OFFIS All Consortium

V0.5 February 22, 2013

- first draft of chapter 2 (Detection of known ADEs)

- LISPA input regarding the ADE Detection Rules

- UMC comments on ADE Detection Rules

OFFIS, LISPA, UMC

All Consortium

V0.6 May 06, 2013 - incorporated feedback

- executive summary

- Appendix

- introduction chapters

- first subsections of implementation chapter

OFFIS, SRDC, UMC

All Consortium


- improvements in implementation chapter

OFFIS, SRDC, UMC

All Consortium


- new introduction to chapter 4

- new subsection 5.2.3

OFFIS, SRDC, TUD

All Consortium

V1.1 March 03, 2014 - initial version of D6.1.2 OFFIS All Consortium

V2.0 March 03, 2014 - final version of D6.1.2 OFFIS, AGFA, LISPA, TUD, UMC, SRDC

All Consortium

FP7-287800 SALUS


Contributors (Benef.) Tobias Krahn (OFFIS), Frerk Müller (OFFIS), Marco Eichelberg (OFFIS)

Dheban Srirangan (TUD), Prof. Peter Schwarz (TUD) Tomas Bergvall (UMC), Johan Ellenius (UMC) Andrea Migliavacca (LISPA), Paolo Invernizzi (LISPA), Sara Facchinetti (LISPA), Filomena Fortinguerra (LISPA) Gokce Banu Laleci Erturkmen (SRDC), A. Anil Sinaci (SRDC), Suat Gönül (SRDC) Marc Twagirumukiza (AGFA)

Responsible Author Tobias Krahn Email [email protected]

Beneficiary OFFIS Phone +49-441-9722-145

FP7-287800 SALUS


SALUS Consortium Contacts:

Beneficiary Name Phone Fax E-Mail SRDC Gokce Banu Laleci

Erturkmen +90-312-2101763 +90(312)2101837 [email protected]

EUROREC Georges De Moor +32-9-2101161 +32-9-3313350 [email protected] UMC Niklas Norén +4618656060 +46 18 65 60 80 [email protected] OFFIS Wilfried Thoben

+49-441-9722131

+49-441-9722111

[email protected]

AGFA Dirk Colaert +32-3-4448408 +32 3 444 8401 [email protected] ERS Gerard Freriks +31 620347088 +31 847371789 [email protected] LISPA Alberto Daprà +390239331605 +39 02 39331207 [email protected] INSERM Marie-Christine Jaulent +33142346983 +33153109201 marie-

[email protected] TUD Peter Schwarz +49 351 458 2715 +49 351 458 7319 Peter.Schwarz@uniklinikum-

dresden.de ROCHE Jamie Robinson +41-61-687 9433 +41 61 68 88412 [email protected]

FP7-287800 SALUS


EXECUTIVE SUMMARY Within this document, D6.1.2 – “Toolsets for Enabling ADE Detection on EHRs based on temporal patterns”, the ADE Notification Tool (ANT) will be described. The main objective of the ANT is on the one hand to detect all kinds of Adverse Drug Events (ADEs) and on the other hand to notify the clinical experts about each suspicious event. In terms of the ANT, ADEs are separated in two classes: known ADEs and unknown ADEs. The first class describes the currently already known events. They are already confirmed and published in the “Summary of Product Characteristics” (SPC) of a drug. The second class covers the currently unknown ADEs. These events are not confirmed as an ADE yet and require further investigation. In case of a known ADE, the clinical expert gets a simple notification, but if the ANT detects a suspicious event that is not an already known ADE, the physician gets alarmed. In addition, all relevant data fields that lead to the detection are displayed to obtain the physician’s opinion. If he confirms an event as an ADE, the reporting process gets triggered with the help of the “Individual Case Safety Reporting Tool” (IRT). In view of the SALUS architecture, the ANT will be deployed on the SALUS “Semantic Interoperability Layer” (SIL), which is able to translate clinical data from the hospital’s EHR system into the standardized SALUS representation utilizing the SALUS “Common Information Model” (CIM). One of the main objectives of the ANT is to enable the detection of ADEs regardless of the EHR’s data quality: even though there is only a little data available in the EHR, the ANT will try to detect suspicious events. The focus is on the deployment in the hospitals of our consortium partner TUD, but to show the scalability of the ANT, a prototypical implementation in the clinical setting of our consortium partner LISPA is planned as well. The first prototype of the ANT was delivered at Month 16. It focused on the detection of already known ADEs, contained a detection rule repository to detect ADEs by other parameters, like special drug prescriptions or lab results, and was an easy to extend vertical prototype. This document describes the concept and implementation of the second prototype, which is due to Month 26 and covers a rule management component along with an extended ADE detection rule repository including several ADE detection approaches.

FP7-287800 SALUS


TABLE OF CONTENTS Executive Summary ................................................................................................................................ 5 Table of contents ..................................................................................................................................... 6 1 Introduction .................................................................................................................................... 7

1.1 Purpose .................................................................................................................................... 7 1.2 Reference documents .............................................................................................................. 7 1.3 Abbreviations and Acronyms .................................................................................................. 8

2 Detection of known ADEs ............................................................................................................. 9 3 ADE Detection Rules ................................................................................................................... 13

3.1 TUD: Rules based on laboratory parameters ........................................................................ 13 3.2 LISPA: Rule concepts ........................................................................................................... 15 3.2.1 Rules defined in function of ADEs reported in the past ................................................... 16 3.2.2 Rules based on specific diseases as triggers of ADEs ...................................................... 20 3.2.3 Rules based on presence of interaction between two or more drugs ................................ 24 3.2.4 Rules based on request of specialistic examinations or diagnostic tests ........................... 27 3.3 Rules based on the prescription of specific antidotes ........................................................... 39 3.4 Changes in drug therapy ....................................................................................................... 48 3.5 Rules based on postmarketing information ........................................................................... 48 3.6 Rules based on ADE related diagnoses ................................................................................. 49 3.7 Drug interactions ................................................................................................................... 49 3.8 Published Rules ..................................................................................................................... 49

3.8.1 PSIP Project .................................................................................................................. 49 3.8.2 Rules published in the paper “Retrospective analysis of the frequency and recognition of adverse drug reactions by means of automatically recorded laboratory signals” by Tegeder et al. 56 3.8.3 Rules published in the paper “Computerized surveillance of adverse drug reactions in hospital: pilot study” by Azaz-Livshits et al. ............................................................................... 57 3.8.4 Rules published in the paper “Implementation of a System for Computerized Adverse Drug Event Surveillance and Intervention at an Academic Medical Center” by Kilbridge et al. 58

4 Detection of unknown ADEs ....................................................................................................... 60 4.1 Bump Hunting for ADE detection ........................................................................................ 60 4.2 Clustering method to detect unknown ADEs ........................................................................ 66

5 Implementation of the ADE Notification Tool ........................................................................ 68

6 Conclusion ................................................................................................................................ 77

FP7-287800 SALUS


1 INTRODUCTION

1.1 Purpose This document describes the prototypical implementation of the ADE Notification Tool developed to detect and notify clinical experts in a hospital setting on the presence of ADEs. The Deliverable itself contains the method descriptions and approaches to detect known and unknown ADEs. As another component of the toolset, we developed an ADE Detection Rule repository that will also be described in this document. Moreover, the technical details of the tool components will be presented. In the description of work regarding task 6.1 the following is specified: “In order to increase the reporting rates of ADEs, intelligent data analysis algorithms will be developed within this task that enable proactive real time screening of EHRs of a patient to enable ADE detection. These screening algorithms will be based on approaches from temporal data mining for the identification of patterns in unstructured, high-dimensional, temporally oriented data and will be deployed to pilot hospitals in order to execute over the information management system of the hospital. In this task special emphasis will be on extracting temporal relations to identify links among drug use and adverse events in EHRs fed to the analysis tool. The developed ADE algorithms will deduce the chronological order of the events to determine relations among them which are especially necessary for detecting ADEs. A possible inclusion of Time To Onset (TTO) data will be evaluated. Finally, a user interface will be developed to present the suspected ADE list back to the medical practitioners during patient’s doctor visit seamlessly.” To be able to detect unknown ADEs, the current knowledge on the set of known ADEs is required, particularly in the light of the fact that just the currently unknown ADEs are of interest to be reported. This document is structured as follows. Section 2 describes the methods and resources for the detection of known ADEs. The ADE detection rules repository is described in Section 3. Section 4 introduces our data mining approach to detect unknown ADEs. Afterwards, in Section 5, the technical details of the ADE Notification Tool including the technical implementation details are described.

1.2 Reference documents The following documents were used or referenced in the development of this document:

• SALUS Description of Work (SALUSPartB_20110118.pdf) • SALUS Deliverable 8.1.1 - Pilot Application Scenario and Requirement Specifications of the

Pilot Application • SALUS Deliverable 3.3.1 - Requirement Specification of the SALUS Architecture • SALUS Deliverable 3.4.1 - Conceptual Design of the SALUS Architecture • SALUS Deliverable 4.3.1 - SALUS Harmonized Ontology for Post Market Safety Studies • SALUS Deliverable 5.3.1 - Interoperability Profiles and Open Source Toolsets for Reporting

Activities for Post Market Safety Studies

FP7-287800 SALUS


1.3 Abbreviations and Acronyms Table 1: List of Abbreviations and Acronyms

Abbreviation/ Acronym DEFINITION

ADE Adverse Drug Event ABDA Bundesvereinigung Deutscher Apothekerverbände ADR Adverse Drug Reaction AIFA Agenzia Italiana del Farmaco (Italian Medicines Agency) AIC Autorizzazione all’Immissione in Commercio (Marketing Authorization number) ATC Anatomical Therapeutic Chemical ANT ADE Notification Tool CIM Common Information Model DB Database

DWH Data Warehouse EHR Electronic Health Record EMA European Medicines Agency

EU-ADR Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge

FDA Food and Drug Administration IC Information Component

ICD-9-CM International Classification of Diseases, 9th Rev., Clinical Modification ICD-10 International Statistical Classification of Diseases and Related Health Problems, 10th rev ICSR Individual Case Safety Report INR International Normalized Ratio IRT ICSR Reporting Tool LDC Linked Data Cloud

LISPA Lombardia Informatica S.p.A. LOINC Logical Observation Identifiers Names and Codes

MedDRA Medical Dictionary for Regulatory Activities PRIM Patient Rule Induction Method

PROTECT Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium

SIDER Side Effect Resource SIL Semantic Interoperability Layer

SIL DS Semantic Interoperability Layer Data Service SPARQL SPARQL Protocol And RDF Query Language

SPC Summary of Product Characteristics SSRIs Selective serotonin reuptake inhibitors TTO Time To Onset TUD Technical University of Dresden VKA Vitamin K antagonist XML eXtensible Markup Language

FP7-287800 SALUS


2 DETECTION OF KNOWN ADES To be able to detect unknown ADEs, the set of currently known ADEs has to be considered. For the detection of known ADEs we will use data from external sources. In Germany for example, the “ABDA”-database contains more than 210.000 data entries on marketed drugs that can be queried1, but the use of this database is not free of charge and the ADEs in that database are not stored in a coded format. As an appropriate coding system for ADEs, “MedDRA” experiences a wide international acceptance.2 Furthermore, the terminology server component developed within the SALUS project supports several mappings between MedDRA and other classification systems. This enables us for example to translate ICD coded diagnoses into MedDRA codes and vice versa. In section 2.1, officially accessible data sources containing coded data on already known ADEs will be presented. For the usage in the ANT, these data sources are consolidated in a single database (see section 2.2). 2.1 Data Sources for known ADEs One objective of the design of the ANT is to facilitate an easy integration of external data sources containing data on already known ADEs. In this section, these data sources are described. 2.1.1 PROTECT DB During another EU-Project, called PROTECT (“Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium”)3, the project consortium together with the EMA (“European Medicines Agency”) created a database containing all ADRs from the Summary of Product Characteristics (SPC) of drugs authorized in the EU. With the help of this database, they aim to increase the efficiency of the signal detection process since the determination of whether an ADR is already known or not is a time-consuming step. The database is an Excel file containing all ADRs in combination with their related MedDRA codes. The drugs are recorded in form of product names and substances of a product (see Figure 1). The PROTECT DB is intended for signal detection activities and research purposes. Currently, the content has been updated on 30 June 2012 and it is planned to update this database routinely, at least once a year. 4 Regarding the ANT, the PROTECT database brings a tremendous added value: To enable the detection of unknown ADEs, the latest state of known ADEs has to be considered in order to prepare and ease the reporting process of suspected unknown ADEs. Therefore the PROTECT DB is an ideal data source to start with.

1 http://www.dimdi.de/static/de/db/dbinfo/ae00.htm 2 http://www.meddra.org/how-to-use/support-documentation/english 3 http://www.imi-protect.eu 4 http://www.imi-protect.eu/documents/ADRdatabase_introduction.pdf

FP7-287800 SALUS


Figure 1: Screenshot of PROTECT database

2.1.2 Other data sources The drugs listed in the PROTECT DB (see section 2.1) are approved for the EU market. However, the pharmaceutical companies also have the opportunity to request an approval only for a subset of the EU countries. To facilitate the accessibility of other data sources containing drugs and known ADRs, the ANT will support Linked Data. By the connection of open available data sources, the amount of machine processable medical data is constantly growing. The current status of this interlinking process is shown in Figure 2.

FP7-287800 SALUS


Figure 2: The Linked Open Data Cloud5

The purple coloured data sources within this figure show the extent to which medical data is available within the Linked Data Cloud. These data sources can be queried by the help of the semantic query language SPARQL. For the purposes of our SALUS project, we identified three data sources that can be used (see the red marked data sources in Figure 1):

- SIDER: SIDER (“Side Effect Resource”) contains linked data about marketed drugs in conjunction with their known ADRs. Currently this data source contains more than 215.000 ADRs. Each ADR is listed as MedDRA preferred name in combination with the ATC name of the drug.6

- DailyMed: DailyMed is a database of the “Food and Drug Administration” (FDA) agency of the United States. DailyMed contains high qualitative specific information about animal and human drugs. Furthermore, this data source includes information on all known ADRs that are described in the summary of product characteristics, but not in a coded representation. The current version contains data on more than 46.000 drugs.7

- DrugBank: DrugBank is a database of the University of Alberta and contains among

chemical information very detailed information about drugs, like for example data on typical patient groups or dosage forms of a drug. In summary, DrugBank includes more than 6.700 data entries.8

5 Cyganiak, Richard (2011): The Linked Open Data Cloud, see http://lod-cloud.net for further details. 6 http://sideeffects.embl.de 7 http://dailymed.nlm.nih.gov/dailymed/ 8 http://www.drugbank.ca

FP7-287800 SALUS


Nearly the complete data that is accessible by these sources is sent by pharmaceutical industries - therefore the data is trustable. The data can be downloaded and locally installed to ensure high availability. 2.2 Database for Known ADE Detection The data from the data sources presented in section 2.1 was used, imported and consolidated into a single database. In addition, the available background information on age/gender groups, the causality and frequency of the known ADEs following the data structure of the PROTECT DB (see setion 2.1.1) was also imported into that database to enable a rough estimation of the probability for a known ADE. Figure 3 shows a small sample of the final database for the known ADE detection module of the ANT.

Figure 3: Sample from the Known ADE Detection database

The database contains in total more than 160.000 data entries on known ADEs. However, if new information on known ADEs becomes available, it can be easily updated and extended.

FP7-287800 SALUS


3 ADE DETECTION RULES The detection of known ADEs presented in chapter 2 can be seen as a rule concept like

[drug] à [diagnosis]

under the temporal condition that the diagnosis has been made after the drug has been taken. However, this pattern is not the only one that will be traced by the ANT. For example, even if a diagnosis isn’t available, an ADE can be detected in the blood profile by monitoring specific parameters as indicators of ADEs. In the literature, many different types of adverse event detection rules are published. A lot of these rules consist of one or two conditions leading to a specific effect. Condition combinations can contain a drug and another drug, a drug in connection with a lab result, a drug alone with its dose, a drug and a patient characteristic, a drug with its dose and a patient characteristic or a drug and a chronic disease. Furthermore, in the SALUS project, we want to consider additional indications for adverse drug events like for example drug discontinuations or specific antidotes. In the following sections, all detection rule concepts used by the ANT will be described. In section 3.1 and 3.2 ADE detection rules composed by the SALUS medical partners TUD and LISPA will be presented – these rules are directly proposed by their medical experts who know their local data sets and workflows. The other sections will show rule concept ideas taken from the published literature.

3.1 TUD: Rules based on laboratory parameters There are several indicators of adverse drug events, e.g. liver-, kidney-, muscle-parameters or bone-marrow- and electrolyte-values. All these parameters are summarized in three major concepts of ADE detection: Concept 1: This concept is based on muscle-, liver- and kidney-parameters that need to be in a normal range before the prescription of a drug. An ADE can be confirmed after either one or more of those parameters of a patient has/have changed more than twice of the reference value after drug prescription. The following table 2 shows three parameters with their corresponding reference values and cut-off values for ADE detection. Parameters Normal range (before

prescription) ADE detection rule (after prescription)

ALAT (Liver) Male: 10 - 50 U/l

Female: 10-35 U/l

2 x normal value (value before drug prescription)

Myoglobin (Muscle) Male: 20 – 70 µg/l

Female: 16 – 60 µg/l


Creatinine (Kidney) Blood: Male: 74 – 110 µmol/l

Female: 58 – 96 µmol/l


Table 2: Concept 1 – muscle, liver- and kidney-parameters

FP7-287800 SALUS


Concept 2: The second concept contains parameters associated with the bone marrow. In order to detect an ADE one needs to specify the number of leuko- and erythrocytes. Low numbers of leuko- and erythrocytes can indicate an ADE occurance. An ADE can be detected if either the number of leukocytes or erythrocytes shrinks more than 30 % of the normal value after drug prescription. Following table 3 shows both parameters with their corresponding normal range and their ADE detection rules.

Parameters Normal range (before prescription) ADE detection rule (after prescription)

Number of leukocytes Up to 6 years: 5.0 – 14.5 x 1000/µL

Up to 10 years: 4.5 - 13.5 x 1000/µL

Up to 14 years: 4.5 – 13.0 x 1000/µL

Adults: 4.4 – 11.3 x 1000/µL

Shrinkage of more than 30% of reference value (value before drug prescription)

Number of erythrocytes Children: 5-10 years: 3.9 – 5.1 million/µL 10-12 years: 4.1 – 5.2 million/µL

Adults: Women: 4.1 – 5.1 million/µL Men: 4.5 -5.9 million/µL

Shrinkage of more than 30% of reference value (value before drug prescription)

Table 3: Concept 2 – bone marrow-parameters

Concept 3: This concept for ADE detection is based on electrolytes. Major electrolyte measures are natrium, potassium, chloride and calcium. These measures are very sensitive and hence are able to give a good feedback on reactions associated with drugs. In general, a 20 % change of the normal value can indicate an ADE occurrence. Particularly, natrium and potassium are well known to interact with drugs resulting in side effects. The following table 4 shows these parameters with their corresponding normal values and ADE detection rules.

Parameters Normal range (before prescription)

ADE detection rule (after prescription)

Natrium 134 - 145 mmol/L At least 20% change of normal value (value before drug

FP7-287800 SALUS


prescription) – and the value being outside the normal range

Potassium 3.4 - 5.2 mmol/L At least 20% change of normal value ( value before drug prescription) – and the value being outside the normal range

Chloride 95 - 112 mmol/L At least 20% change of normal value (value before drug prescription) – and the value being outside the normal range

Calcium 12 - 15 years: 2.10 – 2.50 mmol/L

15 - 18 years: 2.15 – 2.45 mmol/L

18 - 120 years: 2.15 - 2.65 mmol/L

At least 20% change of normal value (value before drug prescription) – and the value being outside the normal range

Table 4: Concept 3 – Electrolytes

3.2 LISPA: Rule concepts The ADE rules currently identified by TUD are not applicable in the LISPA SALUS pilot, because of the lack of laboratory results in the data warehouse (DWH) of Lombardy Region. LISPA proposes different query patterns applicable as ADE detection rules on the basis of available data in SALUS LISPA DWH which is not an EHR hospital system but is a regional health data system. This system contains data regarding all interactions between a citizen and the regional public health systems (clinical and administrative data). In particular data to be used within the scope of SALUS project are: - Pharmaceutical (drugs prescriptions in primary care) - Pharmaceutical in hospital setting (drugs provision in hospital care at patient discharge or day

hospital) - Hospitalization (hospital discharge letter) - Ambulatory (laboratory or other diagnostic tests, …) - Vaccinations (vaccines information) - Allergies - Patient clinical status (clinical condition calculated by an elaboration process based on clinical

history) - Patient pregnancy status (elaborated data)

SALUS project takes advantage in analysing each single data flow and connections between different data sources (e.g. patient with a drug prescription in primary care (pharmaceutical data source) -> adverse drug event which requires hospitalization -> information about hospital stay (hospitalization and pharmaceutical in hospital data sources)). The ADE detection rules for LISPA pilot are designed with a novel approach which uses clinical and administrative data together. The proposed rules, analysed with the Lombardy regional pharmacovigilance center, are grouped in four concepts (see Figure 4):

FP7-287800 SALUS


1. Rules defined on the basis of ADE reported in the past (known ADEs);

2. Rules based on specific diseases as trigger of ADE (known and unknown ADEs);

3. Rules based on presence of interaction between two or more drugs (known ADEs);

4. Rules based on request of specialistic examination or diagnostic tests as trigger of ADE (known

and unknown ADEs).

For every identified rule a tuning phase is foreseen during pilot activities. Within this phase each rule will be adjusted according to the needs highlighted through the use of the rule on the real databases.

3.2.1 Rules defined in function of ADEs reported in the past These rules are focused to find possible ADE not yet reported by involved GPs but already known (previously reported in the pharmacovigilance process). Rules definition approach: The rules are defined using frequent ADEs reported to AIFA by Lombardy region. The ADE report analysis allows to identify correlations between drug – adverse event – indicator. Strengths:

• Easy to define; • Useful to manage and monitor any specific event.

Weaknesses:

• The system is focused only on known ADE previously reported. Rule structure

Figure 4: Overview of LISPA specific ADE detection rule concepts

FP7-287800 SALUS


1st step - Rules setup A spontaneous ADEs regional DB is available and contains: drug (AIC code) – active principle (ATC code) – Diagnosis (MedDRA code) Using Semantic framework MedDRA codes are mapped to ICD-9 CM codes, results are stored in a new table: drug (AIC code) – active principle (ATC code) – Diagnosis (ICD9 code) Examples The following associations examples (see table 5) have been extracted from the regional ADEs DB of 2012

PT_code PT_desc_eng Drugsubstance ATC_CODE

10015090 Epistaxis WARFARIN B01AA03

10018867 Haematuria WARFARIN B01AA03

10002034 Anaemia WARFARIN B01AA03

10038063 Rectalhaemorrhage WARFARIN B01AA03

10022594 International normalised ratio decreased

WARFARIN B01AA03

10008111 Cerebralhaemorrhage WARFARIN B01AA03

10018852 Haematoma WARFARIN B01AA03

10066373 Floppy iris syndrome TAMSULOSIN G04CA02

10021036 Hyponatraemia FUROSEMIDE C03CA01

10042361 Subduralhaematoma WARFARIN B01AA03

10020646 Hyperkalaemia SPIRONOLACTONE C03DA01

10027141 Melaena WARFARIN B01AA03

10015090 Epistaxis CLOPIDOGREL B01AC04

10013968 Dyspnoea AMOXICILLIN/CLAVULANANIC ACID J01CR02

10021036 Hyponatraemia AMILORIDE /HYDROCHLOROTHIAZIDE C03EA01

10000087 Abdominalpainupper KETOPROFEN M01AE03

10015090 Epistaxis ACETYLSALICYLIC ACID B01AC06

10029354 Neutropenia LENALIDOMIDE L04AX04

10021097 Hypotension FUROSEMIDE C03CA01

10042345 Subcutaneoushaematoma WARFARIN B01AA03

10065304 Spontaneoushaematoma WARFARIN B01AA03

10002034 Anaemia ACETYLSALYCILIC ACID B01AC06

10012735 Diarrhoea AMOXICILLIN/CLAVULANIC ACID J01CR02

10020993 Hypoglycaemia METFORMIN/GLIBENCLAMIDE A10BD02

10029354 Neutropenia RITUXIMAB L01XC02

10020646 Hyperkalaemia RAMIPRIL C09AA05

10013968 Dyspnoea CARBOPLATIN L01XA02

10033775 Paraesthesia OXALIPLATIN L01XA03 10038436 Renalfailure acute FUROSEMIDE C03CA01

10020646 Hyperkalaemia ENALAPRIL C09AA02

10013968 Dyspnoea RITUXIMAB L01XC02

10002034 Anaemia CLOPIDOGREL B01AC04

10021015 Hypokalaemia FUROSEMIDE C03CA01

FP7-287800 SALUS


10021036 Hyponatraemia CARBAMAZEPINE N03AF01

10028309 Musclehaemorrhage WARFARIN B01AA03

10016936 Folliculitis CETUXIMAB L01XC06

10012735 Diarrhoea CAPECITABINE L01BC06

10009657 Clostridium difficile colitis CEFTRIAXONE J01DD04

10017955 Gastrointestinalhaemorrhage WARFARIN B01AA03

10038436 Renalfailure acute ENALAPRIL C09AA02

10042772 Syncope FUROSEMIDE C03CA01

10021114 Hypothyroidism AMIODARONE C01BD01

10043554 Thrombocytopenia LENALIDOMIDE L04AX04

10013983 Dystonia METOCLOPRAMIDE A03FA01

10020642 Hyperhidrosis OXALIPLATIN L01XA03

10002965 Aplasia pure redcell EPOETIN BETA B03XA01

10000087 Abdominalpainupper PACLITAXEL L01CD01 10033775 Paraesthesia OXALIPLATINO L01XA03

Table 5 - Drugs – Diagnosis associations examples

2nd step – Rules running

Search period: last 2 month

In SALUS-LISPA DWH, on the basis of the 1st step performed on existing dataset of ADEs reporting,

it is established to perform a data search on the last two months period (SALUS LISPA DWH is

updated on a monthly basis).

When a prescription drug is found then T0 is established (T0=prescription index):

• Search a drug (a specific AIC code or ATC code) AND related ADE (ICD9)

• Search for all the specific drugs prescribed after T0

• the ICD9 has to be in a date after any purchase of the drug in study and in a date not greater

than drug purchase + DDD9 + X weeks/months/years (variable case by case)10

• in the six months before prescription index, there must not be any prescriptions of the same

drug, i.e. the patients must be newly treated

• in the six months before prescription index, there must not be the same ICD-9 diagnosis, i.e.

the patients must not have the same event before the new treatment

A temporal diagram of this rule is as follow (see Figure 5):

9 DDD are Defined Daily Dose by WHO and can be used for calculate the coverage (in days) of the drug 10 To simplify the approach we can consider X = max 8 weeks

FP7-287800 SALUS


In SALUS-LISPA DWH the rule is implemented with the following steps:

1. For drug search:

Table: DRUGS.

Field: ATC_CODE or/and AIC_CODE

Field: DELIVERY_DATE used for compared with the event encountered

2. For diagnosis ICD9

Table: HOSPITALIZATION

Field: DIAGNOSIS_CODE X_ICD9CM (where X is from 1 to 6)

Field: DATE_OF_EVENT is greater or equal than DRUGS. DELIVERY_DATE and minor

than DRUGS.DELIVERY_DATE + DDD + X weeks/months/years (it depends on the drug-

diagnoses). In the six months before index prescription there must be no purchase of that

drug, i.e. the patients must be newly treated.

3. Alternative search for diagnosis ICD9

Table: AMBULATORY

Field: DIAGNOSIS_CODE_ICD9CM

Field: START_DATE is greater or equal than DRUGS.DELIVERY_DATE and minor than

DRUGS.DELIVERY_DATE + X weeks/months/years (it depends on the drug). In the six

months before index prescription there must be no purchase of that drug, i.e. the patients must

be newly treated.

4. For diagnosis ICD9 check in T0-X months


Field: DIAGNOSIS_CODE X_ICD9CM (where X is from 1 to 6 depending on the number of

diagnosis identified in >T0)

Field: DATE_OF_EVENT is previous than DRUGS. DELIVERY_DATE - X

weeks/months/years (X from 1 to 8 weeks). In the period before drug prescription there must

be no occurrences of the identified diagnosis in>T0.

5. Alternative search for diagnosis ICD9 check in T0-X months

Table: AMBULATORY

T0 (prescription index) DRUG X DRUG X DRUG X

T0-6 months

NO DRUG X (AIC and ATC)

NO DIAGNOSIS Y (ICD9)

DRUG X + DDD + X WEEKS

DIAGNOSIS Y

Figure 5: temporal diagram LISPA rule concept #1

FP7-287800 SALUS



Field: START_DATE is previous than DRUGS. DELIVERY_DATE - X weeks/months/years

(X from 1 to 8 weeks). In the period before drug prescription there must be no occurrences of

the identified diagnosis in>T0.

It should be noted that, the ADE detection rules are not directly executed on top of LISPA DWH. Through the mechanisms presented in Section 5, patient data is collected from the underlying EHR sources (such as LISPA DWH) through semantic and technical interoperability layers of SALUS, in a common model conforming to SALUS Common Information Model.

3.2.2 Rules based on specific diseases as triggers of ADEs These rules are based on studies like EU-ADR. This is a list of diseases that frequently could be caused by drugs. The rules have to disclose the possible relationship between a specific disease and the drug use. Rules definition approach: Starting from a diseases list elaborated i.e. by EU-ADR (and modified) related with drug use it is possible define a specific matrix. Strengths:

• Literature support; • Well identified field.

Weaknesses:

• Limitation in new ADE type identification; • The diagnosis has to be included in the discharge letter.

Examples:

• Thrombocytopenia • Upper gastrointestinal bleeding

Rules running Search period: last 1 month

Rule for Thrombocytopenia General rule: Patients should be resident in Lombardy and registered by the National Health Service from more than 1 year at the date of hospital admission (or at start date of ambulatory event) and for an year following the discharge (or from the end date of ambulatory event). Discharge from hospital with 1stdiagnosis of Thrombocytopenia (T0= admission date) or ambulatory diagnosis. From the prescription repository all drugs prescribed two months before T0 should be extracted. The drugs found in the two months before T0 should not be in a time window of six months before the admission date to be classified as suspect. Diagnosis of Thrombocytopenia from hospital admission and/or ambulatory should not be in time window of 12 months before T0 (no chronic case of thrombocytopenia). The record of the hospital diagnosis and the suspected drugs will be presented to the GP. The year subsequent T0 should serve as a check for new thrombocytopenia diagnosis or for searching for new haematological diseases(such as leukaemia). This is a follow up phase.


FP7-287800 SALUS


In ICD9 CM Thrombocytopenia which could be of interest in this rule is identified as:

287.3 Primary thrombocytopenia

287.30 Primary thrombocytopenia, unspecified

287.31 Immune thrombocytopenic purpura

287.39 Other primary thrombocytopenia

287.4 Secondary thrombocytopenia

287.49 Other secondary thrombocytopenia

287.5 Thrombocytopenia, unspecified

(other Thrombocytopenia ICD9 codes are not considered since out of scope in ADE search).

In order to evaluate which of these diagnosis codes are significantly represented in the DWH it is suggested to perform some investigation and to consider the frequency distribution of Thrombocytopenia in ambulatory and hospitalization tables.




Field: DIAGNOSIS=_”Thrombocytopenia” ICD9CM (287.31, 287.49, 287.5)

Field: DATE_OF_EVENT.


Table: AMBULATORY

Field: DIAGNOSIS_CODE_ICD9CM =_”Thrombocytopenia” (287.31, 287.49, 287.5)

3. Field: START_DATE. For drugs list search:

Table: DRUGS.

Field: DELIVERY_DATE= DATE_OF_EVENT OR START_DATE– X weeks (X=0:8)

4. For drugs list exclusion:

Table: DRUGS.

Field: ATC_CODE or/and AIC_CODE of DRUGS found between (DATE_OF_EVENT OR

START_DATE) and (DATE_OF_EVENT OR T0-8 weeks)

Field: DELIVERY_DATE= DATE_OF_EVENT OR START_DATE – X weeks (X=8:48)

DRUG X DRUG Y

T0-12months


NO Thrombocytopenia DIAGNOSIS hospital/ambulatory T0-2 months

2

T0: Thrombocytopenia admission/ambulatory

diagnosis NO DRUG Y (AIC and ATC)

Figure 6: temporal diagram of LISPA rule concept #2

FP7-287800 SALUS


5. For diagnosis exclusion:

Table: HOSPITALIZATION/AMBULATORY.

Field: DIAGNOSIS_CODE_ICD9CM =_”Thrombocytopenia” (287.31, 287.49, 287.5)

found between (DATE_OF_EVENT OR START_DATE) and (DATE_OF_EVENT OR T0-

X weeks (X=0:52))

Rule for Upper gastrointestinal bleeding General rule: Patients should be resident in Lombardy and registered by the National Health Service from more than 1 year at the date of hospital admission (or at start date of ambulatory event) and for an year following the discharge (or from the end date of ambulatory event). Discharge from hospital or ambulatory event with a diagnosis of Upper gastrointestinal bleeding (ICD9 578.x) From the prescription repository all drugs prescribed two months before the hospital admission date should be extracted. The record of the hospital/ambulatory diagnosis and the suspected drugs will be presented to the GP. A further elaboration will be performed with the result of this rule: a comparison between the list of drugs inducing upper gastrointestinal bleeding (found in literature and reported in the following table 6) and the list of drugs - associated frequencies identified through this rule. ATC code, 3rd level

Pharmacological subgroup ATC Code, 4th level

Chemical subgroup ATC Code, 5th level

Drug name

M01A NSAIDs M01AH COXIBs M01AH01 Celecoxib

M01A NSAIDs M01AH COXIBs M01AH05 Etoricoxib

M01A NSAIDs M01AB Acetic acid derivatives and related substances

M01AB01 Indomethacin


M01AB05 Diclofenac


M01AB16 Aceclofenac


M01AB15 Ketorolac

M01A NSAIDs M01AE Propionic acid derivatives M01AE01 Ibuprofen

M01A NSAIDs M01AE Propionic acid derivatives M01AE02 Naproxen

M01A NSAIDs M01AE Propionic acid derivatives M01AE03 Ketoprofen

M01A NSAIDs M01AC Oxicams M01AC01 Piroxicam

M01A NSAIDs M01AC Oxicams M01AC02 Tenoxicam

M01A NSAIDs M01AC Oxicams M01AC05 Lornoxicam

M01A NSAIDs M01AC Oxicams M01AC06 Meloxicam

M01A NSAIDs M01AX NSAIDs, other M01AX17 Nimesulide

B01A Antithrombotic agents B01AA Vitamin K antagonists (VKA) B01AA03 Warfarin

B01A Antithrombotic agents B01AA Vitamin K antagonists (VKA) B01AA07 Acenocoumarol

B01A Antithrombotic agents B01AC Platelet aggregation inhibitors B01AC04 Clopidogrel

B01A Antithrombotic agents B01AC Platelet aggregation inhibitors B01AC06 Low-‐dose Acetylsalicylic acid (ASA)

C03D Potassium-‐sparing agents C03DA Aldosterone antagonists C03DA01 Spironolactone

H02A Corticosteroids for systemic use H02AB Glucocorticoids H02AB01 Betamethasone

H02A Corticosteroids for systemic use H02AB Glucocorticoids H02AB02 Dexamethason

FP7-287800 SALUS


e

H02A Corticosteroids for systemic use H02AB Glucocorticoids H02AB04 Methylprednisolone

H02A Corticosteroids for systemic use H02AB Glucocorticoids H02AB08 Triamcinolone

H02A Corticosteroids for systemic use H02AB Glucocorticoids H02AB09 Hydrocortisone

N06A Antidepressants N06AB Selective serotonin reuptake inhibitors (SSRIs)

N06AB03 Fluoxetine


N06AB04 Citalopram


N06AB05 Paroxetine


N06AB08 Fluvoxamine


N06AB10 Escitalopram


N06AB06 Sertraline

M05B Drugs affecting bone structure and mineralization

M05BA Bisphosphonates M05BA04 Alendronic acid


M05BA Bisphosphonates M05BA06 Ibandronic acid


M05BA Bisphosphonates M05BA07 Risedronic acid


M05BA Bisphosphonates M05BA08 Zoledronic acid

Table 6 – Drugs inducing upper gastrointestinal bleeding





Field: DIAGNOSIS=_”Upper gastrointestinal bleeding” ICD9CM (578.x)

Field: DATE_OF_EVENT.


Table: AMBULATORY

Field: DIAGNOSIS_CODE_ICD9CM =_”Upper gastrointestinal bleeding” ICD9CM (578.x)

Field: START_DATE.

3. For drugs list search:

DRUG XDRUG

DRUG YDRUG X

T0-2 months T0-6 months

1

T0: Upper gastrointestinal bleeding

admission/ambulatory diagnosis

Figure 7: temporal diagram of LISPA rule concept #2 (b)

FP7-287800 SALUS


Table: DRUGS.

Field: DELIVERY_DATE= DATE_OF_EVENT OR START_DATE– X months (X=0:2)

3.2.3 Rules based on presence of interaction between two or more drugs These rules are focused on recognizing possible interactions with the concomitant use of two, three or more drug. The possible interactions are known on the basis of previously reported ADEs or on the basis of literature case. In particular the reference literature for this rule is indicated in the table with the drugs list. Rules definition approach: Starting from a list of drugs related with one other drug. (based on existing ADEs reported in the past (with drugs interaction) and on clinical literature) it is possible to identify specific ADEs. Strengths:

• Literature support; • Well identified field.

Weaknesses:

• Limitation in new ADE type identification. Rule structure 1st step - Rules setup A list of all diagnosis which refers to two, three or more than three drugs that the patient took in the period before diagnosis. The obtained list is used for running the rule. Moreover this rules is based on literature, in particular we consider previous studies of ADEs caused by drugs interaction and we use the list of drugs and associated diagnosis to run the rule. Some of the obtained drugs associations is represented in the following table 7: Drug 1 Drug 2 Drug 3 Drug 4 ICD9 code Onset time

(follow-‐up period)

Ref.

2 DRUGS INTERACTIONS M04AA01 Allopurinol

C09AA02 Enalapril

995.2 Hypersensitivity to correct medicinal substance properly administered

< 90 days Pasina L, et al Pharmacoepidemiology and Drug Safety, 2013 C10AA01

Simvastatin C08CA01 Amlodipine

359.9 Myopathy 728.88 Rhabdomyolysis

< 90 days

C10AA05 Atorvastatin

C08DA01 Verapamil


< 90 days

C01BD01 Amiodarone

C10AA01 Simvastatin


< 90 days

C01BD01 Amiodarone

C07AB03 Atenolol

458.9 Hypotension, unspecified 427.5 Cardiac arrest

21-‐ 35 days

C01BD01 Amiodarone

R06AX13 Loratadine

427.1 Paroxysmal ventricular tachycardia

2 -‐ 7 days Antonelli D, et al. IsrMedAssoc J. 2005

B01AA03 Warfarin

J01MA02 Ciprofloxacin

578.9 Gastrointestinal bleeding

< 20 days Schelleman, H. et al.Clin. Pharmacol. Ther.

FP7-287800 SALUS


J01MA12 Levofloxacin

2008

B01AA03 Warfarin

J01EE01 Cotrimoxazole

578.9 Gastrointestinalbleeding

< 20 days

B01AA03 Warfarin

J02AC01 Fluconazole

578.9 Gastrointestinalbleeding

< 20 days

B01AA03 Warfarin

N06AB04 Citalopram, or N06AB05 Paroxetine , or N06AB03 Fluoxetine, or N06AX16 Venlafaxine

578.9Gastrointestinal bleeding

<119 days Schelleman H, et al PLoS One 2011

B01AA03 Warfarin

C10AB05 Fenofebrate, or C10AB04 Gemfibrozil

578.9 Gastrointestinal bleeding

< 120 days Schelleman, H. et al. Am J Med 2010

3 DRUGS INTERACTIONS C01AA04 Digitoxin

C10AA01 Simvastatin

C01BD01 Amiodarone

728.88 Rhabdomyolysis Not available

Nägele H, et al. Drug Metabol Drug Interact. 2007

J05AE08 Atazanavir

C10AA01 Simvastatin

C01BD01 Amiodarone

728.88 Rhabdomyolysis < 19 days Schmidt GA, et al. J Am Board Fam Med. 2007

4 DRUGS INTERACTIONS L04AD01 Cyclosporin

C10AA01 Simvastatin

C10AB04 Gemfibrozil

J02AC02 Itraconazole

728.88Rhabdomyolysis Not available

Maxa JL, et al Ann Pharmacother. 2002

C09AA05 Ramipril

C09CA03 Valsartan

C03DA01 Spironolactone

M01AE01 Ibuprofen, or M01AB05 Diclofenac, or M01AE09Flurbiprofen, or M01AE02 Naproxen, or M01AE03Ketoprofen

584.9Acute kidney injury (non-‐traumatic)

< 30 days Lapi F, et al. BMJ 2013

Table 7 – List of concomitant drugs

2nd step – Rules running

Search period: last 3 month

In SALUS-LISPA DWH, on the basis of the 1st step performed on existing dataset of ADEs reporting

and on reference literature, it is necessary to perform a data search based on the drugs interactions list.

FP7-287800 SALUS


• Search two, three or more than three drugs (AIC code or ATC code) AND related ICD9

• the ICD9 has to be in a date after the drug purchase and not greater than drug purchase (index

prescription) plus X weeks/months/years (it depends on the couple drugs-diagnoses) + DDD.

The different drugs in general are purchased in different dates. When coverage period of a

drug (defined as delivery date +DDDs)includes delivery date of another drug(s), overlap

period is defined. The overlap period ends when finishes the coverage period of one

concomitant drug. The interactions period Xi depends on drugs association.11

• in the six months before index prescription, there must be no prescriptions of the same

association



1. For drug search:

Table: DRUGS.

Field: ATC_CODE or/and AIC_CODE

Field: DELIVERY_DATE used for compared with the event encountered



Field: DIAGNOSIS_CODE X_ICD9CM (where X is from 1 to 6)

Field: DATE_OF_EVENT is greater or equal than DRUGS. DELIVERY_DATE and minor

than DRUGS.DELIVERY_DATE + X weeks/months/years (it depends on the drugs-

diagnoses). In the six months before index prescription there must be no purchase

concomitantly of those drugs, i.e. the patients must be newly treated.

The drug search is for a group of drugs (drug 1 AND drug 2 AND drug N).


Table: AMBULATORY

11 To simplify the approach we can consider X = max 12 weeks

DRUG X DRUG Y

T0-6 months

NO DRUG X (AIC and ATC) ANDNO DRUG X (AIC and

T0 + overlapping period

T0 + Xi months: Diagnosis ICD9CM

ambulatory/hospitalization NO DRUG Y (AIC and ATC)

AND DIAG AND


FP7-287800 SALUS



Field: START_DATE is greater or equal than DRUGS.DELIVERY_DATE and minor than

DRUGS.DELIVERY_DATE + X weeks/months/years (it depends on the drugs). In the six

months before index prescription there must be no purchase concomitantly of those drugs.

3.2.4 Rules based on request of specialistic examinations or diagnostic tests These rules are focused to identify possible ADEs using indirect indication like specific diagnostic test request, specific specialistic examinations as trigger of ADE. Rules definition approach: the rule is based on a list of diagnostic test request or specialistic examination request that could be considered as an indicator of ADE. Strengths:

• It is a new field, without scientific literature support;

• It explores new clinical patterns which could be linked to an ADE considering both clinical

and administrative data (diagnosis, drugs, exams request, specialistic visits request,…)

Weaknesses:

• limitation of the field;

• limitation in new ADE type identification.

Examples:

1. Psychiatric examination 2. Neurological examination 3. Gastroscopy 4. Cardiovascular examination and/or ECG

1. Rule description for psychiatric ADEs - Patients should be resident in Lombardy and registered by the National Health Service from more than 1 year at the start date of ambulatory event (examination or diagnostic test) and for an year following the end date of ambulatory event. - Requests for the first psychiatric examination (excluding ambulatory events planned or for screening) which could be associated to a psychiatric ADE. From the prescription repository all drugs prescribed two months before the start date of ambulatory event which are listed in the following table 8. The drugs found in the two months before the start date of ambulatory event should not be in a time window of 8 months before the start date to be classified as suspect (patient newly treated). The drugs Y (classified according to ATC code, 3rd level as N05 or N06), or a request for the first psychiatric examination, found in a time window of 8 months before the prescription date of drug X should be excluded (the patient should not have been treated with psychiatric drugs during the last 8 months and not be treated as a psychiatric patient before). A diagnosis with ICD9 code 290 or 319 (mental disorder) in the last 8 months should be excluded The record of the specialistic examination request, the suspected drugs will be presented to the GP.

Specialistic examination ATC Code, 5th level Drug Psychiatric examination J01GB06 Amikacin

Psychiatric examination J01GB03 Gentamycin

FP7-287800 SALUS


Psychiatric examination J01GB05 Neomycin

Psychiatric examination J01GA01 Streptomycin

Psychiatric examination J01GB01 Tobramycin

Psychiatric examination C02AB01 Alpha-methyldopa

Psychiatric examination N04AA02 Biperiden

Psychiatric examination D01BA01 Griseofulvin

Psychiatric examination A02BA01 Cimetidine

Psychiatric examination R06AE07 Cetirizine

Psychiatric examination P01BA01 Chloroquine

Psychiatric examination C07AA05 Propranolol

Psychiatric examination C01AA04 Digitoxin

Psychiatric examination H02AB06 Prednisolone

Psychiatric examination J05AB01 Acyclovir

Psychiatric examination N04BB01 Amantadine

Psychiatric examination J04AC01 Isoniazide

Psychiatric examination J04AB02 Rifampicin

Psychiatric examination J04AK02 Ethambutol

Psychiatric examination C03AA03 Hydrochlorothiazide

Psychiatric examination M01AB01 Indomethacin

Psychiatric examination N02AB03 Fentanyl

Psychiatric examination J01XA01 Vancomycin

Psychiatric examination J01DB01 Cefalexin

Psychiatric examination R03BB01 Ipratropium bromide

Psychiatric examination R03DA04 Theophylline

Psychiatric examination J01EE01 Trimethoprim-sulfamethoxazole

Psychiatric examination H03AA01 Levothyroxine


Psychiatric examination C02AC01 Clonidine

Psychiatric examination D01BA01 Griseofulvin





Psychiatric examination N07BB01 Disulfiram



Psychiatric examination R03DA04 Theophylline

Psychiatric examination R03CA02 Ephedrine

Psychiatric examination R03AC02 Salbutamol




Psychiatric examination R06AE07 Cetirizine




FP7-287800 SALUS


Psychiatric examination J05AB01 Acyclovir








Psychiatric examination R03CA02 Ephedrine













Table 8 – Association between a psychiatric ambulatory event and a list of drugs (not for psychiatric patients) and possible ADE



1. For specialistic examination request search:

Table: AMBULATORY

DRUG X DRUG X

T0-8 months

T0: Request for psychiatric examination

NOT

T0-2 months T0-6months


DRUG Y (ATC 2nd level: N05; N06) OR

Request for psychiatric examination OR


FP7-287800 SALUS


Field: AMB_SPECIALTY_DESC: Psychiatric

Field: AMB_ORDER_DESC: Psychiatric examination (“Prima visita psichiatrica”)

Field: AMB_TYPE: not Z (planned) or S (screening)

Field: START_DATE (T0)

Field: END_DATE.

2. For specialistic examination request or mental disorder diagnosis exclusion:

Table: AMBULATORY

Field: AMB__SPECIALTY_DESC: Psychiatric

Field: AMB_ORDER_DESC: Psychiatric examination found between

(DATE_OF_EVENT) and (DATE_OF_EVENT) OR T0-X weeks (X=0:56)

Field: DIAGNOSIS_CODE_ICD9CM: Mental disorder (ICD9 219.x;390.x) found

between (DATE_OF_EVENT) and (DATE_OF_EVENT) OR T0-X weeks (X=0:56)


Table: DRUGS.

Field: DELIVERY_DATE= START_DATE– X weeks (X=0:8)


Table: DRUGS.

Field: ATC_CODE or/and AIC_CODE of DRUGS found between (START_DATE)

and (START_DATE-8 weeks)

Field: DELIVERY_DATE= START_DATE – X weeks (X=8:48)

Field: ATC_CODE (N05, N06) or/and AIC_CODE of DRUGS found between

(START_DATE) and (START_DATE-8 weeks)


2. Rule description for neurological ADEs

- Patients should be resident in Lombardy and registered by the National Health Service from more than 1 year at the start date of ambulatory event (examination or diagnostic test) and for an year following the end date of ambulatory event.

- Request of a neurological examination (excluding ambulatory events planned or for screening) which could be associated to a neurological ADE.

From the prescription repository all drugs prescribed two months before the start date of ambulatory event which are listed in the following table 9. The drugs found in the two months before the start date of ambulatory event should not be in a time window of 6 months before the start date to be classified as suspect. The drugs Y (classified according to ATC code, 2nd level as N03), or a request for the first neurological examination, found in a time window of 8 months before the prescription date of drug X should be excluded. The record of the specialistic examination request, the suspected drugs will be presented to the GP.

FP7-287800 SALUS


Specialistic examination ATC Code, 5th level Drug

Neurological examination A10AC01 Insulin

Neurological examination C09AA02 Enalapril

Neurological examination R03AC03 Terbutaline

Neurological examination J01GB06 Amikacin

Neurological examination J01GB03 Gentamycin

Neurological examination J01GB05 Neomycin

Neurological examination J01GA01 Streptomycin

Neurological examination J01GB01 Tobramycin


Neurological examination J01GB06 Amikacin


Neurological examination J01GB05 Neomycin

Neurological examination J01GA01 Streptomycin

Neurological examination J01GB01 Tobramycin

Neurological examination C02AB01 Methyldopa

Neurological examination C01BD01 Amiodarone

Neurological examination C01BA03 Disopyramide

Neurological examination C01BA02 Procainamide

Neurological examination C01BD01 Amiodarone

Neurological examination N06AA09 Amitriptyline

Neurological examination N06AB03 Fluoxetine

Neurological examination N03AB02 Phenytoin

Neurological examination N03AG01 Sodium valproate

Neurological examination M04AC01 Colchicine

Neurological examination M04AC01 Colchicine

Neurological examination A02BA01 Cimetidine



Neurological examination P01BA01 Chloroquine



Neurological examination P01BA02 Hydroxychloroquine


Neurological examination L01BA01 Methotrexate

Neurological examination N05AN01 Lithium

Neurological examination N05AN01 Lithium

Neurological examination B01AA03 Warfarin

Neurological examination B01AC06 Low-dose Acetylsalicylic acid (ASA)

Neurological examination C07AB03 Atenolol

Neurological examination C07AA05 Propranolol

Neurological examination C07AA03 Pindolol




Neurological examination C07AB02 Metoprolol

FP7-287800 SALUS


Neurological examination J01CF04 Oxacillin

Neurological examination C01AA04 Digitoxin

Neurological examination C01AA04 Digitoxin

Neurological examination H02AB06 Prednisolone

Neurological examination J04AC01 Isoniazide

Neurological examination J04AC01 Isoniazide

Neurological examination N07BB01 Disulfiram

Neurological examination G03CA01 Ethinylestradiol

Neurological examination C03CC01 Ethacrynic acid

Neurological examination L04AD01 Cyclosporin

Neurological examination C10AB01 Clofibrate

Neurological examination C10AA01 Simvastatin

Neurological examination C03AA07 Cyclopenthiazide

Neurological examination C03AA03 Hydrochlorothiazide

Neurological examination M01AB01 Indomethacin

Neurological examination M01AB01 Indomethacin

Neurological examination N02AD01 Pentazocin

Neurological examination N02AC04 Destropropoxyphene

Neurological examination N02AD01 Pentazocin

Neurological examination N02BE01 Paracetamol

Neurological examination J01XD01 Metronidazole

Neurological examination J01XE01 Nitrofurantoin

Neurological examination J01XA01 Vancomycin

Neurological examination J01XA01 Vancomycin

Neurological examination M01AX02 Niflumic acid

Neurological examination R03BA05 Fluticasone

Neurological examination R03DA04 Theophylline

Neurological examination R03DA05 Aminophylline

Neurological examination R03DA04 Theophylline

Neurological examination J01MA02 Ciprofloxacin

Neurological examination C08CA05 Nifedipine

Neurological examination J01EC01 Sulfamethoxazole

Neurological examination C01DA02 Glyceryl trinitrate

Neurological examination B03BB01 Folic acid

Table 9 - Association between a neurological ambulatory event and a list of drugs (not for neurological patients) and possible ADE


FP7-287800 SALUS



1. For specialistic examination request search:

Table: AMBULATORY

Field: AMB_SPECIALTY_ID: 32

(Field: AMB_SPECIALTY_DESC: Neurology)

Field: AMB_ORDER_CODE: 8913, 89131, 8914, 89141, 89142, 89143, 89144,

89145, 89151, 89152, 89153, 89154, 89155, 89156, 89157, 89158, 89159, 8917,

89181, 89182, 89191, 89192, 93086, 93087, 93088, 94012, 94021, 94022, 94081,

94082, 94084, 99299

Field: AMB_ORDER_DESC: Neurological examination


Field: START_DATE

Field: END_DATE.

2. For specialistic examination request exclusion:

Table: AMBULATORY


Field: AMB_SPECIALTY_DESC: Neurology


89145, 89151, 89152, 89153, 89154, 89155, 89156, 89157, 89158, 89159, 8917,

89181, 89182, 89191, 89192, 93086, 93087, 93088, 94012, 94021, 94022, 94081,

94082, 94084, 99299

Field: AMB_ORDER_DESC Neurological examination found between

(DATE_OF_EVENT) and (DATE_OF_EVENT) OR T0-X weeks (X=0:56)

DRUG X DRUG X

T0-8 months

T0: Request for neurological examination

NOT



DRUG Y (ATC 2nd level: N03) OR

Request for neurological examination

Figure 10: temporal diagram of LISPA rule concept #4 (b)

FP7-287800 SALUS



Table: DRUGS.



Table: DRUGS.




Field: ATC_CODE (N03) or/and AIC_CODE of DRUGS found between



3. Rule description for gastrointestinal ADEs


- Request of a gastroscopy (excluding ambulatory events planned or for screening) which could be associated to a gastrointestinal ADE.

From the prescription repository all drugs prescribed two months before the start date of ambulatory event which are listed in the following table 10. The drugs found in the two months before the start date of ambulatory event should not be in a time window of 6 months before the start date to be classified as suspect. The drugs Y (classified according to ATC code, 3rd level as A02A; A02B), or a request for a gastroscopy found in a time window of 8 months before the prescription date of drug X should be excluded. The record of the diagnostic test request, the suspected drugs will be presented to the GP.

Diagnostic test ATC Code, 5th level Drug Gastroscopy M01AH01 Celecoxib

Gastroscopy M01AH04 Parecoxib

Gastroscopy M01AH05 Etoricoxib

Gastroscopy M01AB01 Indomethacin

Gastroscopy M01AB05 Diclofenac

Gastroscopy M01AB16 Aceclofenac Gastroscopy M01AB15 Ketorolac Gastroscopy M01AE01 Ibuprofen

Gastroscopy M01AE02 Naproxen

Gastroscopy M01AE09 Flurbiprofen

Gastroscopy M01AE03 Ketoprofen

Gastroscopy M01AC01 Piroxicam

Gastroscopy M01AC02 Tenoxicam

Gastroscopy M01AC05 Lornoxicam

Gastroscopy M01AC06 Meloxicam

Gastroscopy M01AX01 Nabumetone

Gastroscopy M01AX17 Nimesulide

FP7-287800 SALUS


Gastroscopy B01AA03 Warfarin

Gastroscopy B01AA07 Acenocoumarol

Gastroscopy B01AC04 Clopidogrel

Gastroscopy B01AC06 Low-‐dose Acetylsalicylic acid (ASA)

Gastroscopy B01AC07 Dipyridamole

Gastroscopy C03DA01 Spironolactone

Gastroscopy H02AB01 Betamethasone

Gastroscopy H02AB02 Dexamethasone

Gastroscopy H02AB04 Methylprednisolone

Gastroscopy H02AB06 Prednisolone

Gastroscopy H02AB08 Triamcinolone

Gastroscopy H02AB09 Hydrocortisone

Gastroscopy H02AB10 Cortisone

Gastroscopy H02AB13 Deflazacort

Gastroscopy N06AB03 Fluoxetine

Gastroscopy N06AB04 Citalopram

Gastroscopy N06AB05 Paroxetine

Gastroscopy N06AB08 Fluvoxamine

Gastroscopy N06AB10 Escitalopram

Gastroscopy N06AB06 Sertraline

Gastroscopy M05BA01 Etidronic acid

Gastroscopy M05BA02 Clodronic acid

Gastroscopy M05BA03 Pamidronic acid

Gastroscopy M05BA04 Alendronic acid

Gastroscopy M05BA06 Ibandronic acid

Gastroscopy M05BA07 Risedronic acid

Gastroscopy M05BA08 Zoledronic acid

Table 10 - Association between a gastroscopy ambulatory event and a list of drugs and possible ADE


DRUG X DRUG X

T0-8 months

T0: Request for a gastroscopy

NOT



DRUG Y (ATC 3rd level: A02A; A02B) OR

Request for a gastroscopy

Figure 11: temporal diagram of LISPA rule concept #4 (c)

FP7-287800 SALUS



1. For diagnostic test request search:

Table: AMBULATORY

Field: AMB_ORDER_DESC: Gastroscopy


Field: START_DATE

Field: END_DATE.

2. For diagnostic test request exclusion:

Table: AMBULATORY

Field: AMB_ORDER_DESC: Gastroscopy found between (DATE_OF_EVENT) and

(DATE_OF_EVENT) OR T0-X weeks (X=0:56)


Table: DRUGS.



Table: DRUGS.




Field: ATC_CODE (A02A; A02B) or/and AIC_CODE of DRUGS found between



4. Rule description for cardiovascular ADEs


- Request of a cardiovascular examination or electrocardiogram (ECG) (excluding ambulatory events planned or for screening) which could be associated to a cardiovascular ADE.

From the prescription repository all drugs prescribed two months before the start date of ambulatory event which are listed in the following table 11. The drugs found in the two months before the start date of ambulatory event should not be in a time window of 6 months before the start date to be classified as suspect. The drugs Y (classified according to ATC code, 1st level as C), or a request for the first cardiovascular examination or ECG, found in a time window of 8 months before the prescription date of drug X should be excluded. The record of the specialistic examination request or diagnostic test request, the suspected drugs will be presented to the GP.

Specialistic examination Diagnostic test ATC Code, 5th level Drug

Cardiological examination ECG R03AC03 Terbutaline

FP7-287800 SALUS


Cardiological examination ECG L01AA01 Cyclophosphamide

Cardiological examination ECG G03BA03 Testosterone

Cardiological examination ECG G03BB02 Androstanolone

Cardiological examination ECG N06AB03 Fluoxetine

Cardiological examination ECG N06AB04 Citalopram

Cardiological examination ECG N06AB10 Escitalopram

Cardiological examination ECG R06AE07 Cetirizine

Cardiological examination ECG N05AA01 Chlorpromazine

Cardiological examination ECG C07AA05 Propranolol

Cardiological examination ECG C07AA05 Propranolol

Cardiological examination ECG C07AA07 Sotalol

Cardiological examination ECG H02AB04 Methylprednisolone

Cardiological examination ECG H02AB06 Prednisolone

Cardiological examination ECG H02AB07 Prednisone

Cardiological examination ECG H02AB02 Dexamethasone

Cardiological examination ECG H02AB01 Betamethasone

Cardiological examination ECG N04BC01 Bromocriptine

Cardiological examination ECG L03AB01 Interferon

Cardiological examination ECG G03FA02 Hydroxyprogesterone and estrogen

Cardiological examination ECG G03FA02 Hydroxyprogesterone and estrogen

Cardiological examination ECG N05AX08 Risperidone

Cardiological examination ECG N05AX13 Paliperidone

Cardiological examination ECG N05AD01 Haloperidol

Cardiological examination ECG N05AH03 Olanzapine

Cardiological examination ECG N05AH04 Quetiapine

Cardiological examination ECG N05AE04 Ziprasidone

Cardiological examination ECG J01FA10 Azithromycin

Cardiological examination ECG J01FA09 Clarithromycin

Cardiological examination ECG J01FA01 Erythromycin

Cardiological examination ECG J01FA15 Telithromycin

Table 11 - Association between a cardiological ambulatory event and a list of drugs (not for cardiac patients) and possible ADE


FP7-287800 SALUS


1. For specialist examination or diagnostic test request search:

Table: AMBULATORY


(Field: AMB_SPECIALTY_DESC: Cardiology)


88723, 88724, 88725, 88735, 88771, 88772, 88773, 89013, 8941, 8942, 8943, 8944,

89481, 89482, 8950, 8952, 8954, 89581, 89582, 89583, 89584, 89585, 89586, 89587,

89588, 8959A, 89591, 89611, 8962, 897A3, 9336, 99292

Field: AMB_ORDER_DESC: ECG and/or other Cardiological examination


Field: START_DATE

Field: END_DATE.

2. For specialistic examination or diagnostic test request exclusion:

Table: AMBULATORY


(Field: AMB_SPECIALTY_DESC: Cardiology)


88723, 88724, 88725, 88735, 88771, 88772, 88773, 89013, 8941, 8942, 8943, 8944,

89481, 89482, 8950, 8952, 8954, 89581, 89582, 89583, 89584, 89585, 89586, 89587,

89588, 8959A, 89591, 89611, 8962, 897A3, 9336, 99292

Field: AMB_ORDER_DESC: ECG and/or other Cardiological examination found

between (DATE_OF_EVENT) and (DATE_OF_EVENT) OR T0-X weeks (X=0:56)


DRUG X DRUG X

T0-8 months

T0: Request for cardiovascular

examination or ECG

NOT



DRUG Y (ATC 1st level: C) OR

Request for a cardiovascular examination OR

Figure 12: temporal diagram of LISPA rule concept #4 (d)

FP7-287800 SALUS


Table: DRUGS.



Table: DRUGS.




Field: ATC_CODE (C) or/and AIC_CODE of DRUGS found between



3.3 Rules based on the prescription of specific antidotes An antidote is a substance that can counteract the effects of a poison. The prescription of an antidote can indicate the presence of a preceding adverse drug event. In table 12 frequently used antidotes are listed and explained.

Antidote ATC-Code description12

acetylcysteine - V03AB23 Acetylcysteine is a derivative of cysteine used as a mucolytic in various bronchopulmonary disorders and as an antidote to acetaminophen poisoning.

activated charcoal - A07BA01 Activated charcoal is a fine black odorless and tasteless powder made from wood or other materials that have been exposed to very high temperatures in an airless environment. It is then treated, or activated, to increase its ability to adsorb various substances by reheating with oxidizing gas or other chemicals to break it into a very fine powder. Activated charcoal is pure carbon specially processed to make it highly adsorbent of particles and gases in the body's digestive system. Activated charcoal has often been used since ancient times to cure a variety of ailments including poisoning. Its healing effects have been well documented since as early as 1550 B.C. by the Egyptians. However, charcoal was almost forgotten until 15 years ago when it was rediscovered as a wonderful oral agent to treat most overdoses and toxins. Activated charcoal's most important use is for treatment of poisoning. It helps prevent the absorption of most poisons or drugs by the stomach and intestines. In addition to being used for most swallowed poisons in humans, charcoal has been effectively used in dogs, rabbits, rats, and other animals, as well. It can also adsorb gas in the bowels

12 The descriptions are based on the http://medical-dictionary.thefreedictionary.com

FP7-287800 SALUS


and has been used for the treatment of gas or diarrhoea. Charcoal's other uses such as treatment of viruses, bacteria, bacterial toxic byproducts, snake venoms and other substances by adsorption have not been supported by clinical studies. By adding water to the powder to make a paste, activated charcoal can be used as an external application to alleviate pain and itching from bites and stings.

ascorbic acid - A11GA01 Asorbic acid (vitamin C), a water-soluble vitamin found in many vegetables and fruits, and an essential element in the diet of humans and many other animals; deficiency produces scurvy and poor wound repair. It is used as an antiscorbutic and nutritional supplement, in the treatment of iron-deficiency anemia and chronic iron toxicity, and in the labeling of red blood cells with sodium chromate Cr 51.

atropine - V03AB44 Atropine is an anticholinergic and antispasmodic alkaloid used as the sulfate salt to relax smooth muscles and increase and regulate the heart rate by blocking the vagus nerve, and to act as a preanesthetic antisialagogue, an antidote for various toxic and anticholinesterase agents and as an antisecretory, mydriatic, and cycloplegic.

beclomethasone - A07EA07 - D07AC15 - R01AD01 - R03BA01

Beclomethasone is a glucocorticoid used in the dipropionate form in the treatment of bronchial asthma, seasonal and nonseasonal allergic rhinitis or other allergic or inflammatory nasal conditions, and some dermatoses, and to prevent recurrence of nasal polyps.

biperidin - N04AA02 Biperidin is an antidyskinetic used as the hydrochloride and lactate salts in the treatment of parkinsonism and drug-induced extrapyramidal reactions.

botulinum antitoxin - J06AA04 Botulism is caused by botulinum toxin, a natural poison produced by certain bacteria in the Clostridium genus. Exposure to the botulinum toxin occurs mostly from eating contaminated food, or in infants, from certain clostridia growing in the intestine. Botulinum toxin blocks motor nerves' ability to release acetylcho-line, the neurotransmitter that relays nerve signals to muscles, and flaccid paralysis occurs. As botulism progresses, the muscles that control the airway and breathing fail. Botulism occurs rarely, but it causes concern because of its high fatality rate. Clinical descriptions of botulism possibly reach as far back in history as ancient Rome and Greece. However, the relationship between contaminated food and botulism wasn't defined until the late 1700s. In 1793 the German physician, Justinius Kerner, deduced that a substance in spoiled sausages, which he called wurstgift (German for sausage poison), caused botulism. The toxin's origin and identity remained elusive until Emile von Ermengem, a Belgian professor, isolated

FP7-287800 SALUS


Clostridium botulinum in 1895 and identified it as the poison source. Three types of botulism have been identified: foodborne, wound, and infant botulism. The main difference between types hinges on the route of exposure to the toxin. In the United States, there are approximately 110 cases of botulism reported annually. Food-borne botulism accounts for 25% of all botulism cases and usually can be traced to eating contaminated home-preserved food. Infant botulism accounts for 72% of all cases, but the recovery rate is good (about 98%) with proper treatment. From 1990 to 2000, 263 cases of food-borne cases were reported in the United States, most of them in Alaska. Though most were related to home canning, two restaurant-associated outbreaks affected 25 people.

calcium gluconate - A12AA03 Calcium gluconate is a calcium salt used to treat or prevent hypercalcemia, nutritional deficiency, and hyperkalemia; also used as a treatment adjunct in cardiac arrest.

calcium trisodium pentetate - V03AB47 Calcium trisodium pentetate can be used to decontaminate humans who have been poisoned with plutonium, americium and other actinides. Upon formation of chelate complexes, these heavy metal ions are less readily unabsorbed and are more readily eliminated in urine.

dantrolene - MA03C01 Dantrolene is a skeletal muscle relaxant, used as the sodium salt in the treatment of chronic spasticity and the treatment and prophylaxis of malignant hyperthermia.

deferoxamine - V03AC01 Deferoxamine is an iron-chelating agent isolated from Streptomyces pilosus; used as the mesylate salt as an antidote in iron poisoning.

diazepam - N05BA01 - N05BA17

Diazepam is a benzodiazepine used as an antianxiety agent, sedative, antipanic agent, antitremor agent, skeletal muscle relaxant, anticonvulsant, and in the management of alcohol withdrawal symptoms.

digitalis-antitoxin - V03AB24 Digoxin Immune Fab (Ovine) (or digitalis-antitoxin) is the generic name for an antidote for overdose of digitalis. It is made from immunoglobulin fragments from sheep who have already been immunized with a digoxin derivative, digoxindicarboxymethoxylamine (DDMA). Its brand names include Digibind and DigiFab, with the former manufactured by GlaxoSmithKline and the latter manufactured by BTG plc.

Dimercaptopropanesulfonic acid

- V03AB43 2,3-Dimercapto-1-propanesulfonic acid (abbreviated DMPS) and its sodium salt (known as Unithiol) are chelating agents that form complexes with various heavy metals. They are related to dimercaprol, which is another chelating agent. The synthesis of DMPS was first reported in 1956 by Petrunkin from Kiev, Soviet Union. The effects of DMPS on heavy metal poisoning, including with polonium-210, were

FP7-287800 SALUS


investigated in the following years. DMPS was found to have some protective effect, prolonging the survival time.

dimethylaminophenol - V03AB27 4-Dimethylaminophenol (abbreviated in medical practice as DMAP) is an aromatic compound containing both phenol and amine functional groups. It has the molecular formula C8H11NO. 4-Dimethylaminophenol has been used as an antidote for cyanide poisoning. It works by generating methemoglobin. This is suitable as an emergency treatment only; treatment must be followed up with sodium thiosulfate or cobalamin.

ethanol - V03AB16 Ethanol is a colorless volatile flammable liquid, C2H5OH, synthesized or obtained by fermentation of sugars and starches and widely used, either pure or denatured, as a solvent and in drugs, cleaning solutions, explosives, and intoxicating beverages. Ethanol can be used as an antidote for methanole.

flumazenil - V03AB25 Flumazenil (also known as flumazepil, code name Ro 15-1788, trade names Anexate, Lanexat, Mazicon, Romazicon) is a benzodiazepine antagonist. Flumazenil is of benefit in patients who become excessively drowsy after benzodiazepines are used for either diagnostic or therapeutic procedures. It has been used as an antidote in the treatment of benzodiazepine overdoses. It reverses the effects of benzodiazepines by competitive inhibition at the benzodiazepine binding site on the GABAA receptor. There are many complications that must be taken into consideration when used in the acute care setting. It has been found to be effective in overdoses of non-benzodiazepine sleep enhancers, namely zolpidem and zaleplon. It has also been used in hepatic encephalopathy, though results have been mixed.

folic acid - B03BB01 A yellowish-orange compound, C19H19N7O6, of the vitamin B complex group, occurring in green plants, fresh fruit, liver, and yeast. Also called folacin, folate, vitamin Bc. Folic asid is used as an antidote for overdose of methotrexate.

folinic acid - V03AF03 Folinic acid (INN) or leucovorin (USAN), generally administered as calcium or sodium folinate (or leucovorin calcium/sodium), is an adjuvant used in cancer chemotherapy involving the drug methotrexate. It is also used in synergistic combination with the chemotherapy agent 5-fluorouracil. Levofolinic acid and its salts are the enantiopure drugs. They are configurated at the remaining asymmetric carbon atom. Folinic acid is administered at the appropriate time following methotrexate as part of a total chemotherapeutic plan, where it may "rescue" bone marrow and gastrointestinal mucosa cells from methotrexate. There is no apparent effect on preexisting methotrexate-induced nephrotoxicity.While not

FP7-287800 SALUS


specifically an antidote for methotrexate, folinic acid may also be useful in the treatment of acute methotrexate overdose. Different dosing protocols are used, but folinic acid should be re-dosed until the methotrexate level is less than 5 x 10−8 M. Folinic acid should be distinguished from folic acid.

fomepizole - V03AB34 Fomepizole or 4-methylpyrazole is indicated for use as an antidote in confirmed or suspected methanol or ethylene glycol poisoning. It may be used alone or in combination with haemodialysis. Apart from medical uses, the role of 4-methylpyrazole in coordination chemistry has been studied.

glucagon H04AA01 Glucagon is a hormone, secreted by the pancreas, that raises blood glucose levels. Its effect is opposite that of insulin, which lowers blood glucose levels. The pancreas releases glucagon when blood glucose levels fall too low. Glucagon causes the liver to convert stored glycogen into glucose, which is released into the bloodstream. Glucagon also stimulates the release of insulin, so that glucose can be taken up and used by insulin-dependent tissues. Thus, glucagon and insulin are part of a feedback system that keeps blood glucose levels at the right level. Glucagon belongs to a family of several other related hormones. It can be used as an antidote for beta-blocker overdoses.

hydroxocobalamin - V03AB33 Hydroxocobalamin is a natural form or vitamer of vitamin B12, a basic member of the cobalamin family of compounds. Hydroxocobalamin is the form of vitamin B12 produced by many bacteria which are used to produce the vitamin commercially. Like other forms of vitamin B12, hydroxocobalamin has an intense red color. It is not a form normally found in the human body, but is easily converted in the body to usable coenzyme forms of vitamin B12. Pharmaceutically, hydroxycobalamin is usually produced as a sterile injectable solution, and is used for treatment of the vitamin deficiency, and also (because of its afinity for cyanide ion) as a treatment for cyanide poisoning. Experimentally, it has been tested as a scavenger of nitric oxide. Vitamin B12 is a term that refers to a group of compounds called cobalamins that are available in the human body in a variety of mostly interconvertible forms. Together with folic acid, cobalamins are essential cofactors required for DNA synthesis in cells where chromosomal replication and division are occurring—most notably the bone marrow and myeloid cells. As a cofactor, cobalamins are essential for two cellular reactions: (1) the mitochondrial methylmalonylcoenzyme A mutase conversion of methylmalonic acid (MMA) to succinate, which links lipid and carbohydrate metabolism, and (2) activation of methionine synthase, which is the rate limiting

FP7-287800 SALUS


step in the synthesis of methionine from homocysteine and 5-methyltetrahydrofolate.

ipecacuanha - V03AB01 Ipecacuanha (Psychotria ipecacuanha) of family Rubiaceae is a flowering plant, the root of which is most commonly used to make syrup of ipecac, a powerful emetic. Its name comes from the Tupi i-pe-kaa-guéne, translated as 'road-side sick-making plant'. It is native to Brazil. The plant had been assigned different names by various botanists. Ipecacuanha has a long history of use as an emetic, for emptying the stomach in cases of poisoning. It has also been used as a nauseant, expectorant, and diaphoretic, and was prescribed for conditions such as bronchitis. The most common and familiar preparation is syrup of ipecac, which was commonly recommended as an emergency treatment for accidental poisoning until the final years of the 20th century. Ipecacuanha was also traditionally used to induce sweating.

iron hexacyanoferrate - V03AB31 Iron hexycyanoferrate is an insoluble dark blue pigment and dye, ferric ferrocyanide or one of its modifications and can be used as an antidote for certain kinds of heavy metal poisoning (cesium and thallium), particularly to block internal exposure to radioactive materials.

levocarnitine - A16AA01 Carnitine (or Levocarnitine) is a quaternary ammonium compound biosynthesized from the amino acids lysine and methionine. In living cells, it is required for the transport of fatty acids from the cytosol into the mitochondria during the breakdown of lipids (fats) for the generation of metabolic energy. It is often sold as a nutritional supplement. Carnitine was originally found as a growth factor for mealworms and labelled vitamin BT. Carnitine is used as an antidote in valproic acid poisoning.

methylthioninium chloride - V03AB17 Methylthioninium chloride (or methylene blue) is a basic aniline dye, C16H18N3SCl · 3H2O, that forms a deep blue solution when dissolved in water. It is used as an antidote for cyanide poisoning and a bacteriological stain.

naloxone - V03AB15 Naloxone is a drug used to counter the effects of opiate overdose, for example heroin or morphine overdose. Naloxone is specifically used to counteract life-threatening depression of the central nervous system and respiratory system. Naloxone is also experimentally used in the treatment for congenital insensitivity to pain with anhidrosis (CIPA), an extremely rare disorder (1 in 125 million) that renders one unable to feel pain. It is marketed under various trademarks including Narcan, Nalone, and Narcanti, and has sometimes been mistakenly called "naltrexate." It is not to be confused with naltrexone, an opioid receptor antagonist with qualitatively different effects, used for dependence treatment

FP7-287800 SALUS


rather than emergency overdose treatment. neostigmine - N07AA01

- S01EB06 Either of two related white crystalline compounds, C12H19BrN2O2 or C13H22N2O6S, that opposes the action of acetylcholinesterase and is used in the treatment of glaucoma, myasthenia gravis, and various postoperative conditions.

obidoxime - V03AB13 Obidoxime is a member of the oxime family used to treat nerve gas poisoning. Oximes are drugs known for their ability to reverse the binding of organophosphorus compounds to the enzyme acetylcholinesterase (AChE). AChE is an enzyme that removes acetylcholine from the synapse after it creates the required stimulation on the next nerve cell. If it gets inhibited, acetylcholine is not removed after the stimulation and multiple stimulations are made, resulting in muscle contractions and paralysis. Organophosphates (such as nerve gases) are well known inhibitors of AChE. They bind to a specific place on the enzyme and prevent it from functioning normally by changing the OH group on the serine residue and by protonating (quaternary nitrogen, R4N+) the nearby nitrogen atom located in the histidine residue. Oximes like these do have side effects and they include liver damage, kidney damage, nausea, vomiting, but they are very efficient antidotes to nerve gas poisoning. Usually treatment of poisoning includes the use of atropine, which can slow down the action of the poison, giving more time to apply the oxime.

physostigmin - V03AB19 Physostigmin is a crystalline alkaloid, C15H21N3O2, extracted from the Calabar bean, used in medicine as a miotic and cholinergic agent and to enhance memory in patients with Alzheimer's disease. Also called eserine. Physostigmin can be used as an antidote for atropine overdoses.

phytomenadione - B02BA01 Phytomenadione is a viscous fat-soluble liquid occurring in plants: essential for the production of prothrombin, required in blood clotting. Formula: C31H46O2 Also called vitamin K1. Phytomenadione. Phytomenadione is antidote for coumarin anticoagulants in hypoprothrombinaemia.

polyethylene glycol - A06AD15 Polyethylene glycol (PEG) is a polyether compound with many applications from industrial manufacturing to medicine. It has also been known as polyethylene oxide (PEO) or polyoxyethylene (POE), depending on its molecular weight, and under the tradename Carbowax. When attached to various protein medications, polyethylene glycol allows a slowed clearance of the carried protein from the blood. This makes for a longer-acting medicinal effect and reduces toxicity, and it allows longer dosing intervals. Examples include PEG-interferon alpha, which is used to treat hepatitis C, and PEG-filgrastim (Neulasta), which is used to treat

FP7-287800 SALUS


neutropenia. prednisolone and promethazine

V03AB05 Prednisolone is the active metabolite of prednisone. Prednisolone is a corticosteroid drug with predominantly glucocorticoid and low mineralocorticoid activity, making it useful for the treatment of a wide range of inflammatory and auto-immune conditions such as asthma. uveitis, pyoderma gangrenosum, rheumatoid arthritis, ulcerative colitis, temporal arteritis and Crohn's disease, Bell's palsy, multiple sclerosis, cluster headaches, vasculitis, acute lymphoblastic leukaemia and autoimmune hepatitis, systemic lupus erythematosus and dermatomyositis.

Promethazine is a first-generation H1 receptor antagonist of the phenothiazine chemical class used medically as an antihistamine antiemetic. It has a strong sedative effect and in some countries is prescribed for insomnia when benzodiazepines are contraindicated. The drug combination prednisolone/promethazine is used as an antidote for snake bites.

protamine V03AB14 Protamines are small, arginine-rich, nuclear proteins that replace histones late in the haploid phase of spermatogenesis and are believed essential for sperm head condensation and DNA stabilization. They may allow for denser packaging of DNA in spermatozoon than histones, but they must be decompressed before the genetic data can be used for protein synthesis. Genes that are essential for early embryonic development are still bound to ordinary histones, comprising about 4 percent of the DNA in spermatozoons. Protamine sulphate is an antidote for heparin. A chain shortened version of protamine also acts as a potent heparin antagonist, but with markedly reduced antigenicity.

pyridoxine - A11HA02 Pyridoxine is one of the compounds that can be called vitamin B6, along with pyridoxal and pyridoxamine. It differs from pyridoxamine by the substituent at the '4' position. It is often used as 'pyridoxine hydrochloride'. Pyridoxine is given to patients taking Isoniazid (INH) to combat the toxic side effects of the drug.

silibinin - A05BA03 Silibinin (INN), also known as silybin, is the major active constituent of silymarin, the mixture of flavonolignans extracted from blessed milk thistle (Silybum marianum) consisting of silibinin A and B, isosibilinin A and B, silicristin, silidianin. Both in vitro and animal research suggest that silibinin has hepatoprotective (antihepatotoxic) properties that protect liver cells against toxins. Silibinin has also demonstrated anti-cancer effects against human prostate adenocarcinoma cells, estrogen-dependent and -independent human breast carcinoma cells,

FP7-287800 SALUS


human ectocervical carcinoma cells, human colon cancer cells, and both small and nonsmall human lung carcinoma cells.Chemically modified silibinin, silibinin dihydrogen disuccinate disodium (trade name Legalon SIL) a solution for injection, is used in treatment of severe intoxications with hepatotoxic substances, such as death cap (Amanita phalloides) poisoning.

sodium thiosulfate - V03AB06 Thiosulfate (Na2S2O3), also spelled sodium thiosulphate, is a colorless crystalline compound that is more familiar as the pentahydrate, Na2S2O3·5H2O, an efflorescent, monoclinic crystalline substance also called sodium hyposulfite or “hypo.” The thiosulfate anion is tetrahedral in shape and is notionally derived by replacing one of the oxygen atoms by a sulfur atom in a sulfate anion. The S-S distance indicates a single bond, implying that the sulfur bears significant negative charge and the S-O interactions have more double bond character. The first protonation of thiosulfate occurs at sulfur. Sodium thiosulfate is used as an antidote to cyanide poisoning. Thiosulfate acts as a sulfur donor for the conversion for cyanide to thiocyanate (which can then be safely excreted in the urine), catalyzed by the enzyme rhodanase.

viper venom antitoxin (antivenin)

- J06AA03 Antivenin is an antitoxin active against the venom of a snake, spider, or other venomous animal or insect.

Table 12: Specific antidotes and their ATC-Codes

The antidotes listed in this table are typical emergency drugs. Additional antidotes that are less often used in practice are classified under the ATC Code “V03AB (Antidote)”:

- V03AB02: nalorphine

- V03AB03: edentates

- V03AB04: pralidoxime

- V03AB08: sodium nitrite

- V03AB09: dimercaprol

- V03AB18: potassium permanganate

- V03AB20: copper sulfate

- V03AB21: potassium iodide

- V03AB22: amyl nitrite

- V03AB26: methionine

- V03AB29: cholinesterase

- V03AB32: glutathione

FP7-287800 SALUS


- V03AB35: sugammadex

- V03AB36: phentolamine

- V03AB45: apomorphine

- V03AB46: toluidine chloride

- V03AB48: silymarin

3.4 Changes in drug therapy When the administration of a drug that normally has to be taken over a specific time period is cancelled in-between, this for example may lead to a higher risk of relapse. Events arising after drug discontinuations have to be checked for ADEs as well as events that occur after the dosage of a drug is changed.

3.5 Rules based on postmarketing information Some of the SPC also contain information about postmarketing experiences of a drug. SIDER (see section 2.1.2) for example makes this information available in a structured format using MedDRA codes. For the use of this concept in ANT, we created a new database comprising this postmarketing experience in order to detect and notify the physician of such unverified cases of suspicious ADEs. Figure 4 shows a small sample from the postmarketing database (see Figure 13).

Figure 13: Sample from the postmarketing ADE Detection database

FP7-287800 SALUS


The database contains in total more than 1.600 data entries on postmarketing ADE information. However, if new information on postmarketing ADE information arrives, it can be easily updated and extended.

3.6 Rules based on ADE related diagnoses In the ICD code system, there are a lot of codes that directly or indirectly refer to the possible presence of an ADE. According to the research results from Stausberg and Hasford13, more than 500 ICD-10GM diagnosis codes are directly correlated with cases of ADEs. This computer-based ADE detection approach benefits from these research results: All published codes were translated, consolidated and prepared as specific ADE detection rules. Figure 14 shows a small sample of the detection rule repository regarding this ADE detection concept.14

Figure 14: Sample from the ADE related diagnoses database

Thus, the ANT is able to screen the EHR data for suspicious diagnoses indicating present ADEs.

3.7 Drug interactions The majority of patients are treated with multiple drugs simultaneously. Whenever a patient takes two or more drugs, drug interactions can occur and not all interactions are both intended and desired. Several of the ADEs related to drug interactions are well known, and available in a semi-structured format, for example in the dataset of DrugBank. To detect these ADEs within ANT, the drug interaction data from DrugBank was extracted and integrated in ANT as ADE detection rules. In addition to the possibility to warn the physician of possible known drug interactions, this detection approach enables the ANT to improve the detection of unknown ADEs by comparing the knowledge from this detection method with the drugs involved in notifications regarding unknown ADEs.

3.8 Published Rules

3.8.1 PSIP Project The ADE Detection Rules listed in table 13 are detected and validated during the PSIP Project15. Condition Outcome NO cancer&NSAI Anaemia (Hb<10g/dL) proton pump inhibitor Anaemia (Hb<10g/dL) NO every traumatism&high weight heparin Hepatic cholestasis (alkalin phosphatase>240

UI/L or bilirubin>22 µmol/L) NO every traumatism&low weight heparin Hepatic cholestasis (alkaline phosphatase>240 13 J. Stausberg and J. Hasford, “Identification of Adverse Drug Events – The Use of ICD-10 Coded Diagnoses in Routine Hospital Data,“ Dtsch Arztebl Int., vol. 107, no. 3, pp. 23-29, Jan. 2010. 14 The complete list of ADE related ICD10GM diagnoses codes can be found here: http://www.ekmed.de/routinedaten/main4.php. 15 Emmanuel Chazard (2011): Automated Detection of Adverse Drug Events by Data Mining of Electronic Health Records, see http://www.psip-project.eu/ for further details.

FP7-287800 SALUS


UI/L or bilirubin>22 µmol/L) NSAI Hepatic cholestasis (alkaline phosphatase>240

UI/L or bilirubin>22 µmol/L) high weight heparin Hepatic cytolysis (alanine transaminase>110

UI/L or aspartate transaminase>110 UI/L) low weight heparin&age < 70 Hepatic cytolysis (alanine transaminase>110 UI/l

or aspartate transaminase>110 UI/L) low weight heparin&age ≥ 70 Hepatic cytolysis (alanine transaminase>110

UI/L or aspartate transaminase>110 UI/L) proton pump inhibitor Hepatic cytolysis (alanine transaminase>110

UI/L or aspartate transaminase>110 UI/L) statin&age < 70 High a CPK rate (CPK>195 UI/L) statin&age ≥ 70 High a CPK rate (CPK>195 UI/L) VKA&thymoanaleptic&NO selective serotonine reuptake inhibitor (SSRI)

Haemorrhage hazard (INR>4.9)

VKA&selective serotonine recapture inhibitor&NO respiratory obstruction


VKA&selective serotonine recapture inhibitor &respiratory obstruction


VKA&proton pump inhibitor&NO benzamide neuroleptic


VKA&proton pump inhibitor&benzamide neuroleptic Haemorrhage hazard (INR>4.9) VKA&quinolone&age < 70 Haemorrhage hazard (INR>4.9) VKA&quinolone&age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&macrolide Haemorrhage hazard (INR>4.9) VKA&cycline Haemorrhage hazard (INR>4.9) VKA&azole antibiotic Haemorrhage hazard (INR>4.9) VKA&cephalosporin &age < 70 Haemorrhage hazard (INR>4.9) VKA&cephalosporin &age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&amoxicilline and clav.ac.&age < 70 Haemorrhage hazard (INR>4.9) VKA&amoxicilline and clav.ac.&age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&other beta lactam Haemorrhage hazard (INR>4.9) VKA&penicillin &age < 70&NO diuretic Haemorrhage hazard (INR>4.9) VKA&penicillin &age < 70&diuretic Haemorrhage hazard (INR>4.9) VKA&penicillin &age ≥ 70&NO diuretic Haemorrhage hazard (INR>4.9) VKA&penicillin &age ≥ 70&diuretic Haemorrhage hazard (INR>4.9) VKA&aminoglycoside Haemorrhage hazard (INR>4.9) VKA&glycopeptide Haemorrhage hazard (INR>4.9) VKA&sulfamide Haemorrhage hazard (INR>4.9) VKA&hypoalbuminemia&NO low INR Haemorrhage hazard (INR>4.9) VKA&hypoalbuminemia&low INR Haemorrhage hazard (INR>4.9) VKA&systemic antifungal&NO griseofulvin Haemorrhage hazard (INR>4.9) VKA&type 3 antiarrhythmic&NO diuretic&age < 70 Haemorrhage hazard (INR>4.9) VKA&type 3 antiarrhythmic&NO diuretic&age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&type 3 antiarrhythmic&diuretic&age < 70 Haemorrhage hazard (INR>4.9) VKA&type 3 antiarrhythmic&diuretic&age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&antiobesity Haemorrhage hazard (INR>4.9) VKA&thyroid hormone&NO antiepileptic Haemorrhage hazard (INR>4.9) VKA&thyroid hormone&antiepileptic Haemorrhage hazard (INR>4.9) VKA&antiepileptic Haemorrhage hazard (INR>4.9) VKA&fibrate Haemorrhage hazard (INR>4.9) VKA&anti-gout&NO antiepileptic Haemorrhage hazard (INR>4.9) VKA&anti-gout&antiepileptic Haemorrhage hazard (INR>4.9) VKA&hypothalamic hypophyseal hormone Haemorrhage hazard (INR>4.9)

FP7-287800 SALUS


VKA&antispasmodic Haemorrhage hazard (INR>4.9) VKA&alcoholism Haemorrhage hazard (INR>4.9) VKA&tocopherol (vit E) Haemorrhage hazard (INR>4.9) VKA &antineoplastic Haemorrhage hazard (INR>4.9) VKA &peripheral vasodilatator&NO sympathomimetic drug


VKA &peripheral vasodilatator &sympathomimetic drug


VKA&type 1 antiarrhythmic Haemorrhage hazard (INR>4.9) VKA&hepatic insufficiency Haemorrhage hazard (INR>4.9) VKA&systemic steroidal anti inflammatory&NO anxiolytic


VKA&systemic steroidal anti inflammatory&anxiolytic


VKA&anti-diarrheal Haemorrhage hazard (INR>4.9) VKA&suspension of anti-diarrheal Haemorrhage hazard (INR>4.9) VKA&hypocalcemia &NO low INR Haemorrhage hazard (INR>4.9) VKA&hypocalcemia &low INR Haemorrhage hazard (INR>4.9) VKA&opioïd Haemorrhage hazard (INR>4.9) VKA&acetaminophen/paracetamol &age < 70 Haemorrhage hazard (INR>4.9) VKA&acetaminophen/paracetamol &age ≥ 70 Haemorrhage hazard (INR>4.9) VKA&NSAI Haemorrhage hazard (INR>4.9) VKA&osmotical laxative Haemorrhage hazard (INR>4.9) VKA&immunomodulation factor Haemorrhage hazard (INR>4.9) Lithium Lithium overdose (to high a lithium level) high weight heparin &hepatic insufficiency Heparin overdose (activated partial

thromboplastin time>1.23) high weight heparin &chronic renal insufficiency Heparin overdose (activated partial

thromboplastin time>1.23) high weight heparin &NSAI Heparin overdose (activated partial

thromboplastin time>1.23) high weight heparin &systemic steroidal anti inflammatory

Heparin overdose (activated partial thromboplastin time>1.23)

high weight heparin &plasma substitutes Heparin overdose (activated partial thromboplastin time>1.23)

high weight heparin Hypereosinophilia (éosinophilia>109/l) low weight heparin&age < 70 Hypereosinophilia (éosinophilia>109/l) low weight heparin&age ≥ 70 Hypereosinophilia (éosinophilia>109/l) quinolone Hypereosinophilia (éosinophilia>109/l) NO renal failure&high weight heparin Hyperkalemia (K+>5.3) NO renal failure&low weight heparin&age < 70 Hyperkalemia (K+>5.3) NO renal failure&low weight heparin&age ≥ 70 Hyperkalemia (K+>5.3) NO renal failure&high weight heparin&diabetes Hyperkalemia (K+>5.3) NO renal failure&low weight heparin&diabetes&NO laxative

Hyperkalemia (K+>5.3)

NO renal failure&low weight heparin&diabetes &laxative


NO renal failure&high weight heparin&angiotensin conversion enzyme inhibitor


NO renal failure&low weight heparin&angiotensin conversion enzyme inhibitor


NO renal failure &high weight heparin&NSAI Hyperkalemia (K+>5.3) NO renal failure&low weight heparin&NSAI Hyperkalemia (K+>5.3) renal failure&high weight heparin Hyperkalemia (K+>5.3)

FP7-287800 SALUS


renal failure&low weight heparin&age < 70 Hyperkalemia (K+>5.3) renal failure&low weight heparin&age ≥ 70 Hyperkalemia (K+>5.3) renal failure&high weight heparin&diabetes Hyperkalemia (K+>5.3) renal failure&low weight heparin&diabetes &NO laxative


renal failure&low weight heparin&diabetes&laxative Hyperkalemia (K+>5.3) renal failure&high weight heparin&angiotensin conversion enzyme inhibitor


renal failure&low weight heparin&angiotensin conversion enzyme inhibitor


renal failure&high weight heparin&NSAI Hyperkalemia (K+>5.3) renal failure&low weight heparin &NSAI Hyperkalemia (K+>5.3) NO renal failure&suspension of osmotical laxative


NO renal failure&suspension of other laxative Hyperkalemia (K+>5.3) NO renal failure&suspension of propulsive laxative


NO renal failure&peripheral sympatholytic Hyperkalemia (K+>5.3) NO renal failure&beta blocker&NO calcium blocker


NO renal failure&beta blocker&calcium blocker Hyperkalemia (K+>5.3) NO renal failure&angiotensin conversion enzyme inhibitor&age < 70


NO renal failure&angiotensin conversion enzyme inhibitor&age ≥ 70


NO renal failure&potassium sparing diuretic Hyperkalemia (K+>5.3) NO renal failure&suspension of potassium lowering diuretic


NO renal failure&potassium Hyperkalemia (K+>5.3) NO renal failure&amoxicilline and clav.ac.&age < 70


NO renal failure&amoxicilline and clav.ac. &age ≥ 70


NO renal failure &suspension of sulfamid or sulfonamid


NO renal failure&NSAI&age < 70 Hyperkalemia (K+>5.3) NO renal failure&NSAI&age ≥ 70 Hyperkalemia (K+>5.3) NO renal failure&suspension of systemic steroidal anti inflammatory


NO renal failure&digitalis glycoside Hyperkalemia (K+>5.3) NO renal failure&immunomodulation factor Hyperkalemia (K+>5.3) renal failure&suspension of osmotical laxative&age < 70


renal failure&suspension of osmotical laxative&age ≥ 70


renal failure&suspension of other laxative&NO hepatic cholestasis


renal failure&suspension of other laxative&hepatic cholestasis


renal failure&propulsive laxative Hyperkalemia (K+>5.3) renal failure&peripheral sympatholytic Hyperkalemia (K+>5.3) renal failure&beta blocker&NO thrombin inhibitor


renal failure&beta blocker&thrombin inhibitor Hyperkalemia (K+>5.3)

FP7-287800 SALUS


renal failure&angiotensin conversion enzyme inhibitor&NO opioïd


renal failure&angiotensin conversion enzyme inhibitor&opioïd


renal failure &potassium sparing diuretic Hyperkalemia (K+>5.3) renal failure&suspension of potassium lowering diuretic&NO thrombin inhibitor


renal failure&suspension of potassium lowering diuretic&thrombin inhibitor


renal failure&potassium Hyperkalemia (K+>5.3) renal failure&thrombin inhibitor Hyperkalemia (K+>5.3) renal failure&amoxicilline and clav.ac. Hyperkalemia (K+>5.3) renal failure&suspension of aminoglycoside Hyperkalemia (K+>5.3) renal failure&suspension of vitamin K antagonist Hyperkalemia (K+>5.3) renal failure&suspension of sulfamid or sulfonamid


renal failure&NSAI&NO potassium sparing diuretic


renal failure&NSAI&potassium sparing diuretic Hyperkalemia (K+>5.3) renal failure&suspension of systemic steroidal anti inflammatory


renal failure&digitalis glycoside Hyperkalemia (K+>5.3) renal failure&immunomodulation factor Hyperkalemia (K+>5.3) NO hypoalbuminemia &proton pump inhibitor Hypocalcemia (calcemia<2.2 mmol/L) NO renal failure&thrombin inhibitor Hypokalemia (K+<3.0) proton pump inhibitor&age < 70 Hyponatremia (Na+<130) proton pump inhibitor&age ≥ 70 Hyponatremia (Na+<130) quinolone&age < 70 Renal failure (creat.>135 µmol/L or urea>8.0

mmol/l) quinolone&age ≥ 70 Renal failure (creat.>135 µmol/L or urea>8.0

mmol/l) NSAI&NO aspirin&NO potassium lowering diuretic&age < 70

Renal failure (creat.>135 µmol/L or urea>8.0 mmol/l)

NSAI&NO aspirin&NO potassium lowering diuretic&age ≥ 70


NSAI&NO aspirin&potassium lowering diuretic&age < 70


NSAI&NO aspirin&potassium lowering diuretic&age ≥ 70


angiotensin conversion enzyme inhibitor&age < 70


angiotensin conversion enzyme inhibitor&age ≥ 70


VKA&griseofulvin VKA underdose (INR<1.6) VKA&antiepileptic VKA underdose (INR<1.6) VKA&anti-diarrheal VKA underdose (INR<1.6) VKA &suspension of anti-diarrheal VKA underdose (INR<1.6) VKA&digitalis glycoside&NO chronic renal insufficiency&age < 70

VKA underdose (INR<1.6)

VKA&digitalis glycoside&NO chronic renal insufficiency&age ≥ 70


VKA&digitalis glycoside&chronic renal insufficiency&age < 70


FP7-287800 SALUS


VKA&digitalis glycoside&chronic renal insufficiency&age ≥ 70


VKA&immunomodulation factor VKA underdose (INR<1.6) VKA &high INR &NO hypoalbuminemia VKA underdose (INR<1.6) VKA &high INR &hypoalbuminemia VKA underdose (INR<1.6) VKA &high INR &NO hypocalcemia VKA underdose (INR<1.6) VKA&high INR&hypocalcemia VKA underdose (INR<1.6) VKA&beta lactam&age < 70 VKA underdose (INR<1.6) VKA&beta lactam&age ≥ 70 VKA underdose (INR<1.6) VKA &prokinetic&NO high INR VKA underdose (INR<1.6) VKA&prokinetic&high INR VKA underdose (INR<1.6) VKA&antiacid VKA underdose (INR<1.6) NO cancer&NSAI Neutropenia (PNN<1500/mm3) proton pump inhibitor Neutropenia (PNN<1500/mm3) azole antibiotic Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/L) cephalosporin&NO hepatic insufficiency Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/L) cephalosporin&hepatic insufficiency Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/lL statin Increase of pancreatic enzymes (amylase>90

UI/lL or lipase>90 UI/L) systemic steroidal anti inflammatory Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/lL proton pump inhibitor&NO hepatic insufficiency Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/L) proton pump inhibitor&hepatic insufficiency Increase of pancreatic enzymes (amylase>90

UI/L or lipase>90 UI/L) NO cancer&NSAI Pancytopenia azole antibiotic Thrombocytosis (count>600,000) low weight heparin Thrombocytosis (count>600,000) systemic antifungal Thrombocytosis (count>600,000) quinolone&age < 70 Thrombocytosis (count>600,000) quinolone&age ≥ 70 Thrombocytosis (count>600,000) NO cancer&high weight heparin&age < 70 Thrombopenia (count<75,000) NO cancer&high weight heparin&age ≥ 70 Thrombopenia (count<75,000) NO cancer&low weight heparin&NO hemostasis disorder (diag)

Thrombopenia (count<75,000)

NO cancer&low weight heparin&hemostasis disorder (diag)


NO cancer&NSAI&NO hepatic insufficiency Thrombopenia (count<75,000) NO cancer&NSAI&hepatic insufficiency Thrombopenia (count<75,000) NO cancer&antiH2 Thrombopenia (count<75,000) NO cancer &platelet aggregation inhibitor&NO NSAI


NO cancer&potassium lowering diuretic Thrombopenia (count<75,000) NO cancer &proton pump inhibitor&NO hepatic insufficiency


NO cancer&proton pump inhibitor &hepatic insufficiency


NO cancer&acetaminophen/paracetamol Thrombopenia (count<75,000) NO cancer&beta blocker&NO hepatic insufficiency


FP7-287800 SALUS


NO cancer&beta blocker&hepatic insufficiency Thrombopenia (count<75,000) NO cancer&beta lactam&NO hepatic insufficiency


NO cancer&beta lactam&hepatic insufficiency Thrombopenia (count<75,000) NO cancer&quinolone&NO hepatic insufficiency


NO cancer&quinolone &hepatic insufficiency Thrombopenia (count<75,000) NO cancer&type 3 antiarrhythmic &NO hepatic insufficiency


NO cancer&type 3 antiarrhythmic&hepatic insufficiency


NO cancer &antiparasitic&NO hepatic insufficiency


NO cancer &antiparasitic&hepatic insufficiency Thrombopenia (count<75,000) NO cancer&selective serotonine recapture inhibitor&NO hepatic insufficiency


NO cancer&selective serotonine recapture inhibitor&hepatic insufficiency


proton pump inhibitor Diarrhoea (prescription of an anti-diarrheal) proton pump inhibitor Diarrhoea (prescription of an antipropulsive) NO beta lactam&NO antineoplastic&NSAI&age < 70&NO diabetes

Bacterial infection (detected by the prescription of antibiotic)

NO beta lactam&NO antineoplastic&NSAI&age < 70&diabetes


NO beta lactam &NO antineoplastic&NSAI&age ≥ 70&NO diabetes


NO beta lactam&NO antineoplastic&NSAI&age ≥ 70&diabetes


acetaminophen/paracetamol paracetamol overdose (detected by the prescription of acetyl-cystein)

systemic steroidal anti inflammatory &age < 70 Fungal infection (detected by the prescription of local antifungal)

systemic steroidal anti inflammatory &age ≥ 70 Fungal infection (detected by the prescription of local antifungal)

azole antibiotic&NSAI < 1 Fungal infection (detected by the prescription of a systemic antifungal)

azole antibiotic&NSAI ≥ 1 Fungal infection (detected by the prescription of a systemic antifungal)

Antiparasitic&NSAI < 1 Fungal infection (detected by the prescription of a systemic antifungal)

Antiparasitic&NSAI ≥ 1 Fungal infection (detected by the prescription of a systemic antifungal)

amoxicilline and clav.ac. Fungal infection (detected by the prescription of a systemic antifungal)

other beta lactam Fungal infection (detected by the prescription of a systemic antifungal)

proton pump inhibitor&age < 70 Fungal infection (detected by the prescription of a systemic antifungal)

proton pump inhibitor&age ≥ 70 Fungal infection (detected by the prescription of a systemic antifungal)

high weight heparin Haemorrhage (detected by the prescription of haemostatic)

low weight heparin Haemorrhage (detected by the prescription of

FP7-287800 SALUS


haemostatic) VKA&age < 70&NO respiratory insufficiency Haemorrhage (detected by the prescription of

haemostatic) VKA&age < 70&respiratory insufficiency Haemorrhage (detected by the prescription of

haemostatic) VKA&age ≥ 70&NO respiratory insufficiency Haemorrhage (detected by the prescription of

haemostatic) VKA&age ≥ 70&respiratory insufficiency Haemorrhage (detected by the prescription of

haemostatic) NSAI Haemorrhage (detected by the prescription of

haemostatic) VKA&NO respiratory insufficiency VKA overdose (detected by the prescription of

vitamin K) VKA&respiratory insufficiency VKA overdose (detected by the prescription of

vitamin K)

Table 13: “PSIP Project” - Published Rules

3.8.2 Rules published in the paper “Retrospective analysis of the frequency and recognition of adverse drug reactions by means of automatically recorded laboratory signals” by Tegeder et al.

In Figure 15 laboratory signals to detect ADEs are listed, that are published in the paper “Retrospective analysis of the frequency and recognition of adverse drug reactions by means of automatically recorded laboratory signals” by Tegeder et al.16.

16 Tegeder, I.; Levy, M.; Muth-Selbach, U.; Oelkers, R.; Neumann, F.; Dormann, H.; Azaz-Livshits, T.; Criegee-Rieck, M.; Schneider, H.T.; Hahn, E-G.; Brune, K.; Geisslinger, G. (1999): Retrospective analysis of the frequency and recognition of adverse drug reactions by means of automatically recorded laboratory signals.

FP7-287800 SALUS


Figure 15: Tegeder et al. – Published Laboratory Signals

3.8.3 Rules published in the paper “Computerized surveillance of adverse drug reactions in hospital: pilot study” by Azaz-Livshits et al.

In Figure 16 laboratory signals to detect ADEs are listed, that are published in the paper “Retrospective analysis of the frequency and recognition of adverse drug reactions by means of automatically recorded laboratory signals” by Azaz-Livshits et al.17

17 Azaz-Livshits, T.; Levy, M.; Sadan, B.; Shalit, M.; Geisslinger, G.; Brune, K. (1998): Computerized surveillance of adverse drug reactions in hospital: pilot study.

FP7-287800 SALUS


Figure 16: Azaz-Livshits et al.18 – Published Laboratory Signals

3.8.4 Rules published in the paper “Implementation of a System for Computerized Adverse Drug Event Surveillance and Intervention at an Academic Medical Center” by Kilbridge et al.

In Figure 17 further signals to detect ADEs are listed, that are published in the paper “Implementation of a System for Computerized Adverse Drug Event Surveillance and Intervention at an Academic Medical Center” by Kilbridge et al.19

18 Azaz-Livshits, T.; Levy, M.; Sadan, B.; Shalit, M.; Geisslinger, G.; Brune, K. (1998): Computerized surveillance of adverse drug reactions in hospital: pilot study. 19 Kilbridge, P. M.; Alexander, L.; Ahmad, A. (2006): Implementation of a System for Computerized Adverse Drug Event Surveillance and Intervention at an Academic Medical Center.

FP7-287800 SALUS


Figure 17: Kilbridge et al. – Published ADE Detection Rules

FP7-287800 SALUS


4 DETECTION OF UNKNOWN ADES In case a diagnosis is added to the EHR of a patient that is not explainable by the known ADE detection module of ANT, statistical methods will be executed to search for conspicuous events in the whole clinical database. In the SALUS project, we have developed two different approaches that will be described in this chapter. The described data mining approaches to detect unknown ADEs are implemented, but they have to be tested with real patient data to tune the data mining parameters. To be able to test and improve this detection module of the ANT, access to real data is needed. This test will be realized in the final six months of the project, where pilot application is planned to be run by end user sites. Therefore, further information on the results will be reported in an update of this deliverable by Month 36.

4.1 Bump Hunting for ADE detection The aim of this work is the detection of temporal patterns between the prescription of a drug and a medical event of interest. In order to perform such detections Noren’s already introduced IC-statistic20 as well as his notation will be used, as these suit the aim of detection the most. The IC-statistic is based on the comparison of the observed number of patients who took a specific drug in combination with the occurrence of the medical event of interest and it’s expected value under descriptive independence. In case the observed number of patients exceeds the expected number, an ADE will be very likely to observe. Hence, if the deviation of the expected number and the observed number of patients exceeds a certain boundary based on a certain significance level, this should indicate an ADE. Optionally, a boundary based on the IC-statistic itself can be used as well in order to avoid multiple testing. Regarding the above mentioned task the Bump Hunting routine can help to identify and characterize patients/ patient groups who took a certain drug and experienced a certain medical event of interest either very often or seldom. In order to identify and characterize patients it is necessary to create so called boxes (boxes contain groups of patients who either experienced an ADE very often or seldom) with group specific characteristics (e.g. female, 20-30 years,…). The Bump Hunting algorithm consists of so called peeling steps followed by pasting steps. In the peeling step each possible covariate is regarded separately and at each step a certain amount of data (subboxes) is peeled away up to a certain predefined border α. This procedure will result in a detection of a box, so called peeling box. In order to further optimize the peeling result pasting will then be performed via adding subboxes to the peeling result. Calculating the IC 0,025 and checking whether the IC 0,025 is greater than 0 is of major interest, as an IC 0,025 > 0 could be indicating patient groups with ADEs. Finally a box will be returned containing a high number of patients with ADEs as well as their characteristics. In order to detect and characterize other ADE risk groups the already detected box will be deleted (all patients inside this box will be deleted from the dataset). This procedure will be iterated as long as no more boxes containing patients with ADEs can be detected. All detected boxes are called a Bump. However, in order to meet the classification task boxes containing patients who did not experience an ADE must be detected. Hence, the characterization of patient groups with a very low IC will be focused on. Therefore, the Bump Hunting routine will be applied to all patients who were not grouped in one of the previously detected boxes that are characterizing patients who experienced an ADE via looking for patient groups (boxes) where the IC 0,025 < 0. This should be an indicator of patients who did not experience an ADE. Thus, the Bump hunting routine is able to allocate patients into ADE risk groups as well as into groups of patients with no ADEs. The user will be able to group patients into one of both classes as well as characterizing them.

20 Niklas Norén, G.; Hopstadius, J.; Bate, A.; Star, K.; Ralph Edwards, I. (2010): Temporal pattern discovery in longitudinal electronic patient records, Data Min Knowl Disc, 20:361–387.

FP7-287800 SALUS


The following Figure 18 depicts a Bump Hunting result for the detection of patient groups with ADEs based on pseudo data.

Figure 18: Bump Hunting for ADE Detection

In order to ensure the ability of a 2D-plot only age and a lab result were included into the model. The result shows that two ADE risk groups could be detected which are defined as: Box 1: age >= 18 Box 2: age <= 5; lab result <= 1.8. Hence, the Bump Hunting result shows that patients elder than 18 years and patients younger than 5 years with a lab result lower than 1.8 are most likely to have experienced an ADE in contrast to all other patients. 4.1.1 Introduction to Bump Hunting The following descriptions correspond to Friedman and Fisher.21 Bump Hunting or patient rule induction method (PRIM) is a data mining method for identifying subgroups (rectangular regions) containing a high mean of a designated target variable. It’s typical application scenario is a data mining situation where the analyst wants to identify some few best subgroups.

21 Friedman, J. H.; Fisher, N. I. (1998): Bump Hunting in High-Dimensional Data.

FP7-287800 SALUS


Let denote the target variable measured on the -th subject for = 1, . . . , and let for = 1, . . ., represent the values in the -dimensional input space constructed by p covariates. The aim is to detect a subregion out of the input space , by either maximizing the mean in a subregion

>>

or minimizing it << .

The resulting subregion (subbox) is defined by paraxial borders. A bump can be regarded as a union of subboxes

for = 1, . . . , representing the index of the boxes in the input space. Let denote the -th

vector of a covariate and possible values of an input variable out of that represents all

possible values of . Thus can be interpreted as a subset of . Then boxes are defined as

which means subboxes are intersections of possible values of different input variables that simply represent borders of . As a summary of the interpretability: • Boxes are intersections of borders • Bumps are unions of boxes PRIM consists of two different optimization procedures, the peeling procedure which is followed by the pasting procedure. Peeling, the first phase of detecting a subregion, starts with a box that covers the entire input space. In every peeling step a certain percentage of each variable is peeled away separately for each variable. The amount of data being peeled away is controlled by the α-parameter, called peeling quintile. A peeling quintile actually determines two quintiles, α and 1-α, because peeling is a two-sided procedure. It starts at the minimum as well as at the maximum of one covariate and calculates the mean of the target variable after having removed a subbox (opposite

of ) from the larger box B until it reaches α and 1-α respectively. This procedure is then applied to all other input variables a well. Figure 19 illustrates this part of the peeling process for a real valued input variable y. Each vertical line represents a quintile and the red lines show both peeling quintiles.

iy i i n ijx jp p

B I IB ⊂

ijxy B∈ Iy

ijx∈

ijxy B∈ Iy

ijx∈

BkB

k

K

k

BBump ∪1=

=

k K jx j

jks jx jS

jx jks jS kB

)(1

jjk

p

jk xsB ∈=

=∩

jx

B

*kB

kB

FP7-287800 SALUS


Figure 19: Illustration of peeling process; at each vertical line the mean in the subbox is

calculated; the red lines represent the α − and 1 − α − quintile After having calculated the means, the actual peeling has to be performed. For this reason it is necessary to select the highest or the lowest mean respectively. By selecting this mean, the

subbox that needs to be peeled away in order to optimize the mean and to detect the region of the

input space with an extreme mean is selected out of the class of eligible subboxes for removal as well. Referring to Figure 7, an input space of a single dimension, the number of eligible subboxes for peeling is eight. The following equation illustrates this selection process.

= (max( )).

After having removed from box B, the current box , as well as the support (described below), are updated in the following manner:

kB*kB

*kB

*kB

*kB *

kj BBxy −∈

*kB kB

FP7-287800 SALUS


= = B − ,

whereas β B (support) represents the relative frequency of observations inside the box and can be calculated in the following manner:

=

where I() is a function counting the number of subjects left in the new box. At the beginning, without having started the peeling procedure, the box mean is identical to the global mean. This decription of a single peeling step is then iterated as long as the box mean can be optimized (improved) and the support does not fall below the threshold parameter which defines the minimal size of a box.

The lower the value, the better the fit, because lower values allow the algorithm to find even the

smallest subpopulation in the input space by specifying as . If one of these stopping criteria

matches the calculations, the peeling process stops and returns the detected peeling box. Peeling is followed by the pasting procedure which is simply an inverted peeling process. Starting at the peeling result, pasting tries to further optimze the box mean by adding cases to the peeling result and to the following box results as shown in the next two equations which is performed stepwise just like peeling. The results are enlarged borders, as Bump Hunting tries to cover the largest region possible. As one utilizes the peeling result for pasting, it is clear that the pasting procedure only uses variables and borders selected in peeling to be further optimized. That is why variable selection in PRIM can only be performed in the peeling process. The first step will be to select a region to be pasted

= (max( )).

Having added to the box B, B is updated as shown in the following equation:

= B − .

The support is updated simultaneously. Having already calculated a peeling box characterized by the borders of the current box, it is necessary to fix all borders, except the one dimension that pasting will be performed on. The size of the region added to the current box can be α , α being the peeling quintile and the size of the current box. Stopping criteria do not differ from those of peeling and so the entire Bump Hunting algorithm stops after reaching one of the two criteria to end the pasting process and a single subbox with the most extreme mean will be returned. A plot of the box mean development given the support, which is called trajectory is sometimes useful and will be implemented in the algorithm. The necessary of further extending and modifying PRIM is quite obvious. Via calculating multiple subboxes (Bump) the algorithm is more flexible in describing the optimal region(s). Furthermore, peeling and pasting, as already described only work for continuous input variables. That is the reason why adjustments had to be done for different types of input variables. As input variables can either be continuous, nominal or ordered, peeling and pasting need to be adjusted to every type of variable. For example, it would not make sense to peel away just certain percentage of one category of a binary variable such as the gender. One is interested in either selecting males or females (a single class) to be able to define a criterion for the subbox-construction for nominal variables. Regarding ordered values, the necessity of keeping the ordered structure of a variable seems to be obvious. That is why only extreme categories, the lowest or the highest category, should be peeled or pasted. Continuous variables can be pasted and peeled as already

newB kB *kB

newB

Bβ ∑=

∈n

inewi BxI

n 1

)(1

Bβ 0β

0β

0β n1

*kB

*kB *

kj BBxy +∈

*kB

newB *B

Bβ

BNBN

FP7-287800 SALUS


described in figure 6, returning an interval containing it’s borders. Finally, it is clearly obvious that the peeling quintile α is only relevant for continuous variables, as the analyst is not interested in peeling or pasting a certain percentage of a category, but rather in selecting an entire one. The algorithm proposed and implemented by Friedman and Fisher will be extended in this work to handle both nominal and ordered input variables. As a single subbox may not be able to classify all subjects efficiently, more subboxes can be calculated in order to describe the optimal region(s). The first subbox induced using the entire data set is removed from the data to calculate the next bump with the data left. This procedure will be repeated as long as the box mean falls below the global mean or the box specific stopping criteria are reached. Although multiple subboxes are induced in such a procedure, the entire input space cannot be covered without leaving parts of the input space undefined because after having reached a stopping criterion the cases not being characterized by any of the previous subboxes are still undefined. It is also possible that the procedure does not even find a single subbox, although this rarely happens due to the patient nature of the algorithm that can be controled via the peeling quintile. The main advantage of the method is the higher quality of the search results due to a large hypothesis space and a patient hill climbing strategy. The patience is expressed via the α-parameter which allows to peel and paste very small boxes by choosing very small values. That is why after having performed one peeling or pasting step there is more data left to enable the algorithm to find more optimal subregions, checking more possibilities. This permits later steps to take advantage of structures that were not covered by earlier steps or to prevent misdirections. Moreover, the method is non-parametric so the user does not need to check any assumptions as in parametric models. The result is easy to interpret and a variable selection is performed automatically. In addition, missing values do not need any special consideration, as the algorithm permits to maintain the calculations considering all availabe cases. A major disadvantage of PRIM is the tendency to overfit, as the detected bumps usually only fit the training data well and show a poor performance on external data. Moreover, the algorithm contains two parameters that have to be evaluated which is very time consuming as one cannot regard both parameters independently but all combinations of those. 4.1.2 Adjusting Bump Hunting for ADE detection PRIM needs to be extended for ADE detection before applying it to such data. A detailed explanation as well as another modification called Bagging that represents a common technique in Machine Learning will be mentioned in the following sections. As the prediction accuracy and stability of variable selection of methods used for classification are of major interest, the following section will be naturally dedicated to the PRIM performance. The Bump Hunting algorithm, introduced by Friedman and Fisher is developed to minimize or maximize the mean of the output variable. However, it is also possible to extend Bump Hunting for ADE detection tasks by allowing the utilization of IC Δ mentioned by Noren et al. and further modified as a surrogate target variable. Instead of maximizing or minimizing the mean one could maximize or minimize the IC Δ given the current box in order to distinguish between subregions containing a lot of ADEs and those regions that are not ADE occurrence specific (the latter only mentioned for evaluation purposes). This approach will then be able to detect subregions in an input space having the additional feature of an automated feature selection describing main effects as well as interactions with the ADE of interest. It can be formally illustrated as shown in the following equation by using the notation introduced by Noren et al. [2010]:

1B

2B

)5.0)|(5.0|

log()(+

+=Δ

BnEBn

BIC uxy

uxy

FP7-287800 SALUS


The aim of using this measure is to detect a bump by stepwise increasing (ADE boxes) or decreasing (no ADE boxes) the IC Δ in each iteration, in order to have a large number of observations belonging to the desired classes in the finally detected bump. Thus, the global mean as one of the stopping criteria can be substituted by IC Δ = 0 because an IC Δ = 0 is the cut-off that enables to separate the classes (ADE, no ADE).

4.2 Clustering method to detect unknown ADEs This approach enables the user to detect and distinguish between the class of ADE free patients and those who experienced an ADE. It applies a clustering approach to the data at hand and already showed good results on artificial data. The following section describes the method in detail. The tool consists of a method that enables the user to detect unknown ADEs based on ICD-coded diagnosis data. It utilizes a hierarchical clustering approach in combination with the hamming distance that can manage to calculate distances between subjects based on categorical data.

Methods

The aim of the tool should be to cluster patients that show similar characteristics. As unknown ADEs (adverse drug events) are very rare events that can occur e.g. 10 times in a population of 10000 patients the clustering result should manage to identify and return the patients with rare events to the user. The fact that these events are very rare makes it difficult to define patients having these rare events as a cluster. Hence, the target should rather be to distinguish these events from a huge baseline of patients remaining ADE free. Thus the aim can be defined as an outlier detection where the rare events can be regarded as outliers. Let yit denote the information of the ith patient with i = 1, …, n observations at time t (t ϵ {before prescription of drug, after prescription of drug}). yit is a binary indicator variable being observed as 1 in case the event of interest occurred and else 0. xit contains the information whether a specific drug of interest has been prescribed for the ith patient at time point t – then the observation will be 1 - or not – with an observation 0. Here the indices are identical to the indices of variable yit and can also be interpreted as an indicator variable. As an unknown ADE can be characterized as an observation with an event of interest after drug prescription having no symptoms prior to drug prescription, the information of interest could be defined as an observation vector with (yib = 0, yia = 1). All other combinations of this observation vector can be regarded as patients without ADEs. Hence, this information is binary again, can be coded as 1 if the values (yib = 0, yia = 1) appear and 0 else and is denoted as Δi. Thus this variable contains the information of whether a patient had an ADE or not. Based on this technique the Δi must be calculated for all individuals and events of interest fixing the drug of interest. Thus the ith individual will not only have one Δi but p Δis with p = 1, …, k events of interest. This information vector is then denoted as Δip. Having these vectors for all patients used for detection, a hierarchical clustering algorithm applying the hamming distance can be used to distinguish between patients with an without ADEs. As already mentioned most of the patients will have the same distance to each other as they will form some kind of baseline or a huge cluster without ADEs respectively and only a few unique distances will indicate one or more ADE occurences because many interesting events associated with one drug are regarded.

Application to artificial data

Applying the above mentioned method to artificial data consisting of 13 patients, one drug of interest, two events of interest and 3 out of the 13 patients were predefined patients with ADEs returns following results in R (see Figure 20).

FP7-287800 SALUS


Figure 20: Cluster Dendrogram

The image shows a dendrogram which is typically returned as a result of a hierarchical clustering approach. As it can be seen there is a baseline of 10 Patients who form the cluster of no ADEs. The other 3 patients are those who were predefined to show ADEs. Two of them are clustered because they show exactly the same number of events and the other one was defined to have both events of interest and hence does not belong to the cluster of the other two. This result shows that the all predefined ADEs were detected by the algorithm. A test on more patients showed similar satisfying results. However, validating the method on real data is imperative to proof its clinical value.

FP7-287800 SALUS


5 IMPLEMENTATION OF THE ADE NOTIFICATION TOOL

The ANT is composed of several components (see Figure 21). The key component is the “ADE Notification Manager”. Using this component, the data is retrieved from the SIL and forwarded to the ANT Content Formatter. After the ADE detection and notification process, confirmed ADEs will be forwarded to the IRT through the ADE Notification Manager. In the following sections, all ANT related components will be described in more detail (for more details on other SALUS components, see the referenced Deliverables).

Figure 21: Overview of the system architecture of ANT and surrounding components

5.1 ADE Notification Manager The ADE Notification Manager sets up the interface for the surrounding SALUS components of the ADE Notification Tool and manages the data flow from the Semantic Interoperability Layer Data Service (SIL DS) and to the ICSR Reporting Tool. By the use of the SIL DS, the ADE Notification Manager queries the underlying EHR system to receive the data in the SALUS Common Information Model (CIM) format which is RDF (see Deliverable 4.3.1 for further details). The ANT prototype uses the SIL DS to query for a single patient utilizing a simple HTTP request. The patient is identified by his Id. As an example, the results for a specific patient under TUD settings are attached in Appendix. An example for LISPA setting is

Interoperability Framework

Patient ID / subscription

Semantic Interoperability Layer

EHR Database

Known ADE Detection

EHRDetection of ADE

Indicators

Data Mining

Terminology Server

IHE QED/CM

XML/RDFConversion

CIM-Converter

SPARQL

ADE Notification Manager

Semantic Data Service

Technical Interoperability

Layer

EHR (RDF)

ADE Notification Tool

IRT

confirmed ADE

ANT Content Formatter

FP7-287800 SALUS


attached in Appendix 2. Moreover, ANT uses subscription based mechanisms provided by the SIL DS to receive data from the moment it becomes available from the EHR system. In order to forward the data to the ADE Notification Tool, the ANT Content Formatter converts the query results into the ADE content format that can be processed by the tool components (see section 5.2.1). The ADE Notification Tool is able to detect known ADEs and suspected, currently unknown ADEs. While a physician in a hospital gets notified of every potential ADE, the suspected unknown ADEs should be reported to the regulatory authorities. In order to achieve this, the ICSR Reporting Tool eases the reporting process (see Deliverable 5.3.1 for further details). To initiate this reporting process, the ADE Notification Manager notifies the ICSR Reporting Manager of a suspected unknown ADE via a web interface (described in D5.3.1, section 6.2) and forwards all available data of a patient in order to avoid double querying by the ICSR Reporting Tool. 5.2 ADE Notification Tool

The ANT consists of three layers in accordance with the usual principles of software design. These layers and its components are described in the following subsections. 5.2.1 ANT Datamodel The ANT Datamodel is initialized with the results coming from the SIL DS. To be able to process the data, the result model (see Appendix 1 and Appendix 2) has to be transformed into the ANT content format. The ANT uses a patient object that contains all available data on a patient, for example age and gender. Besides this, the patient object includes four major lists containing all diagnoses, medications, lab results and procedures. The ANT content format is illustrated in Appendix 3. To extract these data and fill the local data structure, the following queries are executed on the result model.22 - Query for the gender of a patient: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX salus: <http://www.salusproject.eu/ontology/common-information-model#> SELECT ?patient ?genderCode WHERE { ?patient rdf:type salus:Patient. ?patient salus:gender ?gender. ?gender salus:code ?genderCode. } - Query for the birthday of a patient to be able to calculate the age: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX salus: <http://www.salusproject.eu/ontology/common-information-model#> SELECT ?patient ?birthday WHERE { ?patient rdf:type salus:Patient.

22 Because of several improvements in our common information model, the exact queries differ from time to time. Therefore, the listed queries have to be seen as examples for the data extraction process.

FP7-287800 SALUS


?patient salus:dateOfBirth ?birthday. } - Query for the diagnoses of a patient: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX salus: <http://www.salusproject.eu/ontology/common-information-model#> SELECT ?patient ?salusProblemName ?salusProblemCode ?salusProblemCodeSystemName ?salusProblemDate WHERE { ?patient salus:condition ?conditions. ?conditions salus:problemCode ?problemCodes. ?problemCodes salus:displayName ?salusProblemName. ?problemCodes salus:code ?salusProblemCode. ?problemCodes salus:codeSystemName ?salusProblemCodeSystemName. ?conditions salus:problemDate ?problemDates. ?problemDates salus:low ?salusProblemDate. } - Query for the medications of a patient: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX salus: <http://www.salusproject.eu/ontology/common-information-model#> SELECT ?patient ?salusMedicationName ?salusMedicationCode ?salusMedicationStart ?salusMedicationStop WHERE { ?patient salus:medication ?medications. ?medications salus:medicationInformation ?medInfos. ?medInfos salus:codedActiveIngredient ?medCode. ?medCode salus:displayName ?salusMedicationName. ?medCode salus:code ?salusMedicationCode. ?medications salus:indicateMedicationStartStop ?medTimes. ?medTimes salus:low ?salusMedicationTime. OPTIONAL {?medTimes salus:high ?salusMedicationStop.} } - Query for the results of a patient: PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX salus: <http://www.salusproject.eu/ontology/common-information-model#> SELECT ?patient ?salusResultName ?salusResultCodeSystemName ?salusCode ?salusResultValue ?salusResultUnit ?salusResultDate WHERE { ?patient salus:result ?results. ?results salus:resultValue ?resValue. ?resValue salus:pqValue ?pqValue. ?pqValue salus:value ?salusResultValue. ?pqValue salus:unit ?salusResultUnit. ?results salus:resultType ?type. ?type salus:displayName ?salusResultName.

FP7-287800 SALUS


?type salus:codeSystemName ?salusResultCodeSystemName. ?type salus:code ?salusCode. ?results salus:resultDateTime ?resultDate. ?resultDate salus:value ?salusResultDate. } Depending on what data is available, the ANT datamodel loads the databases that are relevant in a respective analysis situation. For example if lab results are not available such as in the LISPA scenario, the Detection Rule Repository part that contains all lab results that can be monitored by the ANT are not loaded. For the detection of known ADRs, the ANT prototype implementation benefits from 2 databases: The PROTECT DB and SIDER, both represented in Excel format and introduced in chapter 2. In their original form these databases were not applicable in respect of the SALUS ANT datamodel, because the PROTECT DB doesn’t contain ATC-Codes for drugs and SIDER doesn’t include neither ATC-Codes nor MedDRA-Codes, which are used in terms of SALUS CIM. To address this issue, the utils-class implements two methods to extend these databases by the required ATC- and MedDRA-codes: - public static void adjustPDB():

This method queries the substance names of a drug, searches in other background databases for the specific ATC-Code based on the exact name and adds a new cell containing the ATC-Code. - public static void adjustSIDERDB(): This method queries the SIDER DB entries for the “MedDRA preferred name” and the substance names of a drug. Based on some other databases, the “MedDRA preferred name” is correlated with the MedDRA-Code and the substance names of a drug are correlated with the specific ATC-Code. Afterwards, both codes are written into the database. As a result, both databases are now applicable for the detection of known ADRs based on the SALUS CIM (see Figures 22 and 23).

Figure 22: PROTECT DB, enhanced with ATC-Codes

FP7-287800 SALUS


Figure 23: SIDER DB, enhanced with ATC-Codes and MedDRA-Codes

ANT uses a centralized database approach by importing and correlating all information from both of these data sources into one database (see section 2.2). In total, the resulting database contains more than 160,000 data entries on known ADRs. For the detection based on ADE detection rules, a detection rule repository was built, where, according to the detection rule types, specific parameters can be populated. We distinguish between the following detection rule types (see chapter 3 for conceptual details): - antidotes:

For each antidote rule entry, the following attributes are populated: - RULE_ID: ID of the rule - RULE_TYPE: type of the rule, in this case: antidote prescription rule - CODESYSTEM: codesystem the antidote is classified in (for example: ATC-Code) - CODE: the code of the condition according to the CONDITION_CODESYSTEM - NAME: name of the condition, for example name of the antidote - ROUTE: if there are differences regarding the form an antidote can be given, the route for this rule has to be specified - DESCRIPTION: textual description of the rule - THRESHOLD: threshold to define a value, where this rule needs to be checked to prevent over-alerting - FALSE_ALARM_COUNTER: counter of how often this rule produces false positive alerts - COUNTER: generic counter of how often this rule produces alerts

FP7-287800 SALUS


- laboratory parameters: For each lab parameter rule entry, the following attributes are populated:

- RULE_ID: ID of the rule - RULE_TYPE: type of the rule, in this case: lab result rule - CODESYSTEM: codesystem the lab result is classified in (for example: LOINC-Code) - CODE: the code of the condition according to the CONDITION_CODESYSTEM - NAME: name of the condition, for example name of the lab result - UNIT: unit of the lab result - AGERANGE: age range23 of a patient this rule is applicable for - GENDER: gender24 of a patient this rule is applicable for - NORMAL_RANGE: normal range of the parameter - CHANGEFACTOR_TO_REFVALUE: reference factor to produce an alert – if this factor is reached after the drug was taken compared to the value before the drug was taken an alert will be produced - THRESHOLD: threshold to define a value, where this rule needs to be checked to prevent over-alerting - FALSE_ALARM_COUNTER: counter of how often this rule produces false positive alerts - COUNTER: generic counter of how often this rule produces alerts - drug interactions:

For each drug interaction detection rule entry, the following attributes are populated:

- ATC_CODE1: ATC Code of drug 1 involved in the interaction. - ATC_CODE2: ATC Code of drug 2 involved in the interaction. - DESCRIPTION: Description to be displayed to the physician. - postmarketing ADRs:

For each postmarketing ADR detection rule entry, the following attributes are populated: - SUBSTANCEATC: The ATC Code of the suspected drug. - ADR: The MedDRA PT Code of the ADR. - ADE related diagnoses:

For each ADE related diagnosis detection rule entry, the following attributes are populated: - NAME: The Name of the ADE related diagnosis. - ICD10GMx_Code: The ICD 10 GM code of the diagnosis (x = version 2004-2013) - ICD9GM_Code: The ICD 9 GM code of the diagnosis. - PROBCLASS: Probability class for the presence of an ADE. Some of these rule parameters are populated in Microsoft Excel format in order to enable the physician to easily adjust the parameters, but others are directly implemented in a relational database.

The ANT prototype implements nearly all rules of the ADE detection rule repository specified in chapter 3. Additionally, the LISPA specific ADE detection rules are exactly implemented as described in section 3.2.

5.2.2 ANTControl 23 Ranges have to be specified following the usual mathematical description of intervals [min, max]. 24 Gender has to be specified as “F” for female or “M” for male.

FP7-287800 SALUS


The ANTControl layer implements the execution of the ADE detection processes. In order to start the execution, the engine classes of the databases have to be instantiated. This is done automatically depending on what kind of ADE detection is possible. If the known ADE Detection is possible, the diagnoses and medications of a patient are extracted from the ANTdataModel. As a prerequisite, in an initial step the dates of diagnoses and medications are compared since a diagnosis has to be made after the drug was given, otherwise the presence of a known ADE in that context can be excluded. For example, if a patient has taken the drug “Nifedipine” (ATC-Code: C08CA05) and gets fever (MedDRA-Code: 1000591125), the engine detects the following correlation in the known ADR-database (see Figure 24):

Figure 24: Nifedipine – known ADRs

In this example case, the patient had a known ADR that is not requiring reporting and therefore no notification will be produced, but the physician can review the found, known ADR in a separate list, where all suspicious events are collected. In the next step, if the presence of a known ADE can be excluded after the first analysis step, the ADE detection rules from the detection rule repository are applied to the patient’s EHR data. The decision which ADE detection methods are executed depends on the type of clinical data available in a given EHR. For example, if information on the drug therapy including medication dosages and periods is not documented in the EHR, the analysis process will skip the “changes in pharmacotherapy” detection rule. This step is implemented in the same way for all the implemented and already described detection rules (antidotes, postmarketing ADR information, ADE related diagnoses, drug interactions, lab results). In the following, the detection rules based on antidotes and laboratory parameters are described to give two implementation examples. The patient data is checked for a prescription of an antidote by screening all medications the patient got and comparing the ATC-Codes of all drugs with the antidote detection rule repository. For example, if the physician prescribed “naloxone” (ATC-Code: V03AB15) this indicates the possible presence of an ADE (see Figure 25).

Figure 25: Naloxone – antidote that indicates the possible presence of an ADE

At the presence of lab results for a specific patient, the DRLabDetectionEngine can be used for monitoring in order to detect suspected ADEs based on indications by lab results. This is implemented by comparing the blood parameter values correlated with the unit and identified by the LOINC-Code with the rules from the lab detection rule repository. In addition, some rules have further conditions, 25 Note: For the translation from ICD10-Codes to MedDRA-Codes, the terminology mapping services of SALUS can be used.

FP7-287800 SALUS


like age ranges and gender specific value ranges. A blank condition is to understand as “always true”. In case there is the same lab parameter value available before a drug was taken, that value is compared using the CHANGEFACTOR_TO_REFVALUE attribute. If there is no reference value available, the normal ranges are taken into account26. For example, a female patient has a “myoglobin” (LOINC-Code: 2639-3) value of 45 ug/L, takes a drug and afterwards her myoglobin value is elevated up to 100 (see Figure 26). These mentioned terminologies are available in the CIM.

Figure 26: Myoglobin – lab parameter that indicates the possible presence of an ADE

In that situation, a notification is produced, the physician gets notified and needs to reject or confirm the suspected ADE. For some ADE detection rules, ANT is not able to determine the exact drug triggering the notification. Therefore, the physician should review the notification and include/exclude drugs from the notification before sending it the reporting tool.

5.2.3 ANTGui

As the patient data is checked for the presence of ADEs and indicators of ADEs, suspicious events will be displayed in a GUI component. The GUI component is realized as a web application using HTML5 in combination with jQUERY based on JavaScript, and Bootstrap. Data exchange between the client and server is implemented using RESTful services.

Figure 27 shows the graphical user interface of the ANT. There are two different modes to operate the system: If the offline mode is selected, the ADE analysis has to be initiated manually. In the online mode, incoming data in a patient’s EHR is instantly analyzed as it becomes available. If the analysis process detects a suspected ADE, a notification will “pop up”. This enables real time feedback, for example at the bedside of a patient during a ward round on a mobile device. In the screenshot of Fig. 14, the “notification” tab is selected, listing all open notifications in a table view. The notifications are

26 The normal values are lab specific and currently implemented according to the TUD setting.

Figure 27: ANT Main GUI

FP7-287800 SALUS


supplemented by its calculated probability to ease the doc-tor’s review, indicated by a status bar at the right of a notification entry. By selecting a notification, the table gets minimized, the notification is marked and the details are displayed. The details section summarizes the relevant clinical data. In order to study the patient’s clinical context, all drugs, diagnoses, laboratory results and executed procedures can be chronologically displayed in a configurable timeline visualization. Moreover, all known ADEs that are found by the ANT are visualized in the “known ADE” tab. The ANT and all available detection methods can be completely configured under the “detection rule configuration” tab (see Figure 28).

Figure 28: Detection Rule Configuration

The “statistics” tab contains data on certain measures summarizing all analysis results, for example how many ADEs have been found in the hospital at all. Finally, if the doctor confirms a notification being an unknown ADE, the ADE reporting system of the SALUS project is triggered and the report is prepopulated utilizing the available EHR data through the ICSR Reporting Tool (see Deliverable D5.3.2).

FP7-287800 SALUS


6 CONCLUSION The main goal of this research is to increase ADE reporting rates by developing intelligent data analysis algorithms in order to support the medical experts in the ADE detection process and to present the results back in an appropriate way. Previous studies have shown parameters that can be used for ADE detection. These parameters were reused for the development of the ANT by building several ADE detection rules, but our pilot partners from TUD and LISPA have also designed own detection rules according to their EHR data. The focus of the ANT is put on EHR data to assist the pharmacotherapy regarding ADE detection. For this reason, this work does not claim to reveal exact ADE prevalences – more than half of the drugs are available over-the-counter and are therefore not electronically recorded in the EHR. Although the ANT has not yet been completely evaluated with real patient data, which is planned for the next months, we already proved the feasibility by implementation and extensive experimentation with clinical EHR test data. The structure and format of the data equals that of real clinical data according to our pilot partners. However, in the next months we will evaluate the ANT with real patient data. One further challenge will be the adjustment of the ADE detection rules for real patient data as the amount of implemented ADE detection rules is huge and will most probably cause many false positive alarms. To support the adjustment for end users, ANT also provides configuration options where the analysis time periods can be changed. Further information on the final results will be reported in an update of this deliverable by Month 36.

FP7-287800 SALUS


APPENDIX 1. RDF-MODEL AS RESULT OF SILDS (P1, TUD SCENARIO)

<rdf:RDF xmlns:j.0="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagArten#" xmlns:j.1="http://www.w3.org/2004/02/skos/core#" xmlns:j.2="http://www.agfa.com/w3c/orbis/orbis-schema/Form#" xmlns:j.3="http://www.agfa.com/w3c/orbis/orbis-schema/CwMedpackung#" xmlns:j.4="uri:iso.org:9834#" xmlns:j.5="http://www.salusproject.eu/ontology/common-information-model#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:j.6="http://www.agfa.com/w3c/orbis/orbis-schema/CwMedgliederung#" xmlns:j.7="http://www.agfa.com/w3c/orbis/orbis-schema/Listfield#" xmlns:j.8="http://www.agfa.com/w3c/orbis/orbis-schema/CwMedpraepglied#" xmlns:j.9="http://www.agfa.com/w3c/orbis/orbis-schema/Patient#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:j.10="http://www.agfa.com/w3c/orbis/orbis-schema/Natperson#" xmlns:j.12="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagnosen#" xmlns:j.11="http://www.agfa.com/w3c/orbis/orbis-schema/Diagnosetyp#" xmlns:j.13="http://www.agfa.com/w3c/orbis/orbis-schema/Fall#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:j.14="http://www.agfa.com/w3c/orbis/orbis-schema/Diagnosen#" > <rdf:Description rdf:nodeID="A0"> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-08-31T11:12:12.000000Z</j.5:low> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/WHOATC"> <j.4:oid>2.16.840.1.113883.6.73</j.4:oid> <rdfs:label>WHOATC</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagArten/564800029#this"> <j.0:feststdatum rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-01T00:00:00+01:00</j.0:feststdatum> <j.0:fallDiagnosenid rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800029#this"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2012"> <j.4:oid>1.2.276.0.76.5.409</j.4:oid> <rdfs:label>icd10gm2012</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450009#this"> <j.14:diagkurz>R58</j.14:diagkurz> <j.14:diagtypid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosetyp/29#this"/> <j.14:diagbez>Blutung, anderenorts nicht klassifiziert</j.14:diagbez> <j.14:codesgbv>R58</j.14:codesgbv> <j.5:code>R58</j.5:code> <j.5:displayName>Blutung, anderenorts nicht klassifiziert</j.5:displayName> <j.5:codeSystem>1.2.276.0.76.5.409</j.5:codeSystem> <j.5:codeSystemName>icd10gm2012</j.5:codeSystemName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2009"> <j.4:oid>1.2.276.0.76.5.356</j.4:oid> <rdfs:label>I10-2009</rdfs:label> <rdfs:label>icd10gm2009</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description>

FP7-287800 SALUS


<rdf:Description rdf:nodeID="A1"> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-01T00:00:00+01:00</j.5:low> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135803#this"> <j.6:bezeichnung>Losartan</j.6:bezeichnung> <j.6:code>C09CA01</j.6:code> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Listfield/3945010601+69930+14578+1#this"> <j.5:medicationInformation rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309602#this"/> <j.5:indicateMedicationStartStop rdf:nodeID="A0"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Medication"/> <j.7:belongTo rdf:resource="https://agfa.com/orbis/SALUS/resource/Form/3945010601#this"/> <j.7:hasName>PZN</j.7:hasName> <j.7:hasListName>ListeMedikamente</j.7:hasListName> <j.7:hasFeldtext>03629017</j.7:hasFeldtext> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Fall/438650001#this"> <j.13:persnr rdf:resource="https://agfa.com/orbis/SALUS/resource/Patient/883001#this"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309601#this"> <j.5:codedActiveIngredient rdf:nodeID="A2"/> <j.5:freeTextBrandName>Nifedipin-ratio Tropfen 100 ml N3</j.5:freeTextBrandName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#MedicationInformation"/> <j.3:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492601#this"/> <j.3:pzn>3552295</j.3:pzn> <j.3:bezeichnung>Nifedipin-ratio Tropfen 100 ml N3</j.3:bezeichnung> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450028#this"> <j.14:diagkurz>I10</j.14:diagkurz> <j.14:diagtypid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosetyp/29#this"/> <j.14:diagbez>Essentielle (primäre) Hypertonie</j.14:diagbez> <j.14:codesgbv>I10.-</j.14:codesgbv> <j.5:code>I10.-</j.5:code> <j.5:displayName>Essentielle (primäre) Hypertonie</j.5:displayName> <j.5:codeSystem>1.2.276.0.76.5.409</j.5:codeSystem> <j.5:codeSystemName>icd10gm2012</j.5:codeSystemName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2005"> <j.4:oid>1.2.276.0.76.5.304</j.4:oid> <rdfs:label>I10-2005</rdfs:label> <rdfs:label>icd10gm2005</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Natperson/883001#this"> <j.10:gebdat rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">1954-01-18T00:00:00+01:00</j.10:gebdat> <j.10:geschlecht>M</j.10:geschlecht> </rdf:Description>

FP7-287800 SALUS


<rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800001#this"> <rdf:type rdf:resource="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagnosen#FallDiagnosen"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Condition"/> <j.12:diagnoseid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450001#this"/> <j.12:fallid rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/43865001#this"/> <j.5:problemCode rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450001#this"/> <j.5:problemDate rdf:nodeID="A3"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450022#this"> <j.14:diagkurz>E66</j.14:diagkurz> <j.14:diagtypid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosetyp/29#this"/> <j.14:diagbez>Adipositas</j.14:diagbez> <j.14:codesgbv>E66.-</j.14:codesgbv> <j.5:code>E66.-</j.5:code> <j.5:displayName>Adipositas</j.5:displayName> <j.5:codeSystem>1.2.276.0.76.5.409</j.5:codeSystem> <j.5:codeSystemName>icd10gm2012</j.5:codeSystemName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:nodeID="A2"> <j.5:code>C08CA05</j.5:code> <j.5:codeSystem>2.16.840.1.113883.6.73</j.5:codeSystem> <j.5:codeSystemName>WC</j.5:codeSystemName> <j.5:displayName>Nifedipin</j.5:displayName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2011"> <j.4:oid>1.2.276.0.76.5.388</j.4:oid> <rdfs:label>icd10gm2011</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Form/3945010601#this"> <j.2:meddate rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2012-08-31+01:00</j.2:meddate> <j.2:fall rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/43865001#this"/> <j.2:hasName>Rezept neu</j.2:hasName> <j.2:medtime rdf:datatype="http://www.w3.org/2001/XMLSchema#time">12:12:12</j.2:medtime> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Fall/43865001#this"> <j.13:persnr rdf:resource="https://agfa.com/orbis/SALUS/resource/Patient/883001#this"/> </rdf:Description> <rdf:Description rdf:nodeID="A4"> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-01T11:12:12.000000Z</j.5:low> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135801#this"> <j.6:bezeichnung>Nifedipin</j.6:bezeichnung> <j.6:code>C08CA05</j.6:code> </rdf:Description>

FP7-287800 SALUS


<rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Form/3945010101#this"> <j.2:meddate rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2012-06-01+01:00</j.2:meddate> <j.2:fall rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/43865001#this"/> <j.2:hasName>Rezept neu</j.2:hasName> <j.2:medtime rdf:datatype="http://www.w3.org/2001/XMLSchema#time">12:12:12</j.2:medtime> </rdf:Description> <rdf:Description rdf:nodeID="A3"> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-08-30T00:00:00+01:00</j.5:low> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800029#this"> <rdf:type rdf:resource="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagnosen#FallDiagnosen"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Condition"/> <j.12:diagnoseid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450022#this"/> <j.12:fallid rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/438650001#this"/> <j.5:problemCode rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450022#this"/> <j.5:problemDate rdf:nodeID="A1"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Diagnosetyp/29#this"> <j.11:hl7kurz>icd10gm2012</j.11:hl7kurz> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2006"> <j.4:oid>1.2.276.0.76.5.311</j.4:oid> <rdfs:label>I10-2006</rdfs:label> <rdfs:label>icd10gm2006</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Patient/883001#this"> <j.5:medication rdf:resource="https://agfa.com/orbis/SALUS/resource/Listfield/39450101+69930+14578+1#this"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Patient"/> <j.5:gender rdf:nodeID="A5"/> <j.9:persnr rdf:resource="https://agfa.com/orbis/SALUS/resource/Natperson/883001#this"/> <j.5:condition rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800029#this"/> <j.5:dateOfBirth rdf:datatype="http://www.w3.org/2001/XMLSchema#date">1954-01-18+01:00</j.5:dateOfBirth> <j.5:condition rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800001#this"/> <j.5:medication rdf:resource="https://agfa.com/orbis/SALUS/resource/Listfield/3945010601+69930+14578+1#this"/> <j.5:condition rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800035#this"/> <j.5:medication rdf:resource="https://agfa.com/orbis/SALUS/resource/Listfield/3945010101+69930+14578+1#this"/> <j.5:condition rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800012#this"/> </rdf:Description>

FP7-287800 SALUS


<rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450001#this"> <j.14:diagkurz>I21</j.14:diagkurz> <j.14:diagtypid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosetyp/29#this"/> <j.14:diagbez>Akuter Myokardinfarkt</j.14:diagbez> <j.14:codesgbv>I21.-</j.14:codesgbv> <j.5:code>I21.-</j.5:code> <j.5:displayName>Akuter Myokardinfarkt</j.5:displayName> <j.5:codeSystem>1.2.276.0.76.5.409</j.5:codeSystem> <j.5:codeSystemName>icd10gm2012</j.5:codeSystemName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:nodeID="A6"> <j.5:code>C09CA01</j.5:code> <j.5:codeSystem>2.16.840.1.113883.6.73</j.5:codeSystem> <j.5:codeSystemName>WC</j.5:codeSystemName> <j.5:displayName>Losartan</j.5:displayName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:nodeID="A7"> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-01T00:00:00+01:00</j.5:low> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Form/39450101#this"> <j.2:meddate rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2012-07-23+01:00</j.2:meddate> <j.2:fall rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/43865001#this"/> <j.2:hasName>Rezept neu</j.2:hasName> <j.2:medtime rdf:datatype="http://www.w3.org/2001/XMLSchema#time">12:12:12</j.2:medtime> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800035#this"> <rdf:type rdf:resource="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagnosen#FallDiagnosen"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Condition"/> <j.12:diagnoseid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450028#this"/> <j.12:fallid rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/438650001#this"/> <j.5:problemCode rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450028#this"/> <j.5:problemDate rdf:nodeID="A7"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagArten/564800035#this"> <j.0:feststdatum rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-01T00:00:00+01:00</j.0:feststdatum> <j.0:fallDiagnosenid rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800035#this"/> </rdf:Description> <rdf:Description rdf:nodeID="A8"> <j.5:code>B01AC06</j.5:code> <j.5:codeSystem>2.16.840.1.113883.6.73</j.5:codeSystem> <j.5:codeSystemName>WC</j.5:codeSystemName> <j.5:displayName>Acetylsalicylsäure</j.5:displayName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2010">

FP7-287800 SALUS


<j.4:oid>1.2.276.0.76.5.384</j.4:oid> <rdfs:label>icd10gm2010</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2007"> <j.4:oid>1.2.276.0.76.5.318</j.4:oid> <rdfs:label>I10-2007</rdfs:label> <rdfs:label>icd10gm2007</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2013"> <j.4:oid>1.2.276.0.76.5.413</j.4:oid> <rdfs:label>icd10gm2013</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Listfield/39450101+69930+14578+1#this"> <j.5:medicationInformation rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309601#this"/> <j.5:indicateMedicationStartStop rdf:nodeID="A9"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Medication"/> <j.7:belongTo rdf:resource="https://agfa.com/orbis/SALUS/resource/Form/39450101#this"/> <j.7:hasName>PZN</j.7:hasName> <j.7:hasListName>ListeMedikamente</j.7:hasListName> <j.7:hasFeldtext>3552295</j.7:hasFeldtext> </rdf:Description> <rdf:Description rdf:nodeID="A9"> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-07-23T11:12:12.000000Z</j.5:low> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> </rdf:Description> <rdf:Description rdf:about="file:///opt/aca/argus/config/rules/salus/tud-salus-diagnosis-conversion-rules.n3"> <rdfs:comment xml:lang="en">Copyright © 2012 Agfa-Gevaert Group. All Rights Reserved. THIS IS UNPUBLISHED PROPRIETARY SOURCE CODE OF Agfa-Gevaert Group. The copyright notice above does not evidence any actual or intended publication of such source code.</rdfs:comment> <dc:description xml:lang="en">Converts TUD diagnose data to the SALUS CIM ontology.</dc:description> <dc:publisher xml:lang="en">Agfa Healthcare/Belgium</dc:publisher> <dc:creator xml:lang="en">Giovanni Mels, MSc; Agfa Healthcare/Belgium</dc:creator> <dc:title xml:lang="en">TUD SALUS conversion rules</dc:title> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpraepglied/11276901#this"> <j.8:medgliederungid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135801#this"/> <j.8:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492601#this"/> </rdf:Description> <rdf:Description rdf:nodeID="A10"> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#ivlTs"/> <j.5:low rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-08-30T00:00:00+01:00</j.5:low> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpraepglied/11276903#this"> <j.8:medgliederungid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135803#this"/>

FP7-287800 SALUS


<j.8:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492603#this"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2008"> <j.4:oid>1.2.276.0.76.5.330</j.4:oid> <rdfs:label>I10-2008</rdfs:label> <rdfs:label>icd10gm2008</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135802#this"> <j.6:bezeichnung>Acetylsalicylsäure</j.6:bezeichnung> <j.6:code>B01AC06</j.6:code> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/Listfield/3945010101+69930+14578+1#this"> <j.5:medicationInformation rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309603#this"/> <j.5:indicateMedicationStartStop rdf:nodeID="A4"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Medication"/> <j.7:belongTo rdf:resource="https://agfa.com/orbis/SALUS/resource/Form/3945010101#this"/> <j.7:hasName>PZN</j.7:hasName> <j.7:hasListName>ListeMedikamente</j.7:hasListName> <j.7:hasFeldtext>05463118</j.7:hasFeldtext> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpraepglied/11276902#this"> <j.8:medgliederungid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedgliederung/11135802#this"/> <j.8:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492602#this"/> </rdf:Description> <rdf:Description rdf:nodeID="A5"> <j.5:displayName>Male</j.5:displayName> <j.5:codeSystemName>AdministrativeGender</j.5:codeSystemName> <j.5:codeSystem>2.16.840.1.113883.5.1</j.5:codeSystem> <j.5:code>M</j.5:code> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#cd"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/terminology/ICD10GM/2004"> <j.4:oid>1.2.276.0.76.5.302</j.4:oid> <rdfs:label>I10-2004</rdfs:label> <rdfs:label>icd10gm2004</rdfs:label> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#ConceptScheme"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagArten/564800001#this"> <j.0:feststdatum rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-08-30T00:00:00+01:00</j.0:feststdatum> <j.0:fallDiagnosenid rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800001#this"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309602#this"> <j.5:codedActiveIngredient rdf:nodeID="A8"/> <j.5:freeTextBrandName>Aspirin protect 100mg Emra 90 Tbl.</j.5:freeTextBrandName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#MedicationInformation"/> <j.3:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492602#this"/> <j.3:pzn>03629017</j.3:pzn> <j.3:bezeichnung>Aspirin protect 100mg Emra 90 Tbl.</j.3:bezeichnung>

FP7-287800 SALUS


</rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800012#this"> <rdf:type rdf:resource="http://www.agfa.com/w3c/orbis/orbis-schema/FallDiagnosen#FallDiagnosen"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#Condition"/> <j.12:diagnoseid rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450009#this"/> <j.12:fallid rdf:resource="https://agfa.com/orbis/SALUS/resource/Fall/438650001#this"/> <j.5:problemCode rdf:resource="https://agfa.com/orbis/SALUS/resource/Diagnosen/726450009#this"/> <j.5:problemDate rdf:nodeID="A10"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/FallDiagArten/564800012#this"> <j.0:feststdatum rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-08-30T00:00:00+01:00</j.0:feststdatum> <j.0:fallDiagnosenid rdf:resource="https://agfa.com/orbis/SALUS/resource/FallDiagnosen/564800012#this"/> </rdf:Description> <rdf:Description rdf:about="https://agfa.com/orbis/SALUS/resource/CwMedpackung/11309603#this"> <j.5:codedActiveIngredient rdf:nodeID="A6"/> <j.5:freeTextBrandName>Losartan-ratiopharm® 25mg 98 Filmtbl. N3</j.5:freeTextBrandName> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#MedicationInformation"/> <j.3:medpraeparatid rdf:resource="https://agfa.com/orbis/SALUS/resource/CwMedpraeparat/11492603#this"/> <j.3:pzn>05463118</j.3:pzn> <j.3:bezeichnung>Losartan-ratiopharm® 25mg 98 Filmtbl. N3</j.3:bezeichnung> </rdf:Description> </rdf:RDF>

FP7-287800 SALUS


APPENDIX 2. RDF-MODEL AS RESULT OF SILDS (P2, LISPA SCENARIO)

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:j.0="http://purl.bioontology.org/ontology/SNOMEDCT/103693007" xmlns:j.1="http://aca.agfa.net/resource/ontology/common#" xmlns:j.2="http://purl.bioontology.org/ontology/SNOMEDCT/410942007" xmlns:j.3="http://purl.bioontology.org/ontology/SNOMEDCT/129019007" xmlns:j.4="http://purl.bioontology.org/ontology/SNOMEDCT/" xmlns:j.5="http://schema.org/" xmlns:j.6="http://purl.bioontology.org/ontology/ATC/" xmlns:j.7="http://www.salusproject.eu/ontology/common-information-model#" xmlns:j.8="http://purl.bioontology.org/ontology/SNOMEDCT/105904009" xmlns:j.9="http://purl.bioontology.org/ontology/SNOMEDCT/16076005" xmlns:j.10="http://aca.agfa.net/resource/ontology/healthcare#" xmlns:j.11="http://purl.bioontology.org/ontology/SNOMEDCT/71388002" xmlns:j.12="http://purl.bioontology.org/ontology/ICD9CM/599.7" xmlns:j.13="http://purl.bioontology.org/ontology/SNOMEDCT/308335008" xmlns:j.14="http://purl.bioontology.org/ontology/SNOMEDCT/52530000" xmlns:j.15="http://www.w3.org/ns/prov#" xmlns:j.16="http://purl.bioontology.org/ontology/SNOMEDCT/439401001" xmlns:j.17="http://www.w3.org/2004/02/skos/core#" xmlns:j.18="http://purl.bioontology.org/ontology/SNOMEDCT/105590001" xmlns:j.19="http://purl.bioontology.org/ontology/SNOMEDCT/116154003" xmlns:j.20="http://purl.bioontology.org/ontology/SNOMEDCT/64572001" xmlns:j.21="http://purl.bioontology.org/ontology/SNOMEDCT/53923005" > <rdf:Description rdf:about="http://purl.bioontology.org/ontology/SNOMEDCT/IMP"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">IMP</j.17:notation> <j.17:inScheme rdf:datatype="urn:hl7-org:v3#uidDatatype">2.16.840.1.113883.5.4</j.17:inScheme> <j.17:prefLabel>Inpatient</j.17:prefLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#QUPC_TE043200UV"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">QUPC_TE043200UV</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#completed"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">completed</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model/patient#99547035"> <j.5:birthDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">1956-05-27T00:00:00</j.5:birthDate> <j.5:gender>Female</j.5:gender> <j.5:homeLocation rdf:nodeID="A0"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/116154003"/> <j.10:hasProcedure rdf:resource="http://www.salusproject.eu/ontology/common-information-model/medication#1633734799"/> <j.10:hasProcedure rdf:resource="http://www.salusproject.eu/ontology/common-information-model/condition#9_0"/> <j.10:hasEncounter rdf:resource="http://www.salusproject.eu/ontology/common-information-model/encounter_stay#9"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model/condition#9_0"> <j.15:generated rdf:nodeID="A1"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/103693007"/>

FP7-287800 SALUS


<rdf:type rdf:resource="http://schema.org/MedicalProcedure"/> </rdf:Description> <rdf:Description rdf:nodeID="A2"> <rdf:type rdf:resource="http://schema.org/Country"/> </rdf:Description> <rdf:Description rdf:nodeID="A3"> <j.5:addressCountry rdf:nodeID="A2"/> <rdf:type rdf:resource="http://schema.org/PostalAddress"/> </rdf:Description> <rdf:Description rdf:about="http://purl.bioontology.org/ontology/ATC/B01AA03"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">B01AA03</j.17:notation> <j.17:inScheme rdf:datatype="urn:hl7-org:v3#uidDatatype">2.16.840.1.113883.6.73</j.17:inScheme> <j.17:prefLabel>WARFARIN</j.17:prefLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A4"> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/105590001"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/ATC/B01AA03"/> </rdf:Description> <rdf:Description rdf:about="http://purl.bioontology.org/ontology/SNOMEDCT/Condition"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">Condition</j.17:notation> <j.17:inScheme rdf:datatype="urn:hl7-org:v3#uidDatatype">2.16.840.1.113883.6.96</j.17:inScheme> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A5"> <j.10:drug rdf:nodeID="A6"/> <j.10:drugIntaken rdf:nodeID="A7"/> <j.5:doseSchedule rdf:nodeID="A8"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/105904009"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/ATC/B01AA03"/> </rdf:Description> <rdf:Description rdf:nodeID="A8"> <j.5:doseUnit>mg</j.5:doseUnit> <j.5:doseValue>0.44117647</j.5:doseValue> <j.5:frequency>7.5 d</j.5:frequency> <j.1:total rdf:nodeID="A9"/> <rdf:type rdf:resource="http://schema.org/DoseSchedule"/> </rdf:Description> <rdf:Description rdf:nodeID="A0"> <j.5:address rdf:nodeID="A3"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#OK"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">OK</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A1"> <j.5:name>Hematuria</j.5:name> <j.5:description>EMATURIA</j.5:description> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/439401001"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/64572001"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/ICD9CM/599.7"/> </rdf:Description> <rdf:Description rdf:nodeID="A10"> <j.5:name>Hematuria</j.5:name> <j.5:description>EMATURIA</j.5:description> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/439401001"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/64572001"/>

FP7-287800 SALUS


<rdf:type rdf:resource="http://purl.bioontology.org/ontology/ICD9CM/599.7"/> </rdf:Description> <rdf:Description rdf:nodeID="A11"> <j.5:name>SomeContry</j.5:name> <rdf:type rdf:resource="http://schema.org/Country"/> </rdf:Description> <rdf:Description rdf:nodeID="A9"> <j.5:unitCode>mg</j.5:unitCode> <j.5:value>0.44117647</j.5:value> <rdf:type rdf:resource="http://schema.org/QuantitativeValue"/> </rdf:Description> <rdf:Description rdf:about="http://purl.bioontology.org/ontology/HL7/AdministrativeGender/F"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">F</j.17:notation> <j.17:inScheme rdf:datatype="urn:hl7-org:v3#uidDatatype">2.16.840.1.113883.5.1</j.17:inScheme> <j.17:prefLabel>Female</j.17:prefLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#active"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">active</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A6"> <j.5:nonProprietaryName rdf:datatype="urn:hl7-org:v3#stDatatype">WARFARIN</j.5:nonProprietaryName> <j.5:activeIngredient rdf:nodeID="A4"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/410942007"/> </rdf:Description> <rdf:Description rdf:nodeID="A7"> <j.5:startDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-11-05T00:00:00</j.5:startDate> <j.5:endDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-11-22T00:00:00</j.5:endDate> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/129019007"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model/encounter_stay#9"> <j.5:startDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-11-12T00:00:00</j.5:startDate> <j.10:indication rdf:nodeID="A10"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/308335008"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/71388002"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/53923005"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/IMP"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#normal"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">normal</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:about="http://purl.bioontology.org/ontology/ICD9CM/599.7"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">599.7</j.17:notation> <j.17:inScheme rdf:datatype="urn:hl7-org:v3#uidDatatype">2.16.840.1.113883.6.2</j.17:inScheme> <j.17:prefLabel>Hematuria</j.17:prefLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A12"> <j.5:unitCode>d</j.5:unitCode> <j.5:value>7.5</j.5:value> <j.1:value>7.5</j.1:value>

FP7-287800 SALUS


<j.1:unit>d</j.1:unit> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model/medication#1633734799"> <j.5:startDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-11-05T00:00:00</j.5:startDate> <j.5:endDate rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-11-22T00:00:00</j.5:endDate> <j.10:drugPreparation rdf:nodeID="A5"/> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/16076005"/> </rdf:Description> <rdf:Description rdf:about="http://www.salusproject.eu/ontology/common-information-model#deliveredResponse"> <j.17:notation rdf:datatype="urn:hl7-org:v3#csDatatype">deliveredResponse</j.17:notation> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/> </rdf:Description> <rdf:Description rdf:nodeID="A13"> <j.7:patient rdf:resource="http://www.salusproject.eu/ontology/common-information-model/patient#99547035"/> </rdf:Description> <rdf:Description rdf:nodeID="A14"> <rdf:type rdf:resource="http://purl.bioontology.org/ontology/SNOMEDCT/52530000"/> <rdf:type rdf:resource="http://www.salusproject.eu/ontology/common-information-model#QUPC_TE043200UV"/> </rdf:Description> <rdf:Description rdf:nodeID="A15"> <j.5:streetAddress>SomeStreet</j.5:streetAddress> <j.5:postalCode>12345</j.5:postalCode> <j.5:addressLocality>SomeCity</j.5:addressLocality> <j.5:addressCountry rdf:nodeID="A11"/> <rdf:type rdf:resource="http://schema.org/PostalAddress"/> </rdf:Description> </rdf:RDF>

FP7-287800 SALUS


APPENDIX 3. ANT CONTENT FORMAT

D6.1.2-Toolsets for Enabling ADE Detection on EHRs based on … · 2017-04-17 · FP7-287800 SALUS...

Documents

Transcript of D6.1.2-Toolsets for Enabling ADE Detection on EHRs based on … · 2017-04-17 · FP7-287800 SALUS...