Global MitoPatients Registry:a challenge

6° CONVEGNO NAZIONALE MITOCON

Piero SantantonioMitocon

IMP

Mito-Patients data: a possible path

Universe of Mito-patients data

Anagr.

basicspecific (family, territ.

context, ...)

Clinicalsymptons

rough

detailed

diagnosis

clinical

rough

det.

biochemical

rough

det.

genetics

rough

det.

Bio

bank

rough

det.

therapy

rough

det.

follow up

rough

det.

QoL

rough det.

Social needs

rough det.

Data needed in Clinical Registries

Universe of Mito-patients data

Anagr.

basicspecific (family, territ.

context, ...)

Clinicalsymptons

rough

detailed

diagnosis

clinical

rough

det.

biochemical

rough

det.

genetics

rough

det.

Bio

bank

rough

det.

therapy

rough

det.

follow up

rough

det.

QoL

rough det.

Social needs

rough det.

Use of Mito-Patients data

Lobby

Clinical trials

Pharma needs

Social needs (assoc.)

Fund Raising

Public Health

ClinicalRegistries

Other National/

Global Rare disease

registries

PatientsRegistries

Why Mito-Registries have to share common language and common data

Data from different registries is used for the same purposes (for instance clinical trial or Public Health Politics)

the language must be uniform (symptoms, syndromes, diagnoses and therapies, for instance have to share common and accepted words)

coherence of the data sets could permit a future merging of data for cross analysis (i.e. what kind of QoL do patients with a specific genetic diagnosis have?)

Present situation

ClinicalRegistries

Other National/ Global Rare

disease registries

PatientsRegistries

A possible approach to build a patients registry coherent with other registries1. collect data set from

active Registries (clinical, patients, «Global»,

2. Involve the main actors:o Patients Ass. because of

QoL and Social needs data

o Clinicians because of clinical data

3. find a possible recommended data set for Mito Patients (RED) and Clinical Registries (YELLOW)QoLProj.

ClinicalReg. #1Data Set

ClinicalReg. #2

Data Set

Patient Reg. #1 Data Set

Recomandeddata set for

ClinicalRegistries

Recom. Data Set for

Patients Reg.

Clinical and Patients already Existing DBAnalysis Report

1

Meeting Feb 2015Nation Resp. DB Type driven by N° of patients Biobank Note

Germany T. Klopstock

Clinical/ research

German consortium 1.000

Yes(about 90% of

pts)

Need funds to continue running

UK R. Mc Farland

Clinical/ research MRC/ Newcastle 1.100

Yes(about 50% of

pts)

SW flexible

USA M. Hirano

Clinical/ research Columbia Univ. 500 Yes

USA P. Yeske Patients UMDF500 pts

400 caregiv.100 family

Commercial SW (Genetic Alliance)

USA Amy Holbert Research

RDCRNRare Diseases Clinical

Research Network NAMDC

North American Mitochondria Disease

Consortium

1159 NoStudies are conducted online through the registry

Italy M. Mancuso

Clinical/ research MITOCON 1.300 Not

directly

SW flexible, SW probably used for Clinical Registry in Spain

«the» Excel file

……………….Analysis possible thanks to the contribution of Daniele Orsucci (Univ. Pisa, Mitocon Grant)

DBs analysis Results

Overlap analysis All the items were categorized in

o 14 Main Categories (i.e. «Biochemicals»)

− Each Main Category contains one or more of the 51 Main Items (in the previous example: «biomarkers: blood», etc.)

o Each Main Item is populated by data (Information: 332 overall) almost choosen in a premeditated list of choises (Specific menu content 753 overall bullet points)in the previous example: «piruvate” or “Carnitine”, ets.menu choises could be less than 10 or more than 100.

Some Registries have parts too specific (i.e.: the German has information about each muscle, UK contains the whole Newcastle scale instead of the synthetic result) that were excluded from the analysis

The 14 Categories and the 51 Main ItemsCategories Main Items

N° of inform

.

Menu Choise

sBasic Info      

  Family info 3  Health insurance 1 2  Patients Info 22  Type of relation with

mitochondrial disease 1 4Privacy Details      

Subject Allowed to manage Data 7

Anamnesi        Date of visit/exams 3  Onset 2 3  Patient history 6  Social_History 2

Diagnosis      

  Clinical diagnosis 3 18

  Diagnosis: other Specific disease 12 113

  Diagnosis: Overlap syndrome 1 16

  Molecular Diagnosis: Methods 1

  Molecular Diagnosis: results 2 6

  Newcastle Scale Score 1  SF6D Score 1

Categories Main ItemsN° of inform.

Menu Choise

sClinical course      

Clinical course 6 16Other

Specific        Apparative diagnostics 1 19  Audiometry 8 29  ECHO 7 37  EEG 7 37  EKG 10 55  EMG 6 23  Exercise 9 1  ncs 3 8  Nerve 6 19  NEUROOPHTALMOLOGICA

L FEATURES 7 23  seizure 4 17  sfemg 1 2

Biochemicals  BIOMARKERS: blood 11 20  BIOMARKERS: Liquor 2 7  BIOMARKERS: Urine 3 8  HISTOLOGICAL FINDINGS 3 19  PDH 2 9  RC Complex 7 35  Tissue Samples 1

Biospecimen      Tissue Samples 2 6

The 14 Categories and the 51 Main Items

Categories Main ItemsN° of inform.

Menu Choise

sRadiologicals      

  CT 4 22  dat_scan 1 2  MRI 4 19  MRS 2 26  NEURORADIOLOGICAL

FEATURES 1 18  PET 5 11  SPECT 3 11

Therapies        Pharmacological therapy 1 43  Therapy: Other

treatments 1 5  Therapy: Rehab 1 4  Therapy: Transplant 1 2

QOL Adults    Quality of Life ADULTS 72

QOL Child    Quality of Life CHILD (0-18y) 49

Caregiver      Patient 12 46

Feedback Questionnair

e     

Feedback on questionnaire 1 2

Categories

Main Items

N° of inform

.

Menu Choise

s

Total  51 332 763

DBs analysis Results

Categories Overlap Analysis in Clinical Registries

Not in Clinical

Registries(QoL Q, UMDF,

Mitocon Patients)

Overlap Analysis in Clinical RegistriesMain Items analysis in the 4 main Clinical DB(US,UK, D and I)

0 means: no registry reports these items1 means: at least 1 DB reports these main items2 means: at least 2 DB reports these main itemsAnd so on….

Clinical overlap Main Item % overlap

0 6 12%

1 14 27%

2 6 12%

3 15 29%

4 10 20%

Overlap Analysis in Clinical Registries

Main Items Overlap Analysis in Clinical Registries

Not in Clinical

Registries(QoL Q, UMDF,

Mitocon Patients)

Overlap Analysis in Clinical Registries A big Harmonization effort have to be spent for Specific

menu information

«only» 49% of the «Main Items» compare al least in 3 (29%) or 4 (20%) registries

Maybe each group worked o self-referential (specific interest or knoledge of the specific team)o Specific research programs requirementso Low level of exchange with other research groupso No pubblications about how to build a mito-DBo ….

Solution: Merge different National platforms

Constitute a study group to define what is needed

Define the actual corrispondence between the existing registries

create a possibility to merge the data from the national registries

IMP is doing this for mito-PATIENTS-Driven-Registry

UMDF

General Patients DB Storage

Mitocon

Others…

IMP Patient Registry timeline

months 1 2 3 4 5 6 7 8 9 10 11 12Agreement for IMP PlatformPlatform set up and test

Define PolicyDefine IMP organization for RegistryDissemination/advertisingStart gathering data and assistance

CONCLUSION

Patients DB is an amazing tool for implementing the knoledge of Mito-disease and mito-patients life

Researcher community is recomanded to implement a common DB

IMP registry project has started in 2015 and is going to be completed at the end of 2016

Thanks from my personal staff