Post on 11-Jul-2015
Jumping the crevasse between assertions of drug interactions and
clinical relevanceDaniel C. Malone, RPh, PhD
Professor The University of Arizona
You wouldn’t believe how many BIG BAD drug‐drug interactionsthere are. Just ask your doctor about all the DDI alerts she gets!
The Problem!
Prescriber’s KnowledgeComputer Screening
Pharmacist’s Knowledge
Latent Failures
Patient Risk Factors
Patient Education
Monitoring
ADR
A + B“When the Holes Line Up”
Defenses
Hansten PD, Horn JR. Modified from: James Reason, Human Error, 1990
Drug Administration
Market Removals Due to Drug‐Drug Interactions
• Terfenadine (Seldane®) – 1998• Mibefradil (Posicor®)‐ 1998• Astemizole (Hismanal®) – 1999• Cisapride (Propulsid®) – 2000• Cerivastatin (Baycol®) – 2001
5
VA practitioner knowledge of drug-drug interactions
• 168 responses to a postal survey• 135 physicians• 22 nurse practitioners• 11 physician assistants
• Clinicians correctly categorized 53% of drug-drug interactions
• But, only• 64% correctly answered sildenafil-isosorbide (28% not sure)• 58% correctly answered cisapride-erythromycin (27% not
sure)• 43% correctly answered phenelzine-sertraline (46% not sure)
Source: Glassman PA et al. Medical Care 2002; 40:1161-1171
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National Survey of Prescribers’Knowledge of DDIs - Methods
• Postal survey of prescribers (12,500)• Sample: Identified via pharmacy claims to a pharmacy benefit
manager• Cases – history of 1 or more DDI’s• Controls – match on prescribing either objective or precipitant
medication• Practice characteristics• Respondents asked to classify 14 drug pairs
• Contraindicated• May be used together but with monitoring• No interaction• Not sure
• Usual source of drug-drug interaction information
Ko et al. Drug Saf. 2008;31(6):525‐536.
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Summary of Prescriber DDI Knowledge
• Correct classification of drug-pairs• Mean (SD) = 6.0 (3.1)• Overall – 42.7% of drug pairs correctly
identified• 30% or more of respondents answered “unsure”
for 8 of the 16 the drug pairs• 2 combinations are contraindicated
• Drug‐drug interaction alerts can be useful• Prescriber knowledge of DDIs is lacking1,2
• 42.7% of drug pairs correctly identified1
• VA practitioners generally agree that DDI alerts are useful3
1) Ko et al. Drug Saf. 2008;31(6):525‐536. 2) Glassman. Med Care. 2002;40(12):1161‐1171. 3) Ko et al. JAMIA 2007;14:56‐64
Background
Sensitivity of Computer Software to Detect Drug Interactions in Arizona Pharmacies (N=64)
89%
86%
88%
45%
81%
90%
75%
84%
87%
83%
80%
70%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Carbamazepine + clarithromycin
Digoxin + amiodarone
Digoxin + clarithromycin
Digoxin + itraconazole
Nitroglycerin + sildenafil
Simvastain + itraconazole
Simvastatin + amiodarone
Simvastatin + gemfibrozil
Warfarin + amiodarone
Warfarin + fluconazole
Warfarin + gemfibrozil
Warfarin + naproxen
Warfarin + sulfamethoxazole/trimethoprim
Saverno et al. JAMIA; 2011:18:32-37
DDI Prevalence in Elderly
• Elderly veterans with new DDI at ED discharge:1 13%
• Older adults exposed to a “major” DDI:2 4%
• Medicare Part D enrollees exposed to certain DDIs: 7.3%
1) J Am Geriatr Soc. 2008;56:875‐80. 2) JAMA. 2008;300:2867‐78.
Who Prescribes Drug‐Drug Interactions?
010203040506070
Potential Drug‐Drug Interactions by the Same Prescriber
Concordance Among DDI Compendia
Methods• Review of print versions of compendia for “major” interactions• Inclusion Criteria:
• DDI listed in at least 3 compendia
• Available in US for human use
• Medications likely to be dispensed in community pharmacy
• Medications likely captured in electronic database
• Interacting medications not used for therapeutic benefit
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Rating Systems & Selection Criteria
• Evaluation of Drug Interactions• Uses 4-item summary measure based on:
• Potential harm to the patient• Frequency and predictability of occurrence• Degree and quality of documentation
• Code 1: highly clinically significant• Code 2: moderately clinically significant• Code 3: minimally clinically significant• Code 4: not clinically significant
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Rating Systems & Selection Criteria
• Drug Interaction Facts• Uses 5-item summary measure based on:
• Severity (i.e., major, moderate, minor)• Documentation (i.e., established, probable,
suspected, possible, unlikely)• 1: major/established, probable, suspected• 2: moderate/established, probable, suspected• 3: minor/established, probable, suspected• 4: major,moderate/possible• 5: minor/possible or any/unlikely
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Rating Systems & Selection Criteria
• Drug Interactions: Analysis and Management• Used 5-item summary measure based on:
• Severity• Corresponding documentation• Availability of alternatives are considered
• 1: Avoid combination• 2: Usually avoid combination• 3: Minimize risk• 4: No action needed• 5: No interaction
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Rating Systems• Drug-REAX (MicroMedex)
• Used 5-item severity scale• Major• Moderate• Minor• None• Not specified
• No summary measure
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Results• 62 ‘major’ DDIs identified• Additional criteria:
• 18 ‘major’ DDIs excluded :• 8 DDIs - not available in U.S. (e.g., terfenadine, mibefradil)• 4 DDIs – not dispensed from a community pharmacy• 4 DDIs – not likely to be captured in electronic database (e.g,
ethanol, tyramine-containing foods)• 1 DDI – occurs upon discontinuation (clonidine-β blockers)• 1 DDI – used for therapeutic benefit (phenothiazine-SSRI)
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
DDIs in 4 of 4: 2.2% (9/406) DDIs in 3 of 4: 8.6% (35/406) DDIs in 2 of 4: 17.4% (71/406) DDIs in 1 of 4: 71.7% (291/406)
Intra-class Correlation Coefficient: -0.092
Agreement Among Four Drug Interaction Compendia
Abarca J et al. J Am Pharmacist Assn 2003: 44:136-141.
Clinical Pharmacology and Therapeutics 2011: 87:48‐51
“Only 13.6% of “critical interactions” listed in both compendia
Quantity and Quality of DDI Evidence – Interactions with Macrolides
Harper, Jackson, and Malone – unpublished data
Reasons for Differences in Drug‐Drug Interaction Compendia
• Rating systems are different• Lack of high‐quality studies
• Case reports• Small pharmacokinetic studies
• Rating is subjective• Different editors/contributors
• Few “tools” for evaluating “poor quality” evidence
Hierarchy of Evidence
Systematic Reviews
RCTs
Controlled Clinical Trials and Observational Studies
Uncontrolled Observational Studies
Case reports and case series
Expert Opinions
Lowest risk of bias
Hypothesis Testing
Hypothesis Generating
Theophylline – Allopurinol:A Major Drug Interaction?
Theophylline
3-Methylxanthine
1-methylurinc acid
1,3 dimethyluric acid
1-methylxanthine35%1A2 3A, 2E140%
16%1A2
Xanthine Oxidase
Theophylline – Allopurinol:A Major Drug Interaction?
• Listed as major in several CDS databases• Two studies found no change in
theophylline PK with allopurinol 300 mg daily x 7 days.1
• One study found ~25% increase in AUC and half-life after 2 weeks of concurrent allopurinol 300 mg BID.2
1. Vozeh S et al. CPT. 1980;27:194‐7. 2. Manfredi RL et al. CPT 1981;29:224‐9
Theophylline Label
Drug Type of Interaction Effect
Alcohol A single large dose of alcohol (3 mL/kg of whiskey) decreases theophylline clearance for up to 24 hours.
30% increase
Allopurinol Decreases theophylline clearance at allopurinol doses ≥600 mg/day.
25% increase
Table II. Clinically significant drug interactions with theophylline*.
Source: http://dailymed.nlm.nih.gov/dailymed
Azithromycin Product Label
• “The following drug interactions have not been reported in clinical trials with azithromycin; however, no specific drug interaction studies have been performed to evaluate potential drug‐drug interaction. Nonetheless, they have been observed with macrolide products. Until further data are developed regarding drug interactions when azithromycin and these drugs are used concomitantly, careful monitoring of patients is advised:• Digoxin–elevated digoxin levels.• Ergotamine or dihydroergotamine–acute ergot toxicity
characterized by severe peripheral vasospasm and dysesthesia.• Triazolam–decrease the clearance of triazolam and thus may
increase the pharmacologic effect of triazolam.• Drugs metabolized by the cytochrome P450 system–elevations
of serum carbamazepine, cyclosporine, hexobarbital, and phenytoin levels.”
Source: http://dailymed.nlm.nih.gov
Health Systems Approach to DDIs• Evidence for DDIs is lacking
• Very few well-controlled studies• Lack of concordance among DDI compendia1
• Differing severity rating systems, terminology, methodologies
• Limitations of DDI clinical decision support2-4
• “Alert fatigue”• High rates of alert override
1) Abarca et al. J Am Pharm Assoc (2003). 2004;44(2):136-141. 2) Grizzle et al. Am J Manag Care. 2007;13(10):573-578. 3) Murphy et al. Am J Health Syst Pharm. 2004;61(14):1484-1487. 4) Abarca et al. J Manag Care Pharm. 2006;12(5):383-389.
Problems With DDIs in CDS Systems
• Classification systems that are based on rules of questionable relevance
• Reliance on label without informed review or evaluation
• Assumption of ‘class–based’ interactions
• Conclusion: “the current DDI alert system is broken”1
1Hatton RC, et al. Ann Pharmacother. Mar 2011;45(3):297‐308.
Model for DDI alerting in CPOE
Integrationsoftware
Knowledge‐base (rules)
Patient database (meds, etc.)
Knowledge engineering
Literature, domain expertise
Potential DDI report
MDPharmacistNurse
Too many alerts!
• Organizational alert override rates are high• 96% in Netherlands1
• US medical center2
• 461 different physicians, 18,354 medication orders
• 2,455 alerts• DDI override rate: 95.1%
1van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006 2006 Mar‐Apr;13(2):138‐47. 2Bryant AD, Fletcher GS, Payne TH. Drug interaction override rates in the Meaningful use era: no evidence of progress. Applied Clinical Informatics 2014; 5:802‐813
Case Study ‐ Sentara Healthcare• Amiodarone + warfarin DDI fires 1000 times
• 960 – alert overridden/bypassed• 40 – warfarin dose adjusted • 10 – INR or CBCs ordered• 30 – INR or CBC frequency adjusted• 50 – pt. education or anticoagulation clinic scheduled• 5 – alert overridden, INR > 5
Practitioners’ Views on DDI Alerts
• Methods• Random sample of 100 to 125 practitioners
in outpatient clinics at 6 VAMCs
• Results (N = 258) (Response rate: 36%)• Internal medicine – 31%• Primary care – 29%
• Years in practice (mean) - 14.8 (10.6)• Rx’s written per week – 97.7 (155.5)
Ko et al. JAMIA 2007; 14: 56-6434
Practitioners’ Views of Alerts
Prescribers’ Views on DDI Alerts Mean Response
I am satisfied with the accuracy of DDI alerting system
3.1
DDI alerts provide me with information I already know
3.4
The DDI system provides alerts that seems to be just about exactly what I need
2.7
DDI alerts change my initial prescribing decisions
1.9
1= strongly disagree; 2 = disagree; 3 = neither disagree or agree;4 = agree; 5 = strongly agree
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Attitudes Toward Improving DDI Alerts
Attribute Mean Response
DDI alerts should be accompanied by management alternatives
3.8
DDI alerts should be accompanied by more detailed information about the interaction
3.7
DDI alerts should only appear once in the order entry process 3.6
DDI alerts are presented in a useful format 3.2
1= strongly disagree; 2 = disagree; 3 = neither disagree or agree;4 = agree; 5 = strongly agree
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Top Ranked Changes to VA’s Alerting System
Change Weighted Preference Score
Provide management options for DDIs 1.53
Show DDI alerts one at a time 1.24
Make it more difficult to override lethal interactions
0.95
Eliminate all DDI alerts 0.05
Rank 1 = 3 points, Rank 2 = 2 points, Rank 3 = 1 point37
“Asthma sufferer wins $28.6 million award” –Seattle Time (9/3/94)
• 24 year old man on theophylline went to ER with an infection, prescribed ciprofloxicin by ER physician
• Theophylline levels doubled, patient had permanent brain damage
• Patient awarded $22.5 million in pain and suffering
Tranylcypromine (Parnate®) +Phenylpropanolamine (Tavist‐D®)
• Patient on Parnate went to ER for URI: received Rx for Tavist-D®
• Rx filled at his regular pharmacy (technician ignored computer warning)
• Patient developed an acute hypertensive episode resulting in a stroke
• Patient committed suicide, leaving a note that the disabilities induced by the stroke were intolerable
Williams KG. Am J Health-Syst Pharm 1996;53:1709.
Current “Lists” of Important DDIsSource Purpose Systematic
ReviewExpert Panel
Consensus Process
Classen 2011 / Leapfrog List
Verify whether an inpatient COPE system has the potential to intercept critical DDIs
Unknown Unknown Unknown
CMS FTag 329‐Unecessary Medications
Medications requiring increased involvement from consultant pharmacists.
Unknown Yes Unknown
Malone 2004To develop a list of clinically important DDIs in outpatient setting
Yes; primary literature and
tertiary references
YesModified Delphi
process
Phansalkar 2012 / ONC List
To reduce alert fatigue and describe the most clinically significant high‐priority DDIs
Unclear Yes Mixed approach
Pharmacy Quality Alliance
Evaluate prescription drug plans and ambulatory/community pharmacists
No No Yes
van Roon 2005 / G‐standard
Dutch database for CDS in NetherlandsYes Yes Yes
Rationale for Changing the Approach
• Drug interaction clinical decision support shouldimprove patient safety
• Instead….• Evidence lacking to support effectiveness• Excessive irrelevant alerts• Wide variation across health systems for DDI CDS• Pandemic clinician annoyance
• What’s needed• Guiding principles for evidence‐based, clinically relevant,
consistent alerts with improved usability• Evidence of effectiveness
Rationale for Changing the Approach
• Concerns about process used to generate current “lists”
• No well‐defined, broadly accepted, uniform standard for rating:• Strength (quality) of DDI evidence• Strength of recommendations for patient risk management
• Concerns about liability
• DDI CDS conference series to:• Develop guidelines for systematic appraisal of DDI evidence (Evidence Workgroup)
• Recommend principles for including DDIs in CDS (Content Workgroup)
• Establish preferred strategies for presenting DDI CDS (Usability Workgroup)
• Consensus recommendations by international experts
AHRQ Conference Series
https://sites.google.com/site/ddiconferenceseriessite
Project At A Glance
DDI CDS Conference Website
https://sites.google.com/site/ddiconferenceseriessite/home
Category EvidenceSufficientSufficient evidence to evaluate a clinically relevant drug interaction
One or more of the following:• Well‐designed and executed prospective controlled studies• Well‐designed and executed retrospective controlled studies• Case reports or series demonstrating probable or highly
probable causality of an interaction (DIPS score of 5‐10)*• Reasonable extrapolation on the basis of:
• Studies of drugs with similar pharmacologic properties • Studies with in vitro substrate data• Human genetic polymorphism studies
DRug Interaction eVidence Evaluation(DRIVE) Instrument
* Drug Interaction Probability Scale; Horn et al. Ann Pharmacother 2007;41:674‐80.
DRug Interaction eVidence Evaluation(DRIVE) Instrument
Category EvidenceInsufficient Insufficient evidence to evaluate a clinically relevant drug interaction
One or more of the following, without supporting evidence from the “sufficient” category:• Extrapolation on the basis of studies with in vitro
inhibitor or inducer data• Case reports or series demonstrating only possible or
doubtful causality of an interaction (DIPS score of <5)• Studies of poor design or execution • Hypotheses‐generating research methods• Unsupported data on file or unsupported
recommendations from product manufacturer • Animal data
* Drug Interaction Probability Scale; Horn et al. Ann Pharmacother 2007;41:674‐80.
AHRQ Conference SeriesWorkgroup Recommendations
• Establish national expert consensus panel• Oversight by national organization • Develop and maintain standard set of DDIs for CDS• Employ transparent, systematic, evidence‐driven process
• Grade quality of evidence• Incorporate expert and clinical advice• Provide graded recommendations for risk management• Collect and incorporate user feedback• Ongoing reevaluation and updates
1. Evidence Synthesis & Grading
2. Expert Advice
3. Consensus Graded
Recommendations4. CDS
Implementation
5. Stakeholder Feedback
6. Reevaluation& Updates
National DDI Expert Panel
Oversight by Central Organization
Transparent, Systematic Process for aStandard Set of DDIs
Support
Agency for Healthcare Research and Quality • Grant #1R13HS021826‐01 (PI – Malone)• Grant #1R13HS018307‐01 (PI – Malone)National Library of MedicineGrant #R01LM011838‐01 (PI – Boyce)
Additional Support• Cerner• Epocrates• First Databank• Truven Health Analytics• Wolters Kluwer Health• Elsevier
Acknowledgements• Lisa Hines, PharmD, University of Arizona• Richard T. Scheife, PharmD, FCCP, Tufts University• Darrell R. Abernethy, MD, PhD, Food and Drug Administration• Richard Boyce, PhD, University of Pittsburgh• Clarissa Borst, PharmD, Elsevier• Sophie Chung, PharmD, Epocrates, athenahealth, Inc.• Susan Comes, PharmD, Epocrates, athenahealth, Inc.• John Horn, PharmD, University of Washington• Gretchen Jones, PharmD, (formerly) Epocrates, athenahealth, Inc.• Jeremiah Momper, PharmD, PhD, University of California, San Diego• Alissa Rich, PharmD, MBA, (formerly) Cancer Treatment Centers of America• Stephen J Sklar, PharmD, Wolters Kluwer Health• Christine D Sommer, PharmD, FDB (First Databank, Inc.)• Tricia Lee Wilkins, PharmD, MS, Office of the National Coordinator for Health Information Technology• Michael A Wittie, MPH, Office of the Chief Medical Officer, Office of the National Coordinator for Health
Information Technology• Samantha K Wong, BSPharm, RPh, Cerner
Acknowledgements• Lisa Hines, PharmD, University of Arizona• Hugh Tilson, MD, DrPH, University of North Carolina • David W Bates, MD, Harvard Medical School• Joseph T Hanlon, PharmD, MS, University of Pittsburgh• Philip Hansten, PharmD, University of Washington• Amy L Helwig, MD, MS, Office of the National Coordinator for HIT • Stefanie Higby‐Baker, RPh, MHA, CPHIT, Cerner Multum• Shiew‐Mei Huang, PhD, Food and Drug Administration• David R Hunt, MD, FACS, Office of the National Coordinator for HIT• Marianne le Comte, PharmD, Royal Dutch Association for the Advancement of Pharmacy• Karl Matuszewski, MS, PharmD, FDB (First Databank, Inc.)• Gerald McEvoy, PharmD, ASHP• Anthony Perre, MD, Cancer Treatment Centers of America• Lynn Pezzullo, RPh, CPEHR, Pharmacy Quality Alliance• John Poikonen, PharmD, MedVentive• Kathy Vieson, PharmD, Elsevier Clinical Solutions• David M Weinstein, RPh, PhD, Lexicomp, Wolters Kluwer Health• Michael A Wittie, MPH, Office of the National Coordinator for HIT
Acknowledgements• Thomas H Payne, MD, FACP, University of Washington • Bruce W Chaffee, PharmD, University of Michigan Health System• Raymond C Chan, PharmD, Sentara• Peter A Glassman, MBBS, VA, Greater Los Angeles Healthcare System • Brian Galbreth, PharmD, PeaceHealth Southwest Medical Center• Christian Hartman, PharmD, MBA, FSMSO, Pharmacy OneSource, Wolters Kluwer Health• Seth Hartman, PharmD, Oregon Health & Science University• Joan Kapusnik‐Uner, PharmD, FDB (First Databank, Inc.) • Gilad J Kuperman, MD, PhD, New York‐Presbyterian Hospital• Gordon Mann, RPh, Clinical Informatics, Epic• Shobha Phansalkar, RPh, PhD, Wolters Kluwer Health (formerly with Partners Healthcare System)• Alissa Russ, PhD, Roudebush VA Medical Center • Hugh Ryan, MD, Cerner Corporation• Howard Strasberg, MD, MS, Wolters Kluwer Health• Amanda Sullins, PharmD, Cerner• Vicki Tamis, PharmD, BCPS, PeaceHealth Southwest Medical Center• Heleen van der Sijs, PharmD, PhD, Erasmus Medical Center
Thanks! Questions and Comments?
Answers (Even Better)?!