Boyce ahrq-ddi-conference-2008

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A talk on methods for representing potential drug-drug interactions (PDDIs) so that they can be more useful for clinical decision support. The talk has three sections: 1) a novel "evidential" approach to evaluating the validity of PDDIs, 2) a discussion of what information is necessary to make a judgement on the clinical relevance of a PDDI exposure, and 3) a proposal to use predictive risk models to actively monitor of the risk of specific adverse events during a PDDI exposure. This talk was given at the AHRQ-sponsored "Generating, Evaluating, and Implementing Evidence for Drug-Drug Interactions in Health Information Technology to Improve Patient Safety: A Multi-Stakeholder Conference"

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Boyce ahrq-ddi-conference-2008

  1. 1. Effective communication of Drug-drug interaction (DDI) knowledge Richard Boyce, PhD Postdoctoral Fellow in Biomedical Informatics University of Pittsburgh Page 1
  2. 2. Objectives1. Present a new approach to representing and making predictions with drug mechanism knowledge2. Identify core knowledge elements for DDI decision support and suggest the possibility of a common model for representing and sharing DDI knowledge3. Suggest how further research on clinical trigger systems could lead to reduced DDI alert fatigue while improving patient safety Page 2
  3. 3. Objective 1: Present a new approach to representing and making predictions with drug mechanism knowledge Page 3
  4. 4. A large number of potential interactions can be inferred based on mechanisms pre-clinical and pre-market post-market in vitro and in vivoFor example, metabolic interactions: Page 4
  5. 5. Three informatics challenges to reasoning with drug mechanisms1 Drug mechanism knowledge is dynamic, sometimes missing, and often uncertain Hypothesis: A computable model of drug-mechanism evidence will enable powerful methods for addressing these issues.[1] Richard Boyce, Carol Collins, John Horn, and Ira Kalet. Modeling Drug MechanismKnowledge Using Evidence and Truth Maintenance. IEEE Transactions on InformationTechnology in Biomedicine, 11(4):386-397, 2007. Page 5
  6. 6. Case study: The Drug Interaction Knowledge Base (DIKB) The DIKB: infers drug-mechanism knowledge from a computable representation of evidence makes interaction and non-interaction predictions with measurable performance characteristics Current focus: DDIs that occur by metabolic inhibition Page 6
  7. 7. The DIKB architecture Page 7
  8. 8. The reasoning systems current DDI prediction model1 Uses facts in the knowledge-base that meet explicit belief criteria Provides a "ball-park" magnitude estimate based on the importance of the affected metabolic pathway to the object drug Predicts enzyme-specific non-interactions enables the suggestion of alternative drugs[1] Richard Boyce. An Evidential Knowledge Representation for Drug-mechanisms andits Application to Drug Safety. PhD thesis, University of Washington, August 2008. Page 8
  9. 9. The DIKB is an evidential systemAll assertions are linked to their evidence for oragainst: (FLUCONAZOLE inhibits CYP2C9) Evidence for Evidence againsthelps assess credibility, draw attention to errors,and establish confidence Page 9
  10. 10. All assumptions are linked to evidenceEnables the system to identify when assumptionsare no longer validHelps identify poor uses of evidence Page 10
  11. 11. All evidence is classified using a study taxonomyOriented toward user confidence assignment36 types over several sub-hierarchiesA DDI clinical trial ...randomized ...non-randomized ...A non-traceable statement ...in drug product labelling ...A metabolic enzyme inhibition experiment ...focusing on CYP450 enzymes ...done in human liver microsomes ... Page 11
  12. 12. Basic quality standards are defined for every evidence type Example: Pharmacokinetic clinical trials adequate duration and magnitude of dosing patient genotype/phenotype is noted if the target enzyme is polymorphic Example: in vitro enzyme metabolism identification human hepatocyte or recombinant CYP450 selective inhibitors and probe substrates Page 12
  13. 13. This DIKBs architecture helps identify evidence use strategiesWhat evidence is sufficient to justify that a drugpossesses certain mechanistic properties in vivo?What evidence-use strategy enables the system tomake the best predictions?How do these predictions compare with the mostrigorous treatment of evidence possible? Page 13
  14. 14. An experiment focusing on statins1 Study overview: Collect evidence on statins and a sub-set of co- prescribed drugs (16 drugs, 19 metabolites)‫‏‬ Make predictions using a “belief criteria strategy” designed by two drug experts Explore computer-generated strategies to find the ones that were “best-performing” Examine the feasibility of novel DDI predictions[1] R. Boyce, C. Collins, J. Horn, and I. Kalet. Computing with evidence part II: Anevidential approach to predicting metabolic drug-drug interactions. J Biomed Inform, 2009. (2009) DOI:10.1016/j.jbi.2009.05.010. Page 14
  15. 15. An experiment focusing on statins1Study overview: (16 drugs, 19 metabolites)‫‏‬ 36,000 strategies[1] R. Boyce, C. Collins, J. Horn, and I. Kalet. Computing with evidence part II: Anevidential approach to predicting metabolic drug-drug interactions. J Biomed Inform, 2009. (2009) DOI:10.1016/j.jbi.2009.05.010. Page 15
  16. 16. Metrics sensitivity, specificity, positive predictive value (PPV)‫‏‬ accuracy = (true pos + true neg) / (all predictions)‫‏‬ kappa Page 16
  17. 17. The validation set drug pairs 586 interactions 41 (7%) non-interactions 7 (1.2%) unknown 538 (91.8%)unknown: a pair for which no interaction or non-interaction can be confirmed Page 17
  18. 18. Test 1 – the DIKBs most rigorous evidence- use strategy possible Sample of the two most critical assertions: D inhibits ENZ : FOR: clinical trials with D and another drug known to selectively inhibit ENZ in vivo AGAINST: metabolic inhibition studies with probe substrates D substrate-of ENZ FOR/AGAINST: drug metabolism studies involving participants with known CYP450 phenotypes or genotypes Page 18
  19. 19. Test 1 – results using the most rigorous criteriaStatistic Valuenumber of predictions 17True positive 14True negative 0novel 3Positive predictive value 1.00Sensitivity 1.00Specificity undefinedCoverage of validation set 14/49 = 29% Page 19
  20. 20. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh in vitro study AND PK authoritative Med Low Page 20
  21. 21. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh authoritative Med authoritative or in vitro Low Page 21
  22. 22. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh authoritative Med Low in vitro study Page 22
  23. 23. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh in vitro study AND PK Med in vitro study Low Page 23
  24. 24. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh Med authoritative or in vitro in vitro study Low Page 24
  25. 25. Test 2 – computer-derived best strategy LOE inhibits substrate-ofHigh Med in vitro study Low in vitro study Page 25
  26. 26. Test 2 – better-performing strategies 8,351 (23%) of 36,000 tested strategies Equal or better sensitivity, positive predictive value, and agreement than the expert-designed strategy 1,152 (3%) performed at the top level Statistic Value Statistic Valuetrue positives 34 false positives 0true negatives 0 false negatives 0 sensitivity 1.00 specificity n/a PPV 1.00 kappa 0.52 Page 26
  27. 27. Test 2 – relaxing belief criteria often improved performance more stringent less stringent Page 27
  28. 28. Test 2 – relaxing belief criteria often improved performance n=~1k n=~17k n=8 n=~18k more stringent less stringent Page 28
  29. 29. Test 2 Novel Predictions 31 novel interaction predictions 13 matched to 15 published case reports 18 other non-refuted interaction predictions Two interaction predictions supported by case reports that were not present in product labelling: Interaction prediction Level Evidencediltiazem – atorvastatin > 2-fold 2 case reports – possiblefluconazole – simvastatin > 2-fold 1 case report – possible Page 29
  30. 30. Conclusions from the pilot experimentThe new approach to representing drug mechanismknowledge can suggest the best use of available evidence for metabolic DDI prediction is less subjective than ranking evidence and selecting the most rigorousThis is interesting because drug mechanism evidence base varies dramatically across marketed drugs Page 30
  31. 31. Objective 2: Identify core knowledge elements for DDI decision support and suggest the possibility of a common model for representing and sharing DDI knowledge Page 31
  32. 32. Example - A possible observed DDI Interaction prediction Level Evidencediltiazem – atorvastatin > 2-fold 2 case reports – possible Two case reports reporting on five individuals who developed symptoms of myopathy or rhabdomyolysis1,2 All cases provide some evidence that an adverse event (AE) was caused by this DDI [1] P. Gladding, H. Pilmore, and C. Edwards. Potentially fatal interaction between diltiazem and statins. Ann Intern Med, 140(8):W31, 2004. [2] J. J. Lewin 3rd, J. M. Nappi, and M. H. Taylor. Rhabdomyolysis with concurrent atorvastatin and diltiazem. Ann Pharmacother, 36(10):1546-1549, 2002. Page 32
  33. 33. Assessing the example DDI Interaction prediction Level Evidencediltiazem – atorvastatin > 2-fold 2 case reports – possible The DDI: is reasonable could lead to a serious or fatal adverse event ...but, we dont know: patient-specific risk factors prevalence of co-prescribing and various outcomes Page 33
  34. 34. Structured DDI assessment A structured assessment scores evidence and potential severity1[1] E. N. van Roon, S. Flikweert, M. le Comte, P. N. Langendijk, W. J. Kwee-Zuiderwijk, P. Smits, and J. R. Brouwers. Clinical relevance of drug-drug interactions :a structured assessment procedure. Drug Saf, 28(12):1131-1139, 2005. Page 34
  35. 35. Evidence for/against a DDIpre- and post-market studiesin vitro experimentsknown or theoretical mechanismscase reports and case seriespharmacovigilance Page 35
  36. 36. Risk factorspatient characteristics X potential adverse eventpatient characteristics X DDI mechanismdrug characteristics route of administration, dose, timing, sequence Page 36
  37. 37. Risk factors depend on evidence Page 37
  38. 38. Incidenceprevalence of co-prescriptionprevalence of AEincidence of AE in exposed and non-exposed Page 38
  39. 39. Incidence and evidence strengthen each- other Page 39
  40. 40. Seriousness of the AEClassified by specific clinical outcome...but, can any seriousness ranking be generallyaccepted? no effect death ? Page 40
  41. 41. Re-assessing the example DDIdrug pair diltiazem – atorvastatinpotential AE rhabdomyolysis other HMG-CoA, inhibitors,risk factors phenotype, ... five possible cases, establishedevidence mechanismprevalance of pair commongeneral incidence of AE rareincidence in exposed ...incidence in non-exposed ... Page 41
  42. 42. Structured assessments vary focus and content methods for ranking severity across compendia, vendors1, and implementations2[1] F.D. Min, B. Smyth, N. Berry, H. Lee, and B.C. Knollmann. Critical evaluation ofhand-held electronic prescribing guides for physicians. In American Society forClinical Pharmacology and Therapeutics, volume 75. 2004.[2] Thomas Hazlet, Todd A. Lee, Phillip Hansten, and John R. Horn. Performance ofcommunity pharmacy drug interaction software. J Am Pharm Assoc, 41(2):200-204,2001. Page 42
  43. 43. Agreement on common elements might be possible drug pair potential AE risk factors evidence prevalance of pair general incidence of AE incidence in exposed incidence in non-exposed ... ...and could form the basis for a sharing DDI knowledge across resources Page 43
  44. 44. Objective 3: Suggest how further research on clinical triggersystems might lead to reduced DDI alert fatigue whileimproving patient safety Page 44
  45. 45. Results from a recent review on medication alerting1 “Adverse events were observed in 2.3%, 2.5%, and 6% of the overridden alerts, respectively, in studies with override rates of 57%, 90%, and 80%.” “The most important reason for overriding was alert fatigue caused by poor signal-to-noise ratio” Only one study looked at error reductions for DDI alerts – the results were statistically non-significant[1] H. van der Sijs, J. Aarts, A. Vulto, and M. Berg. Overriding of drug safety alertsin computerized physician order entry. J Am Med Inform Assoc, 13(2):138-147,2006. Page 45
  46. 46. What does the literature suggest?find a balance between push vs. pull alertstier DDI alerts by severitygive users the ability to set preferences for sometypes of alertsprovide value e.g. changing meds or correcting themedical record from the alertmake alert systems more intelligent Page 46
  47. 47. A potential complementary approach Clinical event monitor - a system that identifies and flags clinical data indicative of a potentially risky patient state Page 47
  48. 48. Example – UPMC MARS-AiDE Page 48
  49. 49. Example triggers from UPMC MARS-AiDE1 signal alerts ADRs PPV Na+ polystyrene given; taking a drug 2 1 0.5 that may cause hyperkalemia Vitamin K given; taking warfarin 4 4 1 Clostridium difficile toxin positive; taking a drug that may cause 2 2 1 pseudomembranous colitis Elevated alanine aminotransferase concentration; taking a drug that may 5 5 1 cause/worsen hepatocellular toxicity[1] S. M. Handler, J. T. Hanlon, S. Perera, M. I. Saul, D. B. Fridsma, S. Visweswaran, S. A.Studenski, Y. F. Roumani, N. G. Castle, D. A. Nace, and M. J. Becich. Assessing theperformance characteristics of signals used by a clinical event monitor to detect adversedrug reactions in the nursing home. AMIA Annu Symp Proc, pages 278-282, 2008. Page 49
  50. 50. DDI-aware clinical event monitoring Page 50
  51. 51. Potential benefits and risks of DDI-aware clinical event monitoringBenefits automatic consideration of patient-specific risk factors a possible safety net for some potential DDI alerts may provide implicit management options (e.g. stop/change interacting drug)‫‏‬Risks potential for additional alert burden is it ethical to not alert prescribers? ... Page 51
  52. 52. Conclusions Weve looked briefly at three areas of research thataim to make more effective use of DDI knowledge inclinical care: representing and using evidence for predicting DDIs DDI knowledge sharing integrating DDI knowledge with clinical event monitoring Page 52
  53. 53. AcknowledgementsThanks to AHRQ for sponsoring this conferenceAdvisors/mentors: Steve Handler, Ira Kalet, Carol Collins, John Horn, Tom Hazlet, Joe Hanlon, Roger DayFunding: University of Pittsburgh Department of Biomedical Informatics NIH grant T15 LM07442 Elmer M. Plein Endowment Research Fund from the UW School of Pharmacy University of Pittsburgh Institute on Aging Page 53
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