Effective communication of drug-drug interaction knowledge

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Effective communication of Drug-drug
interaction (DDI) knowledge
Richard Boyce PhD Postdoctoral Fellow in
Biomedical Informatics University of Pittsburgh
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Objectives
1. Identify core knowledge elements for DDI
decision support and suggest the possibility
of a common model for representing and
sharing DDI knowledge 2. Suggest how further
research on clinical trigger systems could
lead to reduced DDI alert fatigue while improving
patient safety
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Objective 1 Identify core knowledge
elements for DDI decision support and
suggest the possibility of a common model
for representing and sharing DDI knowledge
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Example - A possible observed DDI

• Two case reports reporting on four individuals
  who developed symptoms of myopathy or
  rhabdomyolysis12
• 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.
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Assessing the example DDI

• 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

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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.
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Evidence for/against a DDI

• pre- and post-market studies
• in vitro experiments
• known or theoretical mechanisms
• case reports and case series
• pharmacovigilance

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Risk factors

• patient characteristics X potential adverse event
• patient characteristics X DDI mechanism
• drug characteristics
• route of administration dose timing sequence

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Risk factors depend on evidence
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Incidence

• prevalence of co-prescription
• prevalence of AE
• incidence of AE in exposed and non-exposed

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Incidence and evidence strengthen each-other
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Seriousness of the AE

• Classified by specific clinical outcome
• ...but can any seriousness ranking be generally
  accepted

no effect
death

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Re-assessing the example DDI
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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 of hand-held
electronic prescribing guides for physicians. In
American Society for Clinical Pharmacology and
Therapeutics volume 75. 2004. 2 Thomas
Hazlet Todd A. Lee Phillip Hansten and John R.
Horn. Performance of community pharmacy drug
interaction software. J Am Pharm Assoc
41(2)200-204 2001.
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Agreement on common elements might be possible
...and could form the basis for a sharing DDI
knowledge across resources
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Objective 2 Suggest how further research
on clinical trigger systems
might lead to reduced DDI alert fatigue while
improving patient safety
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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 alerts in
computerized physician order entry. J Am Med
Inform Assoc 13(2)138-147 2006.
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What does the literature suggest

• find a balance between push vs. pull alerts
• tier DDI alerts by severity
• give users the ability to set preferences for
  some types of alerts
• provide value e.g. changing meds or correcting
  the medical record from the alert
• make alert systems more intelligent

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A potential complementary approach
Clinical event monitor - a system that
identifies and flags clinical data indicative of
a potentially risky patient state
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Example UPMC MARS-AiDE
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Example triggers from UPMC MARS-AiDE1
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 the performance
characteristics of signals used by a clinical
event monitor to detect adverse drug reactions in
the nursing home. AMIA Annu Symp Proc pages
278-282 2008.
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DDI-aware clinical event monitoring
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Potential benefits and risks of DDI-aware
clinical event monitoring

• Benefits
• 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
• ...

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Conclusions

• Weve looked briefly at two areas of research
  that aim to make more effective use of DDI
  knowledge in clinical care
• DDI knowledge sharing
• integrating DDI knowledge with clinical event
  monitoring

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Acknowledgements

• Advisors/mentors Steve Handler Ira Kalet Carol
  Collins John Horn Tom Hazlet Joe Hanlon
  Roger Day
• Funding
• 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