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
  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.
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Assessing the example DDI

• The DDI
• is reasonable
• could lead to a serious or fatal adverse event
• ...but, we don't 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

• We've 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