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Effective communication of drug-drug interaction knowledge

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A brief discussion on the need for better methods for representing and communicating drug-drug interaction knowledge. An excerpt from a talk given by Richard Boyce in 2009 at the AHRQ-sponsored conference "Generating, Evaluating, and Implementing Evidence for Drug-Drug Interactions in Health Information Technology to Improve Patient Safety: A Multi-Stakeholder Conference" – PowerPoint PPT presentation

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Title: Effective communication of drug-drug interaction knowledge


1
Effective communication of Drug-drug
interaction (DDI) knowledge
Richard Boyce, PhD Postdoctoral Fellow in
Biomedical Informatics University of Pittsburgh
2
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
3
Objective 1 Identify core knowledge
elements for DDI decision support and
suggest the possibility of a common model
for representing and sharing DDI knowledge
4
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.
5
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

6
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.
7
Evidence for/against a DDI
  • pre- and post-market studies
  • in vitro experiments
  • known or theoretical mechanisms
  • case reports and case series
  • pharmacovigilance

8
Risk factors
  • patient characteristics X potential adverse event
  • patient characteristics X DDI mechanism
  • drug characteristics
  • route of administration, dose, timing, sequence

9
Risk factors depend on evidence
10
Incidence
  • prevalence of co-prescription
  • prevalence of AE
  • incidence of AE in exposed and non-exposed

11
Incidence and evidence strengthen each-other
12
Seriousness of the AE
  • Classified by specific clinical outcome
  • ...but, can any seriousness ranking be generally
    accepted?

no effect
death
?
13
Re-assessing the example DDI
14
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.
15
Agreement on common elements might be possible
...and could form the basis for a sharing DDI
knowledge across resources
16
Objective 2 Suggest how further research
on clinical trigger systems
might lead to reduced DDI alert fatigue while
improving patient safety
17
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.
18
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

19
A potential complementary approach
Clinical event monitor - a system that
identifies and flags clinical data indicative of
a potentially risky patient state
20
Example UPMC MARS-AiDE
21
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.
22
DDI-aware clinical event monitoring
23
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?
  • ...

24
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

25
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
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