the Fine Print Visualizing Medication SideEffects in Complex Multidrug Regimens Jon D Duke MD NLM Medical Informatics Fellow Regenstrief Institute Indiana University The QuARK ID: 377739
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Slide1
Illuminating the Fine Print: Visualizing Medication Side-Effects in Complex Multi-drug Regimens
Jon D. Duke,
MD
NLM Medical
Informatics Fellow
Regenstrief
Institute
Indiana UniversitySlide2
The QuARK Project
Quantitative Adverse
R
eaction KnowledgebaseSlide3
The Tao of QuARK
The Concept
Building the Knowledgebase
Clinical Applications
Testing the Model
Future Directions and Research
QuARK
:
Quantitative
Adverse Reactions KnowledgebaseSlide4
Part I:
The ConceptSlide5
The primary goal of QuARK is to simplify the process of assessing adverse drug reactions in patients taking multiple medications.
QuARK
: What is it good for?
QuARK
:
Quantitative
Adverse Reactions KnowledgebaseSlide6
PolypharmacySlide7
Polypharmacy
Kaufman DW, Kelly JP, Rosenberg L, Anderson TE, Mitchell AA. Recent patterns of medication use in the ambulatory
adult population of the United States: the Slone survey. JAMA 2002;287: 337-44.Slide8
PolypharmacyHas increased significantly over past 20 yearsIncreases risk for adverse drug reactions
Known risk factor for overall morbidity and mortality
Estimated cost $76B annually
Hajjar
ER,
Cafiero
AC, Hanlon JT. Polypharmacy in elderly patients. Am J
Geriatr
Pharmacother
2007;5: 345-51.
2. Nguyen JK,
Fouts
MM,
Kotabe
SE, Lo E. Polypharmacy as a risk factor for adverse drug reactions in geriatric
nursing home residents. Am J
Geriatr
Pharmacother 2006;4: 36-41.3. Tam-McDevitt J. Polypharmacy, Aging, and Cancer: Growing Risks. Oncology 2008;9.
1
2
1
3Slide9
Side-Effect ComplexitySlide10
Number of Drugs
SE Complexity
X
Physician Time
= The ProblemSlide11
Side-Effects InteractionsSlide12
Current SolutionsSlide13
GoalsLook up multiple medications simultaneouslyRapidly get to side-effect of interest
Show the relative strength of association between a drug and its side-effects
Well-integrated into clinical workflowSlide14
Origins of QuARKSlide15
Hmmmmm….
Zocor
Metformin
Norvasc
Lisinoprol
/HCTZ
Azithromycin
Nausea Dizziness Edema Fatigue Cough PalpitationsSlide16
Part II:Building the KnowledgebaseSlide17
Which Medications to Include?By prescribing volumeBy formulary
QuARK
Wishard
Top 500
Clarian Top 500
U.S. Top 300Slide18
Coding the MedicationsRxNormUNI
NDC
Regenstrief
Dictionary
QuARK
RI Dictionary
RxNormSlide19
Sources of Adverse Reaction Data
FDA Label
MedWatch
/ AERS
Clinical Repository (
eg
. RMRS)
Social Networks (
eg
. patientslikeme.com)
QuARK
FDA LabelSlide20
Coding the Side-EffectsMedDRACTCAE
SNOMED-CT
ICD-9
UMLS CUI
QuARK
MedDRA
UMLS CUI
SNOMED-CTSlide21
Which Side-Effects to Include?
Must select a
single
unique
representation of each
medication / side-effect pairSlide22
Which Side-Effect Data to Include?
Which treatment indication?
Which dose?
Which trial duration?
Pre- / Post-marketing data?
QuARK
Most common indication preferred
Aggregate dose
data preferred,
otherwise most
common dose
Larger trials with
longer duration
preferred
If duplicate data:
Post-marketing
data included if
not present in trialsSlide23
Side-Effect Quantification
The
assignment of a
numeric
score
to represent the relative frequency at which a particular medication causes a particular side-effect.Slide24
Types of Frequency DataDrug vs
Placebo
34% of
Neurontin
patients experienced nausea
vs
12% of placebo patientsFrequency RangeBetween 3% and 9% of patients taking Lipitor experienced dizzinessQualitative Frequency Descriptor
Diarrhea occurred
infrequently in patients taking
Lisinopril
Statement of Occurrence
Thrombocytopenia was reported in patients taking
Norvasc
.Slide25
Drug vs Placebo
Optimal data format
Applied “Absolute Risk Reduction” approach (
ie
. treatment incidence – placebo incidence)
ex. Score = 34 - 12 = 22
Database would include both the original raw data in addition to the calculated score
QuARK
Drug
vs
Placebo
Score =
Treatment Incidence
-
Placebo
Incidence
eg
. 34% of
Neurontin
pts experienced nausea
vs
12% of placebo ptsSlide26
Frequency RangeNo placebo data given
Study size and duration not available
Patient population unknown
Conservative score calculation: Score = x+(y-x)/3 = 3+(9-3)/3 = 5
Original data range preserved in database
QuARK
eg
. Between 3% and 9% of Lipitor patients experienced dizziness
Frequency Range
Scoring
Between X% and Y% of patients taking {drug} experienced {effect}
Score =
X+(Y-X)/3Slide27
Qualitative Frequency Descriptor
No placebo or population data
Wide range of terms used (
eg
. rarely, occasionally, often)
Quantitative mappings may be provided (Rarely = “< 1/100”)
Where mappings unavailable, conservative scores assigned based on interpretation of terms (sometimes = occasionally > infrequently)
QuARK
eg
. Diarrhea occurred infrequently in patients taking
Lisinopril
Qualitative
Scoring
Occasionally 0.75
Infrequently 0.5
Rarely 0.3Slide28
Statement of Occurrence
No frequency information
No placebo or population data
Commonly seen with post-marketing reports or class effects
Conservative scoring applied
“Post-Marketing” status noted in database
QuARK
eg
. Thrombocytopenia was reported in patients taking
Norvasc
.
Occurrence
Scoring
Occurs in drug 0.8
Occurs in class 0.7
Occurs more often in placebo 0.1Slide29Slide30
QuARK Part III:ApplicationsSlide31
RxploreInteractive visualization of
QuARK
data
Allows quick retrieval of most common side-effects of complex drug regimens
Highlights potential causal agents in the setting of an adverse drug event
Allows “virtual swapping” of a medication to assess impact on patient’s side-effect profileSlide32Slide33
QuARK & GopherGoal: Allow
QuARK
visualizations to be retrieved directly from Gopher order entry
Created prototype running on Gopher Dev
Auto-populates medication list directly from Gopher patient chartSlide34Slide35Slide36
QuARK
Bubble MapSlide37
Medication Heat Map
Adverse Effects by Organ System
Dizziness 24%
vs
3%
Headache 11%
vs
2%
Insomnia 6%
vs
3%
Highly Affected
Minimally Affected
DiazepamSlide38
QuARK & Clinical RemindersChief Complaint-driven
Trigger Event-drivenSlide39
QuARK & Clinical RemindersChief Complaint-driven
Which of a patient’s medications are associated with the Chief Complaint?
At what frequency?
“
The patient’s complaint of Dizziness has been associated with use of:
Gabapentin (28% vs. 7% Placebo)
Atenolol
(13%
vs
6% Placebo)
Omeprazole
(Less than 1%) ”Slide40
QuARK & Clinical RemindersTrigger Event-driven
Laboratory / EKG change generates reminder
Offers suggestions for possible causal agents
“
Neutropenia
(WBC 1.4 10/22/08) has been
associated with use of:
Valsartan
(1.9%
vs
0.8% placebo)
Amiodarone
(Has been reported)
Lisinopril
(Occurs rarely) ”Slide41
QuARK
Part IV:
Testing the ModelSlide42
Garbage In / Garbage Out?Limitations of the DataAlgorithmic Considerations
Does a Gold Standard exist?
An Approach to ValidationSlide43
Comparison with AERSAdverse Event Reporting SystemCaptures over 400,000 reports a year
Allows for listing of multiple medications
Records Adverse Reaction and Suspected Cause
Subset includes “
Dechallenge
” DataSlide44
QuARK vs AERS
Dechallenge
data from 2008 Q2
Evaluated reports of four common reactions (nausea, edema, insomnia,
hyponatremia
)
Limited to cases where patient was taking at least 5 medicationsCompared the QuARK “suspected drug” with actual reported causeSlide45
% Accuracy
Reported Adverse Reaction
Accuracy of
QuARK
Ranking for AERS Reports Q2 2008
n=31
n=21
n=14
n=25Slide46
Sources of Error<10% missed cases due to algorithm error>90% missed cases due to complete absence of the adverse reaction from the drug label
Delays in drug label updating
Reflects nature of adverse event reporting
Known side-effects often not reported
New drug mandatory reporting predominates AERSSlide47
QuARK
Part V:
Future Directions and ResearchSlide48
Evaluation StudiesLaboratory study of “decision velocity”Survey of User Satisfaction / EfficiencySlide49
Clinical Reminder StudyGenerate
QuARK
-based reminders for laboratory triggers (
eg
. LFT’s)
Intervention group receives reminder noting potential causal agents / frequency data
Compare drug discontinuation rates as well as time between trigger and discontinuationSlide50
Build a Better QuARKAdditional medications
Expansion of AERS-
QuARK
analysis
Optimization of scoring algorithm
Additional visualization methods
Potential use in consumer healthSlide51
SummaryQuARK is a knowledgebase containing quantitative frequency data for adverse drug reactions
Potential applications include:
visualization of side-effect data
simplified lookup of multidrug regimens
clinical reminders targeted at
adverse drug
eventsOpportunities for research collaborationSlide52
Thanks! NLM, Steve Downs, Mike McCoy, Marc
Overhage
, Shaun
Grannis
,
Gunther
Schadow, Siu Hui, Martin Were, Marc
Rosenmann
,
Linas
Simonatis
,
Atif
Zafar
, Paul Dexter, Mike Weiner, Paul Biondich, Burke Mamlin, Anne
Belsito
Pop the QuARK
!