Accepted for/Published in: JMIR Medical Informatics
Date Submitted: May 30, 2020
Date Accepted: Oct 21, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Towards the automated, empirical filtering of drug-drug-interaction alerts in clinical decision support systems: the example of vitamin K antagonists
ABSTRACT
Background:
Drug-drug interactions (DDIs) involving vitamin K antagonists (VKAs) constitute an important cause of in-hospital morbidity and mortality. However, the list of potential DDIs is long; the implementation of all these interactions in a clinical decision support system (CDSS) results in over-alerting and alert fatigue – limiting the benefits provided by the CDSS.
Objective:
To estimate the probability of occurrence of INR changes for each DDI rule, via the reuse of electronic health record (EHRs).
Methods:
An 8-year, exhaustive, population-based, historical cohort study including: French community hospital, a group of Danish community hospitals, and a Bulgarian hospital. The study database included 156,893 stays. After filtering against two criteria (at least one VKA administration and at least one INR laboratory result), the final analysis covered 4047 stays. The Exposure to any of the 149 drugs known to interact with VKA was tracked and analyzed if at least 3 patients were concerned. The main outcomes are VKA potentiation (defined as an INR≥5) and VKA inhibition (defined as an INR≤1.5). Groups were compared using Fisher’s exact test and logistic regression, and the results were expressed as an odds ratio [95% confidence interval]
Results:
The drugs known to interact with VKAs either did not have a statistically significant association (47 drug administrations and 24 discontinuations) or were associated with significant reduction in risk (odds ratio <1 for 18 administrations and 41 discontinuations).
Conclusions:
The probabilities of outcomes obtained were not those expected on the basis of our current body of pharmacological knowledge. The results do not cast doubt on our current pharmacological knowledge per se but do challenge the commonly accepted idea whereby this knowledge alone should be used to define when a DDI alert should be displayed. Real-life probabilities should also be considered during the filtration of DDI alerts by CDSSs, as proposed in SPC-CDSS (statistically prioritized and contextualized CDSS). However, these probabilities may differ from one hospital to another and so should probably be calculated locally.
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