Towards The Automated, Empirical Filtering of Drug-Drug Interaction Alerts in Clinical Decision Support Systems: Historical Cohort Study 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 international normalized ratio (INR) changes for each DDI rule, via the reuse of electronic health records. Methods An 8-year, exhaustive, population-based, historical cohort study including a 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. Exposure to any of the 145 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 the Fisher exact test and logistic regression, and the results were expressed as an odds ratio (95% confidence limits). Results The drugs known to interact with VKAs either did not have a statistically significant association regarding the outcome (47 drug administrations and 14 discontinuations) or were associated with significant reduction in risk of its occurrence (odds ratio<1 for 18 administrations and 21 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.

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

“…This approach has limited opportunities for improving specificity without compromising sensitivity. [17][18][19][20][21][22][23][24] Recent studies concluded that CDS systems should have greater flexibility to customize DDI alerting especially by adding contextual modulation, also called specificity modulation. [25][26][27] The term contextual modulation comes from neurobiology, being the change in the neurons' responsiveness to a stimulus caused by context.…”

“…Too many irrelevant alerts can lead to alert fatigue, 9,13–15 described as a “mental state being the result of too many irrelevant alerts consuming time and mental energy, which can cause important alerts to be ignored along with clinically unimportant ones.” 16 Basic DDI‐CDS systems have limited options for suppressing irrelevant DDI alerts, other than turning DDI alerts off for specific drug combinations. This approach has limited opportunities for improving specificity without compromising sensitivity 17–24 . Recent studies concluded that CDS systems should have greater flexibility to customize DDI alerting especially by adding contextual modulation, also called specificity modulation 25–27 .…”

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis

“…In order to prevent mistakes, alerts, defined as notifications or warnings that highlight the risk of danger, have been widely utilized in many fields [2][3][4]. This includes healthcare, where clinical decision support systems (CDSS) commonly use alerts to notify clinicians of actual or potential errors [5,6]. Previous studies have confirmed that alerts are an efficient way to prevent medication errors and streamline clinical workflow [7][8][9].…”