%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e68256
%T Impact of an Alert-Based Inpatient Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome: Large-Scale, System-Wide Observational Study
%A Trinkley,Katy E
%A Simon,Steven T
%A Rosenberg,Michael A
%+ Division of Cardiology, University of Colorado Anschutz Medical Campus, Mail Stop B132, 12401 E. 17th Avenue, Aurora, CO, United States, 1 7208486563, michael.a.rosenberg@cuanschutz.edu
%K drug-induced QT prolongation
%K predictive modeling
%K electronic health records
%K clinical decision support
%K alert-based CDS system
%K tools
%K long QT syndrome
%K prevention
%D 2025
%7 14.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Prevention of drug-induced QT prolongation (diLQTS) has been the focus of many system-wide clinical decision support (CDS) tools, which can be directly embedded within the framework of the electronic health record system and triggered to alert in high-risk patients when a known QT-prolonging medication is ordered. Justification for these CDS systems typically lies in the ability to accurately predict which patients are at high risk; however, it is not always evident that identification of risk alone is sufficient for appropriate CDS implementation. Objective: In this investigation, we examined the impact of a system-wide, alert-based, inpatient CDS tool designed to prevent diLQTS across 10 known QT-prolonging medications. Methods: We compared the risk of diLQTS, duration of hospitalization, and in- and out-of-hospital mortality before and after implementation of the CDS system in 178,097 hospitalizations among 102,847 patients. We also compared outcomes between those in whom an alert fired and those in whom it did not, and within the various responses to the alert by providers. Analyses were adjusted for age, sex, race and ethnicity, inpatient location, electrolyte values, and comorbidities, with the latter processed using an unsupervised clustering analysis applied to the top 500 most common medications and diagnosis codes, respectively. Results: We found that the simple, rule-based logic of the CDS (any prior electrocardiograph with heart rate–corrected QT interval (QTc)≥500 ms) successfully identified patients at high risk of diLQTS with an odds ratio of 2.28 (95% CI 2.10-2.47, P<.001) among those in whom it fired. However, we did not identify any impact on the risk of diLQTS based on provider responses or on the risk of inpatient, 3-month, 6-month, or 1-year mortality. When compared with rates prior to implementation, the risk of diLQTS was not significantly different after the CDS tools were deployed across the system, although mortality was significantly higher after the tools were implemented. Conclusions: We found that despite successful identification of high-risk patients for diLQTS, deployment of an alert-based CDS did not impact the risk of diLQTS. These findings suggest that quantification of high risk may be insufficient rationale for implementation of a CDS system and that hospital systems should consider evaluation of the system in its entirety prior to adoption to improve clinical outcomes. 
%R 10.2196/68256
%U https://www.jmir.org/2025/1/e68256
%U https://doi.org/10.2196/68256