Appl Clin Inform 2021; 12(01): 190-197
DOI: 10.1055/s-0041-1724043
Research Article

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome

Katy E. Trinkley
1  Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
2  Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
3  Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
,
Jonathan M. Pell
2  Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
3  Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
,
Dario D. Martinez
1  Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
,
Nicola R. Maude
1  Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
,
Gary Hale
3  Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
,
Michael A. Rosenberg
4  Division of Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora, Colorado, United States
5  Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
› Author Affiliations
Funding Data from the electronic health record was obtained through participation of the Health Data Compass data repository, in the Colorado Center for Personalized Medicine, on the University of Colorado Anschutz Medical Campus. CDS analysis was made possible through cooperation of the Epic CDS Governance Committee, directed by Dr. CT Lin. Funding for this investigation was provided by the National Institutes of Health, National Heart Lung and Blood Institute (MAR: K23 HL127296, R01 HL146824; KET: K12 HL137862).

Abstract

Objective Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

Methods We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.

Results The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.

Conclusion Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by the Colorado Multiple Institutional Review Board.




Publication History

Received: 08 October 2020

Accepted: 03 January 2021

Publication Date:
10 March 2021 (online)

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