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

Katy E. Trinkley; Jonathan M. Pell; Dario D. Martinez; Nicola R. Maude; Gary Hale; Michael A. Rosenberg

doi:10.1055/s-0041-1724043

Applied Clinical Informatics, Inhaltsverzeichnis

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

¹Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States

²Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States

³Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States

,

Jonathan M. Pell

²Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States

³Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States

,

Dario D. Martinez

¹Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States

,

Nicola R. Maude

¹Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States

,

Gary Hale

³Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States

,

Michael A. Rosenberg

⁴Division of Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora, Colorado, United States

⁵Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States

› Institutsangaben

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.

Keywords

clinical decision support - electronic health records - drug-induced QT prolongation

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