Development of a predictive model for drug-associated QT prolongation in the inpatient setting using electronic health record data. (11th June 2019)

• Record Type: Journal Article
• Authors: Hincapie-Castillo, Juan M; Staley, Benjamin; Henriksen, Carl; Saidi, Arwa; Lipori, Gloria Pflugfelder; Winterstein, Almut G
• Is Part Of: American journal of health-system pharmacy. Volume 76:Number 14(2019)
• Page Start: 1059
• Page End: 1070
• Publication Date: 2019-06-11
• Subjects: drug-induced arrhythmia -- electronic health records -- prediction model -- QT prolongation -- risk score; Hospital pharmacies -- United States -- Periodicals; 615.1
• DOI: 10.1093/ajhp/zxz100
• Languages: English
• ISSNs: 1079-2082

Abstract

Abstract: Purpose: We aimed to construct a dynamic model for predicting severe QT interval prolongation in hospitalized patients using inpatient electronic health record (EHR) data. Methods: A retrospective cohort consisting of all adults admitted to 2 large hospitals from January 2012 through October 2013 was established. Thirty-five risk factors for severe QT prolongation (defined as a Bazett's formula—corrected QT interval [QTc] of ≥500 msec or a QTc increase of ≥60 msec from baseline) were operationalized for automated EHR retrieval; upon univariate analyses, 26 factors were retained in models for predicting the 24-hour risk of QT events on hospital day 1 (the Day 1 model) and on hospital days 2–5 (the Days 2–5 model). Results: A total of 1, 672 QT prolongation events occurred over 165, 847 days of risk exposure during the study period. C statistics were 0.828 for the Day 1 model and 0.813 for the Days 2–5 model. Patients in the upper 50th percentile of calculated risk scores experienced 755 of 799 QT events (94%) allocated in the Day 1 model and 804 of 873 QT events (92%) allocated in the Days 2–5 model. Among patients in the 90th percentile, the Day 1 and Days 2–5 models captured 351 of 799 (44%) and 362 of 873 (41%) QT events, respectively. Conclusion: The risk models derived from EHR data for all admitted patients had good predictive validity. All risk factors were operationalized from discrete EHR fields to allow full automation for real-time identification of high-risk patients. Further research to test the models in other health systems and evaluate their effectiveness on outcomes and patient care in clinical practice is recommended.