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Simon ST, Lin M, Trinkley KE, Aleong R, Rafaels N, Crooks KR, Reiter MJ, Gignoux CR, Rosenberg MA. A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation. PLoS One 2024; 19:e0303261. [PMID: 38885227 PMCID: PMC11182491 DOI: 10.1371/journal.pone.0303261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 04/23/2024] [Indexed: 06/20/2024] Open
Abstract
Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17-1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS.
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Affiliation(s)
- Steven T. Simon
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Meng Lin
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Katy E. Trinkley
- Department of Clinical Pharmacy, School of Pharmacy, University of Colorado, Aurora, CO, United States of America
| | - Ryan Aleong
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Kristy R. Crooks
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Michael J. Reiter
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Christopher R. Gignoux
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Michael A. Rosenberg
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States of America
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Lowe RN, Wright G, Olivas L, Teel C, Suresh K, Macke LB, Sieja A, Rosenberg MA, Trinkley KE. Evaluating the prescribing and monitoring of medications associated with QTc-prolongation in the ambulatory care setting. J Eval Clin Pract 2024; 30:385-392. [PMID: 38073034 PMCID: PMC11023790 DOI: 10.1111/jep.13949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 02/03/2024]
Abstract
RATIONALE Little is known about the prescribing of medications with potential to cause QTc-prolongation in the ambulatory care settings. Understanding real-world prescribing of QTc-prolonging medications and actions taken to mitigate this risk will help guide strategies to optimize safety and appropriate prescribing among ambulatory patients. OBJECTIVE To evaluate the frequency of clinician action taken to monitor and mitigate modifiable risk factors for QTc-prolongation when indicated. METHODS This retrospective, cross-sectional study evaluated clinician action at the time of prescribing prespecified medications with potential to prolong QTc in adult patients in primary care. The index date was defined as the date the medication was ordered. Electronic health record (EHR) data were evaluated to assess patient, clinician and visit characteristics. Clinician action was determined if baseline or follow-up monitoring was ordered or if action was taken to mitigate modifiable risk factors (laboratory abnormalities or electrocardiogram [ECG] monitoring) within 48 h of prescribing a medication with QTc-prolonging risk. Descriptive statistics were used to describe current practice. RESULTS A total of 399 prescriptions were prescribed to 386 patients, with a mean age of 51 ± 18 years, during March 2021 from a single-centre, multisite health system. Of these, 17 (4%) patients had a known history of QTc-prolongation, 170 (44%) did not have a documented history of QTc-prolongation and 199 (52%) had an unknown history (no ECG documented). Thirty-nine patients (10%) had at least one laboratory-related risk factor at the time of prescribing, specifically hypokalemia (16 patients), hypomagnesemia (8 patients) or hypocalcemia (19 patients). Of these 39 patients with laboratory risk factors, only 6 patients (15%) had their risk acknowledged or addressed by a clinician. Additionally, eight patients' most recent QTc was ≥500 ms and none had an ECG checked at the time the prescription was ordered. CONCLUSION Despite national recommendations, medication monitoring and risk mitigation is infrequent when prescribing QTc-prolonging medications in the ambulatory care setting. These findings call for additional research to better understand this gap, including reasons for the gap and consequences on patient outcomes.
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Affiliation(s)
- Rachel N Lowe
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
| | - Garth Wright
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
| | - Lucas Olivas
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
| | - Candance Teel
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
| | - Krithika Suresh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Laura B Macke
- Department of Internal Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Amber Sieja
- Department of Internal Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Michael A Rosenberg
- Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Katy E Trinkley
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, USA
- Department of Family Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
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Simon ST, Trinkley KE, Malone DC, Rosenberg MA. Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis. J Med Internet Res 2022; 24:e42163. [PMID: 36454608 PMCID: PMC9756119 DOI: 10.2196/42163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/31/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability. OBJECTIVE In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk. METHODS We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters. RESULTS Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration. CONCLUSIONS In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.
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Affiliation(s)
- Steven T Simon
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Katy E Trinkley
- Department of Clinical Pharmacy, School of Pharmacy, University of Colorado, Aurora, CO, United States
| | - Daniel C Malone
- College of Pharmacy, University of Utah, Salt Lake City, UT, United States
| | - Michael Aaron Rosenberg
- Division of Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora, CO, United States
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Trinkley KE, Pell JM, Martinez DD, Maude NR, Hale G, Rosenberg MA. Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome. Appl Clin Inform 2021; 12:190-197. [PMID: 33694143 DOI: 10.1055/s-0041-1724043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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.
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Affiliation(s)
- Katy E Trinkley
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States.,Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States.,Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
| | - Jonathan M Pell
- Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States.,Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
| | - Dario D Martinez
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Nicola R Maude
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Gary Hale
- Department of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States
| | - Michael A Rosenberg
- Division of Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora, Colorado, United States.,Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
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