Reining in the QTc: reducing the risk of Torsades de Pointes across a major health system

Cheung, Dora; Cumbler, Ethan; Hale, Gary; Pell, Jonathan

doi:10.1093/jamia/ocy081

Cited by 7 publications

(12 citation statements)

References 12 publications

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“…20 This idea has prompted several centers to implement CDS systems to guide providers. In addition to the CDS program, here at the University of Colorado, 11,12 other investigators [21][22][23] have examined automated CDS at their respective institutions for impact on prescription patterns and outcomes. Bertsche et al examined a computerized CDS for drug-drug interactions in the ICU, and found that the CDS tool reduced incidence of QT prolongation by 64%.…”

Section: Discussionmentioning

confidence: 99%

“…500 ms). 11,12 These CDS tools decreased use of QT-prolonging medications broadly within the population. However, further work is needed to understand the strengths and weaknesses of this alert at the scale in which it is being applied and to identify measures to optimize its effectiveness.…”

Section: Background and Significancementioning

confidence: 99%

“…The CDS tools we studied in this investigation (►Fig. 1) 11,12 are triggered when a patient has had a prior ECG with a QTc value logged in the EHR that is ! 500 ms within the past 14 days and is prescribed 1 of 10 medications with potential to cause diLQTS: azithromycin, chlorpromazine, citalopram, clarithromycin, erythromycin, escitalopram, haloperidol, methadone, moxifloxacin, or parenteral ondansetron.…”

Section: Description Of the Clinical Decision Support Toolsmentioning

confidence: 99%

“…Preliminary work suggested that reasons were inconsistently applied, thus formal examination was beyond the scope of this investigation. 11,12 If providers select "Remove," then the order for the QT-prolonging medication is canceled. There is also an option to select "Cancel" which allows providers to dismiss the alert and continue with their intended medication order.…”

Section: Description Of the Clinical Decision Support Toolsmentioning

confidence: 99%

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

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%

Section: Background and Significancementioning

confidence: 99%

Section: Description Of the Clinical Decision Support Toolsmentioning

confidence: 99%

Section: Description Of the Clinical Decision Support Toolsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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“…Drug-induced long-QT syndrome (diLQTS) [1,2] is a major concern for inpatients worldwide and has been identified as a key target for clinical decision support tools [3][4][5][6][7]. Importantly, although certain medications have been implicated as having significant clinical risk [8,9], for others, despite a known risk of diLQTS, clinical validation has been lacking [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Simon¹,

Trinkley²,

Malone³

et al. 2022

J Med Internet Res

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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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Predictive analytics for reducing the risk of QT interval prolongation and torsades de pointes

Tisdale¹

2022

Torsades De Pointes

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Reining in the QTc: reducing the risk of Torsades de Pointes across a major health system

Cited by 7 publications

References 12 publications

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

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

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Predictive analytics for reducing the risk of QT interval prolongation and torsades de pointes

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