Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 24, 2022
Date Accepted: Nov 17, 2022
Interpretable Machine-Learning Prediction of Drug-induced QT Prolongation Based on Electronic Health Record Analysis
ABSTRACT
Background:
Drug-induced QT prolongation (diLQTS) is a major concern among hospitalized patients, for which prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict risk of diLQTS, in which case 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 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 risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least one of 39 medications associated with risk of diLQTS, and who had an ECG in the system performed within 24 hours of medication administration. Predictors included over 22K diagnoses and medications at the time of medication administration, with cases of diLQTS defined as QTc over 500ms 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 anti-arrhythmic medications were associated with increased risk across all clusters, and that in non-critically ill patients without cardiovascular disease, propofol was associated with increased risk while ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (AUC 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 prediction of diLQTS. Future investigations should consider this trade-off in development of methods for clinical prediction. Clinical Trial: N/A
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