Clin Med (Lond). 2026 Oct 28:100666. doi: 10.1016/j.clinme.2026.100666. Online ahead of print.
ABSTRACT
BACKGROUND: Drug-induced QTc-prolongation is a major concern for developing ventricular arrhythmias, which may eventually lead to sudden cardiac death. Identifying high-risk patients is essential to support healthcare providers in handling automatically generated alerts in clinical practice. The aim of study was to develop clinical data-driven machine learning methods to predict drug-induced QTc-prolongation.
METHODS: Inpatients with paired electrocardiograms were enrolled from Southwest Hospital (2015-2022). Six machine learning (ML) algorithms were developed to predict drug-induced QTc prolongation (ΔQTc > 60 ms). Model performance was evaluated using an internal test set and an independent external validation cohort. Model interpretations were recognized by feature importance and SHapley Additive exPlanations (SHAP) methods. Finally, we benchmarked the ML models against the traditional Tisdale risk score.
RESULTS: A total of 2451 participants were recruited, of whom 292 developed QTc-prolongation, while 150 patients from a separate center served as the external validation cohort. The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with recall of 0.884 and AUC of 0.814 in internal test set. During external validation, the AUC remained robust at 0.864, although recall declined to 0.650. SHAP analysis identified baseline QTc and QT intervals, inflammation, high-risk drugs, and hypertension are the primary drivers of model predictions.
CONCLUSIONS: The XGBoost algorithm provides a highly sensitive framework for predicting drug-induced QTc prolongation. Integrating this ML tool with identified clinical drivers into hospital decision support systems would facilitate the optimization of treatment strategies and early ECG monitoring strategies.
PMID:42951254 | DOI:10.1016/j.clinme.2026.100666