Introduction: Many medications are associated with QTc prolongation (LQT). Current LQT predictors, including Tisdale and RISQ-PATH, have limited utility due to lack of generalizability to key use cases and modest model performance. A highly accurate LQT risk predictor utilizing data often available at the time of drug starts could broadly support LQT risk management.

Hypothesis: An electronic health record (EHR)-based machine learning (ML) model is superior in predicting drug-induced LQT in a clinical population vs. Tisdale and RISQ-PATH.

Methods: We identified all patients who began taking QT-prolonging medications (‘QTdrug’, per CredibleMeds) at Geisinger and had a follow-up 12-lead ECG within 1 year of medication start date while still on the drug. In case of multiple QTdrugs with overlapping dates, the last start date was used. Using 5-fold cross-validation, we trained an XGBoost ML model to predict LQT (QTc >500 ms) within 1 year of QTdrug start date using EHR data as input, including 131 features across age, sex, smoking history, vitals, lab tests, comorbidities, baseline ECG metrics (within 3 years before drug start date) and QTdrug usage (ever on QTdrug, number of current QTdrugs).

Results: QTdrug records were identified for 362,086 patients, and 6% had LQT within 1 year. The ML model demonstrated superior performance (Figure A) in predicting LQT (area under the curve (AUROC): 0.86, average precision score (AUPRC): 0.41) compared to RISQ-PATH (AUROC: 0.70, AUPRC: 0.19) and Tisdale (AUROC: 0.76, AUPRC: 0.21, calculated in a hospitalized subset N=113544). At the same specificity as RISQ-PATH (98%), the ML model had higher sensitivity (30% vs 10%) and positive predictive value (55% vs 29%). Similar comparisons were observed for Tisdale (Figure B).

Conclusions: An EHR-based ML model outperforms commonly used risk calculators for predicting drug-induced LQT. This model can be used to stratify patients by risk of developing drug-induced LQT in a clinical setting.

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