AHA Scientific Sessions 2021, Tempus-authored —
Introduction: Initiation of QTc-prolonging medications may lead to the rare but potentially catastrophic event, torsades de pointes (TDP). At present, no adequate, generalizable tools exist to predict drug-induced long QTc (LQT); machine learning from ECG data is a promising approach.
Hypothesis: Prediction of drug-induced LQT using an ECG-based machine learning model is feasible, and outperforms a model trained on baseline QTc, age, and sex alone.
Methods: We identified baseline 12-lead ECGs with QTc values < 500 ms for patients who had not received any known, conditional, or possible QTc prolonging medication per CredibleMeds at the time of ECG or within the past 90 days. We matched these with ECGs from the same patients while they were taking at least one CredibleMeds drug (“on-drug” ECGs). Using 5-fold cross-validation, we trained and tested two machine learning models using the baseline ECGs of the 92,848 resulting pairs to predict drug-induced LQT (≥500 ms) in the on-drug ECGs: a deep neural network using ECG voltage data, and a gradient-boosted tree using the baseline QTc. Age and sex were also inputs to both models.
Results: On-drug LQT prevalence was 16%. The ECG model demonstrated superior performance in predicting on-drug LQT (area under the receiver operating characteristic curve (AUC) = 0.756) compared to the QTc model (0.710). At a potential operating point (Figure), the ECG model had 89% sensitivity and 95% negative predictive value. Even in the subset of patients with baseline QTc < 470/480 ms (male/female; post-drug LQT prevalence = 14%), the ECG model demonstrated good performance (AUC = 0.736).
Conclusions: An ECG-based machine learning model can stratify patients by risk of developing drug-induced LQT better than a model using baseline QTc alone. This model may have clinical value to identify high-risk drug starts that would benefit from closer monitoring and others who are at low risk of drug-induced LQT.
View the full publication here.
Authors: Thomas Morland, Sushravya Raghunath, Christopher R Kelsey, Jeffrey Ruhl, Steven R Steinhubl, Mariya P Monfette, John Pfeifer, Ruijun Chen, Noah Zimmerman, Brian P Delisle, Randle Storm, Christopher M Haggerty, and Brandon Fornwalt