Abstract
Torsade de points (TdP), a life-threatening arrhythmia that can increase the risk of sudden cardiac death, is associated with drug-induced QT-interval prolongation on the electrocardiogram (ECG). While many modern ECG machines provide automated measurements of the QT-interval, these automated QT values are usually correct only for a noise-free normal sinus rhythm, in which the T-wave morphology is well defined. As QT-prolonging drugs often affect the morphology of the T-wave, automated QT measurements taken under these circumstances are easily invalidated. An additional challenge is that the QT-value at risk of TdP varies with heart rate, with the slower the heart rate, the greater the risk of TdP. This paper presents an explainable algorithm that uses an understanding of human visual perception and expert ECG interpretation to automate the detection of QT-prolongation at risk of TdP regardless of heart rate and T-wave morphology. It was tested on a large number of ECGs (n=5050) with variable QT-intervals at varying heart rates, acquired from a clinical trial that assessed the effect of four known QT-prolonging drugs versus placebo on healthy subjects. The algorithm yielded a balanced accuracy of 0.97, sensitivity of 0.94, specificity of 0.99, F1-score of 0.88, ROC (AUC) of 0.98, precision-recall (AUC) of 0.88, and Matthews correlation coefficient (MCC) of 0.88. The results indicate that a prolonged ventricular repolarisation area can be a significant risk predictor of TdP, and detection of this is potentially easier and more reliable to automate than measuring the QT-interval distance directly. The proposed algorithm can be visualised using pseudo-colour on the ECG trace, thus intuitively ‘explaining’ how its decision was made, which results of a focus group show may help people to self-monitor QT-prolongation, as well as ensuring clinicians can validate its results.
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•We develop an algorithm to detect drug-induced QT-prolongation at risk of TdP regardless of heart rate and T-wave morphology.•We used a ‘human-like’ approach, along with diagnostic clinical rules, tested on a large number of ECGs (n=5050).•We explore automating the rule-generation with a decision tree, but it overfits and lacks clinical validity.•A focus group evaluation supports an ‘expert’ rule-based approach, rather than a fully automated approach.•Defining ‘human-like’ features to automate ECG interpretation has the benefit of being explainable to patients and clinicians.