Literature DB >> 33636421

An explainable algorithm for detecting drug-induced QT-prolongation at risk of torsades de pointes (TdP) regardless of heart rate and T-wave morphology.

Alaa Alahmadi1, Alan Davies2, Jennifer Royle3, Leanna Goodwin4, Katharine Cresswell5, Zahra Arain6, Markel Vigo7, Caroline Jay8.   

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.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Automated ECG interpretation; Drug-induced LQTS; Explainable AI; Human-like algorithm; Machine perception; QT-Prolongation; Rule-based algorithm; TdP; Torsades de pointes; Visual perception

Year:  2021        PMID: 33636421     DOI: 10.1016/j.compbiomed.2021.104281

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  1 in total

1.  SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot recognition.

Authors:  Shui-Hua Wang; Kaihong Wu; Tianshu Chu; Steven L Fernandes; Qinghua Zhou; Yu-Dong Zhang; Jian Sun
Journal:  Wirel Commun Mob Comput       Date:  2021-07-01       Impact factor: 2.336

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.