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Lookup NU author(s): Dr Alaa AlahmadiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Background: Drug-induced QT-prolongation increases the risk of TdP arrhythmia attacks and sudden cardiac death. However, measuring the QT-interval and determining a precise cut-off QT/QTc value that could put a patient at risk of TdP is challenging and influenced by many factors including female sex, drug-free baseline, age, genetic predisposition, and bradycardia. Objectives: This paper presents a novel approach for intuitively and visually monitoring QT-prolongation showing a potential risk of TdP, which can be adjusted according to patient-specific risk factors, using a pseudo-coloring technique and explainable artificial intelligence (AI). Methods: We extended the development and evaluation of an explainable AI-based technique− visualized using pseudo-color on the ECG signal, thus intuitively ‘explaining’ how its decision was made −to detect QT-prolongation showing a potential risk of TdP according to a cut-off personalized QTc value (using Bazett's QTc > 60 ms relative to drug-free baseline and Bazett's QTc > 500 ms as examples), and validated its performance using a large number of ECGs (n = 5050), acquired from a clinical trial assessing the effects of four known QT-prolonging drugs versus placebo on healthy subjects. We compared this new personalized approach to our previous study that used a more general approach using the QT-nomogram. Results and conclusions: The explainable AI-based algorithm can accurately detect QT-prolongation when adjusted to a personalized patient-specific cut-off QTc value showing a potential risk of TdP. Using QTc > 60 ms relative to drug-free baseline and QTc > 500 ms as examples, the algorithm yielded a sensitivity of 0.95 and 0.79, and a specificity of 0.95 and 0.98, respectively. We found that adjusting pseudo-coloring according to Bazett's ∆QTc > 60 ms relative to a drug-free baseline personalized to each patient provides better sensitivity than using Bazett's QTc > 500 ms, which could underestimate a potentially clinically significant QT-prolongation with bradycardia.
Author(s): Alahmadi A, Davies A, Vigo M, Jay C
Publication type: Article
Publication status: Published
Journal: Journal of Electrocardiology
Year: 2023
Volume: 81
Pages: 218-223
Online publication date: 07/10/2023
Acceptance date: 07/08/2023
Date deposited: 30/01/2025
ISSN (print): 0022-0736
ISSN (electronic): 1532-8430
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.jelectrocard.2023.09.012
DOI: 10.1016/j.jelectrocard.2023.09.012
ePrints DOI: 10.57711/7jbt-5q14
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