Melo Luke, Ciconte Giuseppe, Christy Ashton, Vicedomini Gabriele, Anastasia Luigi, Pappone Carlo, Grant Edward
Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy.
PNAS Nexus. 2023 Oct 13;2(11):pgad327. doi: 10.1093/pnasnexus/pgad327. eCollection 2023 Nov.
One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.
十分之一的心脏性猝死病例是由遗传性心律失常性心肌病导致的，且没有任何预警，比如布加综合征（BrS）。正常的生理变化常常会掩盖常规心电图（ECG）中这种疾病以及相关危及生命的离子通道病的明显迹象。钠通道阻滞剂能够揭示先前隐藏的诊断性心电图特征，然而，使用它们存在引发危及生命的致心律失常副作用的风险。由于缺乏非侵入性检测方法，很大一部分处于心脏性猝死风险中的人群被严重低估了。在此，我们提出一种机器学习算法，用于提取、对齐和分类心电图波形，以检测布加综合征的存在。该方案在不使用钠通道阻滞剂的情况下取得了成功（验证准确率为88.4%，AUC为0.934），能够帮助临床医生识别这种潜在的危及生命的心脏病。
