Division of Heart Rhythm Services, Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, United States of America.
Division of Pediatric Cardiology, Department of Pediatrics and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.
PLoS One. 2018 Aug 22;13(8):e0201059. doi: 10.1371/journal.pone.0201059. eCollection 2018.
Dofetilide is an effective antiarrhythmic medication for rhythm control in atrial fibrillation, but carries a significant risk of pro-arrhythmia and requires meticulous dosing and monitoring. The cornerstone of this monitoring, measurement of the QT/QTc interval, is an imperfect surrogate for plasma concentration, efficacy, and risk of pro-arrhythmic potential.
The aim of our study was to test the application of a deep learning approach (using a convolutional neural network) to assess morphological changes on the surface ECG (beyond the QT interval) in relation to dofetilide plasma concentrations.
We obtained publically available serial ECGs and plasma drug concentrations from 42 healthy subjects who received dofetilide or placebo in a placebo-controlled cross-over randomized controlled clinical trial. Three replicate 10-s ECGs were extracted at predefined time-points with simultaneous measurement of dofetilide plasma concentration We developed a deep learning algorithm to predict dofetilide plasma concentration in 30 subjects and then tested the model in the remaining 12 subjects. We compared the deep leaning approach to a linear model based only on QTc.
Fourty two healthy subjects (21 females, 21 males) were studied with a mean age of 26.9 ± 5.5 years. A linear model of the QTc correlated reasonably well with dofetilide drug levels (r = 0.64). The best correlation to dofetilide level was achieved with the deep learning model (r = 0.85).
This proof of concept study suggests that artificial intelligence (deep learning/neural network) applied to the surface ECG is superior to analysis of the QT interval alone in predicting plasma dofetilide concentration.
多非利特是心房颤动节律控制的有效抗心律失常药物，但有明显的致心律失常风险，需要精细的剂量调整和监测。这种监测的基石是 QT/QTc 间期的测量，但它并不能完美地替代血浆浓度、疗效和致心律失常风险。
我们的研究目的是测试深度学习方法（使用卷积神经网络）在评估表面心电图（QT 间期以外）形态变化与多非利特血浆浓度之间的应用。
我们从 42 名接受多非利特或安慰剂的健康受试者中获得了公开的连续心电图和血浆药物浓度，这些受试者参加了一项安慰剂对照交叉随机临床试验。在预定的时间点提取了三个重复的 10 秒心电图，并同时测量了多非利特的血浆浓度。我们开发了一种深度学习算法来预测 30 名受试者的多非利特血浆浓度，然后在其余 12 名受试者中测试该模型。我们将深度学习方法与仅基于 QTc 的线性模型进行了比较。
42 名健康受试者（21 名女性，21 名男性）进行了研究，平均年龄为 26.9 ± 5.5 岁。QTc 的线性模型与多非利特药物水平相关性较好（r = 0.64）。与多非利特水平相关性最好的是深度学习模型（r = 0.85）。
这项概念验证研究表明，人工智能（深度学习/神经网络）应用于体表心电图在预测血浆多非利特浓度方面优于单独分析 QT 间期。
