预测心房颤动伴快速心室反应时左心室收缩功能障碍的深度学习算法。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2024-08-19 eCollection Date: 2024-11-01 DOI:10.1093/ehjdh/ztae062
Joo Hee Jeong, Sora Kang, Hak Seung Lee, Min Sung Lee, Jeong Min Son, Joon-Myung Kwon, Hyoung Seok Lee, Yun Young Choi, So Ree Kim, Dong-Hyuk Cho, Yun Gi Kim, Mi-Na Kim, Jaemin Shim, Seong-Mi Park, Young-Hoon Kim, Jong-Il Choi
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引用次数: 0

摘要

目的:尽管评估左心室射血分数(LVEF)对于决定房颤患者的心率控制策略至关重要,但在门诊环境中,LVEF的实时评估却很有限。我们旨在研究基于人工智能的算法在预测房颤和快速心室反应(RVR)患者左心室收缩功能障碍(LVSD)方面的性能:本研究是对基于残差神经网络架构的已有深度学习算法的外部验证。数据来自 2018 年至 2023 年期间在一个单一中心进行的房颤伴 RVR 的前瞻性队列。主要结果是 LVSD 的检测,定义为 LVEF ≤ 40%,使用 12 导联心电图(ECG)进行评估。次要结果包括使用单导联心电图(I导联)预测 LVSD。在 423 名患者中,有 241 人在 2 个月内获得了超声心动图数据,其中 54 人(22.4%)被证实患有 LVSD。深度学习算法在预测 LVSD 方面表现尚可[曲线下面积 (AUC) 0.78]。排除 LVSD 的负预测值为 0.88。与脑钠肽 N 端前体相比,深度学习算法在预测 LVSD 方面表现出色(AUC 0.78 vs. 0.70,P = 0.12)。深度学习算法在导联I中的预测性能较低(AUC为0.68);但阴性预测值保持一致(0.88):结论:深度学习算法在预测房颤和 RVR 患者的 LVSD 方面表现出色。在门诊环境中,使用基于人工智能的算法可能有助于预测 LVSD 和更早地选择药物,从而更好地控制房颤合并 RVR 患者的症状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response.

Aims: Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR).

Methods and results: This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤ 40%, assessed using 12-lead electrocardiography (ECG). Secondary outcome involved predicting LVSD using 1-lead ECG (Lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep learning algorithm demonstrated fair performance in predicting LVSD [area under the curve (AUC) 0.78]. Negative predictive value for excluding LVSD was 0.88. Deep learning algorithm resulted competent performance in predicting LVSD compared with N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, P = 0.12). Predictive performance of the deep learning algorithm was lower in Lead I (AUC 0.68); however, negative predictive value remained consistent (0.88).

Conclusion: Deep learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR.

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