Mahayni Abdulah A, Attia Zachi I, Medina-Inojosa Jose R, Elsisy Mohamed F A, Noseworthy Peter A, Lopez-Jimenez Francisco, Kapa Suraj, Asirvatham Samuel J, Friedman Paul A, Crestenallo Juan A, Alkhouli Mohamad
Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
Department of Cardiovascular Surgery, Mayo Clinic, Rochester, MN.
Mayo Clin Proc. 2021 Dec;96(12):3062-3070. doi: 10.1016/j.mayocp.2021.06.024.
To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%).
Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality.
A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%.
A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.
评估一种基于心电图开发的人工智能（AI）算法，该算法用于检测严重心室功能障碍（左心室射血分数[LVEF]为35%或更低），能否独立预测无严重心室功能障碍（LVEF>35%）的心脏手术患者的长期死亡率。
纳入在梅奥诊所（1993 - 2019年）接受瓣膜或冠状动脉搭桥手术且基线心电图记录的LVEF高于35%的患者。我们比较了术前心电图上人工智能增强心电图（AI-ECG）筛查LVEF为35%或更低时结果异常与正常的患者。主要终点是全因死亡率。
共纳入20627例患者，其中17125例（83.0%）AI-ECG筛查结果正常，3502例（17.0%）AI-ECG筛查结果异常。AI-ECG筛查结果异常的患者年龄更大，合并症更多。AI-ECG筛查结果正常的患者5年和10年生存率分别为86.2%和68.2%，而筛查结果异常的患者分别为71.4%和45.1%（对数秩检验，P<0.01）。在多变量Cox生存分析中，AI-ECG筛查结果异常与总体全因死亡率较高独立相关（风险比[HR]，1.31；95%CI，1.24至1.37），在单纯瓣膜手术亚组（HR，1.30；95%CI，1.18至1.42）、单纯冠状动脉搭桥术亚组（HR，1.29；95%CI，1.20至1.39）以及冠状动脉搭桥术与瓣膜手术联合亚组（HR，1.19；95%CI，1.08至1.32）中也是如此。在亚组分析中，AI-ECG筛查结果异常与死亡率之间的关联在LVEF为35%至55%的患者以及LVEF高于55%的患者中是一致的。
一种预测严重心室功能障碍的新型基于心电图的AI算法可以预测LVEF高于35%且接受瓣膜和/或冠状动脉搭桥手术患者的长期死亡率。
