Howard James Philip, Vasudevan Neethu, Sarkar Shantanu, Landman Sean, Koehler Jodi, Keene Daniel
National Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK.
Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA.
Eur Heart J Digit Health. 2024 May 8;5(4):435-443. doi: 10.1093/ehjdh/ztae035. eCollection 2024 Jul.
Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown.
We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28-2.79; = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27-2.79 = 0.002).
An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.
植入式循环记录仪（ILR）可提供连续单导联动态心电图（aECG）监测。这些aECG能否用于识别心力衰竭（HF）恶化尚不清楚。
我们将美敦力设备数据库中的ILR aECG与Optum去识别电子健康记录数据集中的左心室射血分数（LVEF）测量值相链接。我们在来自2247例患者的35741份aECG数据集上训练了一种人工智能（AI）算法[aECG卷积神经网络（CNN）]，以识别LVEF≤40%，并使用受试者操作特征曲线下面积评估其性能。然后，动态心电图-CNN被用于在909例既往有HF诊断的真实世界队列中识别HF住院风险增加的患者。该数据集提供了12467次每月随访评估，其中有201次HF住院。对于每个月，这些预测的时间序列特征被用于将患者分为高风险和低风险组，并预测下个月的HF住院情况。与低风险组相比，动态心电图-CNN识别为高风险的队列中，未来30天HF住院风险显著更高[风险比（HR）1.89；95%置信区间（CI）1.28-2.79；P = 0.001]，即使在调整患者人口统计学特征后（HR 1.88；95%CI 1.27-2.79；P = 0.002）。
使用ILR aECG训练以检测LVEF≤40%的AI算法，也可以通过监测30天内HF概率的变化，轻松识别HF住院风险增加的患者。
