Holmes Carmen R, Ahmed Ahmed K, Mangold Kathryn E, Noseworthy Peter A, Lopez-Jimenez Francisco, Graff-Radford Jonathan, Rabinstein Alejandro A, English Stephen W
Department of Neurology, Mayo Clinic, Rochester, MN.
Department of Neurology, Mayo Clinic, Jacksonville, FL.
Mayo Clin Proc. 2025 Aug;100(8):1360-1369. doi: 10.1016/j.mayocp.2024.10.019. Epub 2025 Jul 2.
To evaluate the performance of an artificial intelligence (AI)-enhanced electrocardiography (ECG; AI-ECG) algorithm to predict atrial fibrillation (AF) detection on prolonged cardiac monitoring (PCM) after index stroke.
This retrospective study included all adult patients with ischemic stroke evaluated at Mayo Clinic with baseline ECG and PCM from January 1, 2018, to December 31, 2020. We recorded demographic characteristics, clinical features, presumed stroke mechanism, PCM duration, and PCM outcome (AF vs no AF) and AF burden. Electrocardiograms were analyzed using the AI-ECG algorithm to determine likelihood of AF capture with PCM. Stroke etiology was adjudicated using TOAST (Trial of ORG 10172 in Acute Stroke Treatment) and embolic stroke of undetermined source (ESUS) definitions. The ability of the AI-ECG algorithm to predict AF detected by PCM was assessed via receiver operating characteristics analysis, calculating the area under the receiver operating characteristic curve (C statistic). Sensitivity and specificity analyses were performed for each tool using optimal cutoffs (using maximum Youden indices).
We identified 863 patients for inclusion in the study. The median age was 69 years, 496 (57.5%) were male, 367 (42.5%) were women, and 561 patients (65.0%) were categorized as having ESUS. Prolonged cardiac monitoring detected AF in 85 patients (9.8%). Median duration of PCM was 30 days (IQR, 25 to 30 days). The AI-ECG algorithm identified a notable difference in probability of AF on PCM. For its optimal model output cutoff of 0.24, AI-ECG had a negative predictive value of 94.2% (95% CI, 92.2% to 95.9%) and a specificity of 81.8% (95% CI, 78.9% to 84.4%) for excluding AF on PCM. When evaluating for an AF burden of 6 minutes or longer, the AI-ECG had a negative predictive value of 96.7% (95% CI, 95.5% to 97.6%). There was no significant difference in the area under the receiver operating characteristic curve when comparing the ESUS vs non-ESUS subgroups (P=.42).
This study found that AI-ECG may help identify patients unlikely to have AF on PCM and can predict the occurrence of longer episodes of AF. Thus, AI-ECG may be used to stratify which patients should undergo PCM after stroke. Future studies should compare the performance of AI-ECG and PCM for the clinical end point of stroke recurrence.
评估一种人工智能（AI）增强型心电图（ECG；AI-ECG）算法在预测首次卒中后长期心脏监测（PCM）时房颤（AF）检测情况方面的性能。
这项回顾性研究纳入了2018年1月1日至2020年12月31日在梅奥诊所接受评估的所有成年缺血性卒中患者，这些患者均有基线心电图和PCM数据。我们记录了人口统计学特征、临床特征、推测的卒中机制、PCM持续时间、PCM结果（AF发生与否）以及AF负荷。使用AI-ECG算法分析心电图，以确定PCM检测到AF的可能性。采用急性卒中治疗中ORG 10172试验（TOAST）和不明来源栓塞性卒中（ESUS）的定义来判定卒中病因。通过受试者工作特征分析评估AI-ECG算法预测PCM检测到AF的能力，计算受试者工作特征曲线下面积（C统计量）。使用最佳截断值（采用最大约登指数）对每种工具进行敏感性和特异性分析。
我们确定了863例患者纳入研究。中位年龄为69岁，496例（57.5%）为男性，367例（42.5%）为女性，561例患者（65.0%）被归类为患有ESUS。长期心脏监测在85例患者（9.8%）中检测到AF。PCM的中位持续时间为30天（四分位间距，25至30天）。AI-ECG算法在PCM检测AF的概率方面发现了显著差异。对于其最佳模型输出截断值0.24，AI-ECG排除PCM检测到AF的阴性预测值为94.2%（95%CI，92.2%至95.9%），特异性为81.8%（95%CI，78.9%至84.4%）。当评估AF负荷为6分钟或更长时间时，AI-ECG的阴性预测值为96.7%（95%CI，95.5%至97.6%）。比较ESUS与非ESUS亚组时，受试者工作特征曲线下面积无显著差异（P = 0.42）。
本研究发现，AI-ECG可能有助于识别PCM时不太可能发生AF的患者，并可预测更长时间AF发作的发生。因此，AI-ECG可用于对卒中后哪些患者应接受PCM进行分层。未来的研究应比较AI-ECG和PCM在卒中复发临床终点方面的性能。
