Kimura Takehiro, Jinzaki Masahiro, Miyama Hiroshi, Hashimoto Kenji, Yamashita Terumasa, Katsumata Yoshinori, Takatsuki Seiji, Fukuda Keiichi, Ieda Masaki
Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Department of Radiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Am J Prev Cardiol. 2025 Feb 23;21:100951. doi: 10.1016/j.ajpc.2025.100951. eCollection 2025 Mar.
The Apple Watch alerts users to irregular heart rhythms and potential atrial fibrillation (AF), but delays in obtaining electrocardiograms (ECGs) after notifications can impede accurate disease diagnosis. We aimed to predict personalized AF risk using continuous Apple Watch lifelog data to facilitate timely ECG acquisition. We conducted two analyses: Keio and national. In the Keio analysis, AF patients underwent continuous 2-week Holter ECG monitoring, and a machine-learning model combining gradient-boosting decision trees and deep learning was developed. The national analysis recruited Apple Watch users across Japan to assess the model; data and survey responses were collected for seven days via a dedicated iPhone app.
A total of 100 subjects (age: 63.9 ± 12.4 years, AF burden: 37.7 %) participated in the Keio analysis, while 8,935 subjects participated in the national analysis. Significant differences in Apple Watch data, including pulse rate ( < 0.001) and step count ( < 0.001), were observed between days with and without AF onset. Healthcare data measured by the Apple Watch, including sleep patterns, were significantly correlated with subjective survey responses ( < 0.001) and incorporated into the model. The model achieved an F-value of 90.7 % compared to diagnosis based on a 2-week Holter ECG. The model showed an additive benefit to Apple Watch irregular-rhythm notifications for AF detection (irregular-rhythm notification vs. model: 68.8 % vs. 88.2 % for paroxysmal AF and 84.4 % vs. 100.0 % for persistent AF).
Apple Watch-derived lifelogs enabled individualized AF onset risk assessment and the development of a machine-learning model for optimizing ECG timing for early AF detection.
苹果手表可提醒用户注意心律不齐和潜在的心房颤动（AF），但通知后获取心电图（ECG）的延迟可能会妨碍疾病的准确诊断。我们旨在利用苹果手表的连续生活记录数据预测个性化的房颤风险，以便及时进行心电图采集。我们进行了两项分析：庆应义塾大学分析和全国性分析。在庆应义塾大学分析中，房颤患者接受了为期2周的连续动态心电图监测，并开发了一种结合梯度提升决策树和深度学习的机器学习模型。全国性分析招募了日本各地的苹果手表用户来评估该模型；通过一款专用的iPhone应用程序收集了7天的数据和调查回复。
共有100名受试者（年龄：63.9±12.4岁，房颤负担：37.7%）参与了庆应义塾大学分析，8935名受试者参与了全国性分析。在房颤发作日和未发作日之间，观察到苹果手表数据存在显著差异，包括心率（<0.001）和步数（<0.001）。苹果手表测量的健康数据，包括睡眠模式，与主观调查回复显著相关（<0.001），并被纳入模型。与基于2周动态心电图的诊断相比，该模型的F值达到了90.7%。该模型显示出对苹果手表房颤检测不规则心律通知的附加益处（不规则心律通知与模型：阵发性房颤为68.8%对88.2%，持续性房颤为84.4%对100.0%）。
源自苹果手表的生活记录能够实现个性化的房颤发作风险评估，并开发出一种机器学习模型，用于优化早期房颤检测的心电图检查时机。
