Reissenberger Pamela, Serfözö Peter, Piper Diana, Juchler Norman, Glanzmann Sara, Gram Jasmin, Hensler Karina, Tonidandel Hannah, Börlin Elena, D'Souza Marcus, Badertscher Patrick, Eckstein Jens
Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.
Preventicus, Ernst-Abbe-Str. 15, 07743 Jena, Germany.
Eur Heart J Digit Health. 2023 Jul 6;4(5):402-410. doi: 10.1093/ehjdh/ztad039. eCollection 2023 Oct.
Recent studies suggest that atrial fibrillation (AF) burden (time AF is present) is an independent risk factor for stroke. The aim of this trial was to study the feasibility and accuracy to identify AF episodes and quantify AF burden in patients with a known history of paroxysmal AF with a photoplethysmography (PPG)-based wearable.
In this prospective, single-centre trial, the PPG-based estimation of AF burden was compared with measurements of a conventional 48 h Holter electrocardiogram (ECG), which served as the gold standard. An automated algorithm performed PPG analysis, while a cardiologist, blinded for the PPG data, analysed the ECG data. Detected episodes of AF measured by both methods were aligned timewise.Out of 100 patients recruited, 8 had to be excluded due to technical issues. Data from 92 patients were analysed [55.4% male; age 73.3 years (standard deviation, SD: 10.4)]. Twenty-five patients presented AF during the study period. The intraclass correlation coefficient of total AF burden minutes detected by the two measurement methods was 0.88. The percentage of correctly identified AF burden over all patients was 85.1% and the respective parameter for non-AF time was 99.9%.
Our results demonstrate that a PPG-based wearable in combination with an analytical algorithm appears to be suitable for a semiquantitative estimation of AF burden in patients with a known history of paroxysmal AF.
NCT04563572.
近期研究表明,房颤(AF)负荷(房颤存在的时间)是卒中的独立危险因素。本试验的目的是研究使用基于光电容积脉搏波描记法(PPG)的可穿戴设备识别房颤发作并量化已知阵发性房颤病史患者房颤负荷的可行性和准确性。
在这项前瞻性单中心试验中,将基于PPG的房颤负荷估计值与传统48小时动态心电图(ECG)测量值进行比较,后者作为金标准。采用自动算法进行PPG分析,同时由一名对PPG数据不知情的心脏病专家分析ECG数据。两种方法检测到的房颤发作按时间对齐。在招募的100名患者中,8名因技术问题被排除。对92名患者的数据进行了分析[男性占55.4%;年龄73.3岁(标准差,SD:10.4)]。25名患者在研究期间出现房颤。两种测量方法检测到的总房颤负荷分钟数的组内相关系数为0.88。所有患者中正确识别的房颤负荷百分比为85.1%,非房颤时间的相应参数为99.9%。
我们的结果表明,基于PPG的可穿戴设备与分析算法相结合似乎适用于对已知阵发性房颤病史患者的房颤负荷进行半定量估计。
NCT04563572。