Department of Stroke and Neuroscience, Charing Cross Hospital, Imperial College London NHS Healthcare Trust, London, United Kingdom; Division of Brain Sciences, Department of Medicine, Hammersmith Campus, Imperial College London, London, United Kingdom; University of Athens, Athens, Greece.
Middlemore Hospital, Counties Manukau District Health Board, Auckland, New Zealand; University of Athens, Athens, Greece.
J Stroke Cerebrovasc Dis. 2020 Apr;29(4):104669. doi: 10.1016/j.jstrokecerebrovasdis.2020.104669. Epub 2020 Feb 11.
Rapid and sensitive detection of atrial fibrillation (AF) is of paramount importance for initiation of adequate preventive therapy after stroke. Stroke Unit care includes continuous electrocardiogram monitoring (CEM) but the optimal exploitation of the recorded ECG traces is controversial. In this retrospective single-center study, we investigated whether an automated analysis of continuous electrocardiogram monitoring (ACEM), based on a software algorithm, accelerates the detection of AF in patients admitted to our Stroke Unit compared to the routine CEM.
Patients with acute ischemic stroke or transient ischemic attack were consecutively enrolled. After a 12-channel ECG on admission, all patients received CEM. Additionally, in the second phase of the study the CEM traces of the patients underwent ACEM analysis using a software algorithm for AF detection. Patients with history of AF or with AF on the admission ECG were excluded.
The CEM (n = 208) and ACEM cohorts (n= 114) did not differ significantly regarding risk factors, duration of monitoring and length of admission. We found a higher rate of newly-detected AF in the ACEM cohort compared to the CEM cohort (15.8% versus 10.1%, P < .001). Median time to first detection of AF was shorter in the ACEM compared to the CEM cohort [10 hours (IQR 0-23) versus 46.50 hours (IQR 0-108.25), P < .001].
ACEM accelerates the detection of AF in patients with stroke compared with the routine CEM. Further evidences are required to confirm the increased rate of AF detected using ACEM.
快速、敏感地检测心房颤动（AF）对于中风后启动充分的预防治疗至关重要。卒中单元护理包括连续心电图监测（CEM），但记录心电图的最佳利用方式存在争议。在这项回顾性单中心研究中，我们研究了与常规 CEM 相比，基于软件算法的连续心电图监测（ACEM）自动分析是否能加速我们卒中单元患者 AF 的检测。
连续纳入急性缺血性卒中和短暂性脑缺血发作患者。入院时进行 12 导联心电图检查后，所有患者均接受 CEM。此外，在研究的第二阶段，使用 AF 检测软件算法对 CEM 轨迹进行 ACEM 分析。排除有 AF 病史或入院心电图有 AF 的患者。
CEM（n=208）和 ACEM 队列（n=114）在危险因素、监测持续时间和住院时间方面无显著差异。与 CEM 组相比，ACEM 组新检出 AF 的比例更高（15.8%比 10.1%，P<0.001）。ACEM 组首次检测到 AF 的中位时间较 CEM 组更短[10 小时（IQR 0-23）比 46.50 小时（IQR 0-108.25），P<0.001]。
与常规 CEM 相比，ACEM 可加速中风患者 AF 的检测。需要进一步的证据来证实使用 ACEM 检测到的 AF 发生率增加。
