Department of Neurology, Evangelisches Klinikum Bethel, EvKB, Bielefeld, Bielefeld, Germany,
Department of Neurology, Evangelisches Klinikum Bethel, EvKB, Bielefeld, Bielefeld, Germany.
Cerebrovasc Dis. 2020;49(6):647-655. doi: 10.1159/000511563. Epub 2020 Nov 18.
Detection of atrial fibrillation (AF) is one of the primary diagnostic goals for patients on a stroke unit. Physician-based manual analysis of continuous ECG monitoring is regarded as the gold standard for AF detection but requires considerable resources. Recently, automated computer-based analysis of RR intervals was established to simplify AF detection. The present prospective study analyzes both methods head to head regarding AF detection specificity, sensitivity, and overall effectiveness.
Consecutive stroke patients without history of AF or proof of AF in the admission ECG were enrolled over the period of 7 months. All patients received continuous ECG telemetry during the complete stay on the stroke unit. All ECGs underwent automated analysis by a commercially available program. Blinded to these results, all ECG tracings were also assessed manually. Sensitivity, specificity, time consumption, costs per day, and cost-effectiveness were compared.
216 consecutive patients were enrolled (70.7 ± 14.1 years, 56% male) and 555 analysis days compared. AF was detected by manual ECG analysis on 37 days (6.7%) and automatically on 57 days (10.3%). Specificity of the automated algorithm was 94.6% and sensitivity 78.4% (28 [5.0%] false positive and 8 [1.4%] false negative). Patients with AF were older and had more often arterial hypertension, higher NIHSS at admission, more often left atrial dilatation, and a higher CHA2DS2-VASc score. Automation significantly reduced human resources but was more expensive compared to manual analysis alone.
Automatic AF detection is highly specific, but sensitivity is relatively low. Results of this study suggest that automated computer-based AF detection should be rather complementary to manual ECG analysis than replacing it.
心房颤动（AF）的检测是卒中单元患者的主要诊断目标之一。基于医生的连续心电图监测手动分析被认为是 AF 检测的金标准，但需要大量资源。最近，已经建立了基于自动计算机的 RR 间隔分析来简化 AF 检测。本前瞻性研究对头对头比较了这两种方法在 AF 检测特异性、敏感性和总体有效性方面的表现。
连续 7 个月，纳入无 AF 病史或入院心电图证明 AF 的卒中患者。所有患者在卒中单元期间均接受连续心电图遥测。所有心电图均由商业可用程序进行自动分析。在不了解这些结果的情况下，所有心电图描记也进行手动评估。比较了敏感性、特异性、时间消耗、每天的成本和成本效益。
共纳入 216 例连续患者（70.7±14.1 岁，56%为男性），共比较了 555 个分析日。手动心电图分析在 37 天（6.7%）和自动分析在 57 天（10.3%）检测到 AF。自动算法的特异性为 94.6%，敏感性为 78.4%（28 次[5.0%]假阳性和 8 次[1.4%]假阴性）。患有 AF 的患者年龄较大，更常患有动脉高血压，入院时 NIHSS 更高，更常伴有左心房扩张，CHA2DS2-VASc 评分更高。自动化显著减少了人力资源，但与单独的手动分析相比成本更高。
自动 AF 检测具有高度特异性，但敏感性相对较低。本研究结果表明，自动计算机化的 AF 检测应是对手动心电图分析的补充，而不是替代。
