Pandiaraja Madhumitha, Brimicombe James, Cowie Martin, Dymond Andrew, Lindén Hannah Clair, Lip Gregory Y H, Mant Jonathan, Williams Kate, Charlton Peter H
Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London SW3 6LY, UK.
Eng Proc. 2020 Nov 14;2(1):78. doi: 10.3390/ecsa-7-08195. eCollection 2020.
Atrial fibrillation (AF) is a common irregular heart rhythm associated with a five-fold increase in stroke risk. It is often not recognised as it can occur intermittently and without symptoms. A promising approach to detect AF is to use a handheld electrocardiogram (ECG) sensor for screening. However, the ECG recordings must be manually reviewed, which is time-consuming and costly. Our aims were to: (i) evaluate the manual review workload; and (ii) evaluate strategies to reduce the workload. In total, 2141 older adults were asked to record their ECG four times per day for 1-4 weeks in the SAFER (Screening for Atrial Fibrillation with ECG to Reduce stroke) Feasibility Study, producing 162,515 recordings. Patients with AF were identified by: (i) an algorithm classifying recordings based on signal quality (high or low) and heart rhythm; (ii) a nurse reviewing recordings to correct algorithm misclassifications; and (iii) two cardiologists independently reviewing recordings from patients with any evidence of rhythm abnormality. It was estimated that 30,165 reviews were required (20,155 by the nurse, and 5005 by each cardiologist). The total number of reviews could be reduced to 24,561 if low-quality recordings were excluded from review; 18,573 by only reviewing ECGs falling under certain pathological classifications; and 18,144 by only reviewing ECGs displaying an irregularly irregular rhythm for the entire recording. The number of AF patients identified would not fall considerably: from 54 to 54, 54 and 53, respectively. In conclusion, simple approaches may help feasibly reduce the manual workload by 38.4% whilst still identifying the same number of patients with undiagnosed, clinically relevant AF.
心房颤动（AF）是一种常见的不规则心律，与中风风险增加五倍相关。它常常未被识别，因为其可能间歇性发作且没有症状。一种有前景的检测AF的方法是使用手持式心电图（ECG）传感器进行筛查。然而，ECG记录必须人工审核，这既耗时又昂贵。我们的目标是：（i）评估人工审核的工作量；（ii）评估减少工作量的策略。在SAFER（利用心电图筛查心房颤动以降低中风风险）可行性研究中，总共2141名老年人被要求每天记录4次ECG，持续1 - 4周，共产生162,515份记录。通过以下方式识别AF患者：（i）一种基于信号质量（高或低）和心律对记录进行分类的算法；（ii）一名护士审核记录以纠正算法的错误分类；（iii）两名心脏病专家独立审核有任何心律异常证据患者的记录。据估计需要进行30,165次审核（护士审核20,155次，每位心脏病专家审核5005次）。如果排除低质量记录进行审核，审核总数可减少至24,561次；仅审核某些病理分类下的ECG可减少至18,573次；仅审核整个记录显示不规则心律的ECG可减少至18,144次。识别出的AF患者数量不会大幅下降：分别从54例降至54例、54例和53例。总之，简单的方法可能有助于切实将人工工作量减少38.4%，同时仍能识别出相同数量的未诊断出的、具有临床相关性的AF患者。
