Department of Cardiology, Public Hospital Elisabethinen, Academic Teaching Hospital, Ordensklinikum A-4020 Linz, Fadingerstraße 1, Austria.
Department of Cardiology, Centre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Europace. 2018 Nov 1;20(FI_3):f321-f328. doi: 10.1093/europace/eux272.
Intermittent change in p-wave discernibility during periods of ectopy and sinus arrhythmia is a cause of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). To address this, we developed and validated an enhanced AF detection algorithm.
Atrial fibrillation detection in Reveal LINQ ICM uses patterns of incoherence in RR intervals and absence of P-wave evidence over a 2-min period. The enhanced algorithm includes P-wave evidence during RR irregularity as evidence of sinus arrhythmia or ectopy to adaptively optimize sensitivity for AF detection. The algorithm was developed and validated using Holter data from the XPECT and LINQ Usability studies which collected surface electrocardiogram (ECG) and continuous ICM ECG over a 24-48 h period. The algorithm detections were compared with Holter annotations, performed by multiple reviewers, to compute episode and duration detection performance. The validation dataset comprised of 3187 h of valid Holter and LINQ recordings from 138 patients, with true AF in 37 patients yielding 108 true AF episodes ≥2-min and 449 h of AF. The enhanced algorithm reduced inappropriately detected episodes by 49% and duration by 66% with <1% loss in true episodes or duration. The algorithm correctly identified 98.9% of total AF duration and 99.8% of total sinus or non-AF rhythm duration. The algorithm detected 97.2% (99.7% per-patient average) of all AF episodes ≥2-min, and 84.9% (95.3% per-patient average) of detected episodes involved AF.
An enhancement that adapts sensitivity for AF detection reduced inappropriately detected episodes and duration with minimal reduction in sensitivity.
在异位节律和窦性心律失常期间，p 波可辨别性的间歇性变化是导致可植入式心脏监测器（ICM）中不适当的房颤（AF）检测的原因。为了解决这个问题，我们开发并验证了一种增强的 AF 检测算法。
Reveal LINQ ICM 中的房颤检测使用 RR 间期不连贯性模式和 2 分钟内无 P 波证据来检测。增强的算法包括在 RR 不规则期间的 P 波证据，作为窦性心律失常或异位的证据，以自适应地优化 AF 检测的灵敏度。该算法是使用来自 XPECT 和 LINQ 可用性研究的 Holter 数据开发和验证的，这些研究在 24-48 小时内收集了体表心电图（ECG）和连续的 ICM ECG。算法检测结果与多位审核员进行的 Holter 注释进行比较，以计算发作和持续时间检测性能。验证数据集包括来自 138 名患者的 3187 小时有效 Holter 和 LINQ 记录，其中 37 名患者存在真实的 AF，产生了 108 个≥2 分钟的真实 AF 发作和 449 小时的 AF。增强算法将不适当的检测发作减少了 49%，持续时间减少了 66%，而真实发作或持续时间的损失小于 1%。该算法正确识别了 98.9%的总 AF 持续时间和 99.8%的总窦性或非 AF 节律持续时间。该算法检测到了 97.2%（每个患者平均 99.7%）的所有≥2 分钟的 AF 发作，84.9%（每个患者平均 95.3%）的检测发作涉及 AF。
一种适应 AF 检测灵敏度的增强功能减少了不适当的检测发作和持续时间，同时对灵敏度的降低最小化。
