Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3239-3242. doi: 10.1109/EMBC48229.2022.9871092.
Screening for atrial fibrillation (AF) could reduce the incidence of stroke by identifying undiagnosed AF and prompting anticoagulation. However, screening may involve recording many electrocardiograms (ECGs) from each participant, several of which require manual review which is costly and time-consuming. The aim of this study was to investigate whether the number of ECG reviews could be reduced by using a model to prioritise ECGs for review, whilst still accurately diagnosing AF. A multiple logistic regression model was created to estimate the likelihood of an ECG exhibiting AF based on the mean RR-interval and variability in RR-intervals. It was trained on 1,428 manually labelled ECGs from 144 AF screening programme participants, and evaluated using 11,443 ECGs from 1,521 participants. When using the model to order ECGs for review, the number of reviews for AF participants was reduced by 74% since no further reviews are required after an AF ECG is identified; however, it did not impact the number of reviews in non-AF participants (the vast majority of participants), so the overall number of reviews was reduced by 3% with no missed AF diagnoses. When using the model to also exclude ECGs from review, the overall number of reviews was reduced by 28% with no missed AF diagnoses, and by 53% with only 4% of AF diagnoses missed. In conclusion, the workload can be reduced by using a model to prioritise ECGs for review. Ordering ECGs alone only provides only a moderate reduction in workload. The additional use of a threshold to exclude ECGs from review provides a much greater reduction in workload at the expense of some missed AF diagnoses. Clinical Relevance-This shows the potential benefit of using a model to prioritise electrocardiograms for review in order to reduce the manual workload of AF screening.
筛查心房颤动 (AF) 可以通过识别未诊断的 AF 并促使抗凝来降低中风的发生率。然而，筛查可能涉及记录每个参与者的许多心电图 (ECG)，其中一些需要手动审查，这既昂贵又耗时。本研究旨在探讨是否可以通过使用模型对 ECG 进行优先审查，同时仍准确诊断 AF，从而减少需要进行手动审查的 ECG 数量。创建了一个多逻辑回归模型，根据平均 RR 间隔和 RR 间隔的变异性来估计 ECG 出现 AF 的可能性。它基于来自 144 个 AF 筛查计划参与者的 1,428 个手动标记 ECG 进行训练，并使用来自 1,521 个参与者的 11,443 个 ECG 进行评估。当使用该模型对 ECG 进行审查排序时，由于一旦识别出 AF ECG，就无需再进行进一步的审查，因此 AF 参与者的审查次数减少了 74%；然而，这并不会影响非 AF 参与者（绝大多数参与者）的审查次数，因此总体审查次数减少了 3%，没有错过 AF 诊断。当使用该模型排除审查的 ECG 时，由于没有错过 AF 诊断，因此审查次数总体减少了 28%，而仅漏诊了 4%的 AF 诊断，则审查次数减少了 53%。总之，通过使用模型对 ECG 进行审查排序，可以减少工作量。仅对 ECG 进行排序只能适度减少工作量。额外使用阈值排除审查的 ECG 可以大大减少工作量，但代价是一些 AF 诊断可能会被遗漏。临床意义-这表明在 AF 筛查中使用模型对心电图进行优先排序以减少手动工作量的潜在益处。
