PICOS of the systematic review.

Background
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in the world. AF increases the risk of stroke 5-fold, though the risk can be reduced with appropriate treatment. Therefore, early diagnosis is imperative but remains a global challenge. In low-and middle-income countries (LMICs), a lack of diagnostic equipment and under-resourced healthcare systems generate further barriers. The rapid development of digital technologies that are capable of diagnosing AF remotely and cost-effectively could prove beneficial for LMICs. However, evidence is lacking on what digital technologies exist and how they compare in regards to diagnostic accuracy. We aim to systematically review the diagnostic accuracy of all digital technologies capable of AF diagnosis.
Methods
MEDLINE, Embase and Web of Science will be searched for eligible studies. Free text terms will be combined with corresponding index terms where available and searches will not be limited by language nor time of publication. Cohort or cross-sectional studies comprising adult (≥18 years) participants will be included. Only studies that use a 12-lead ECG as the reference test (comparator) and report outcomes of sensitivity, specificity, the diagnostic odds ratio (DOR) or the positive and negative predictive value (PPV and NPV) will be included (or if they provide sufficient data to calculate these outcomes). Two reviewers will independently assess articles for inclusion, extract data using a piloted tool and assess risk of bias using the QUADAS-2 tool. The feasibility of a meta-analysis will be determined by assessing heterogeneity across the studies, grouped by index device, diagnostic threshold and setting. If a meta-analysis is feasible for any index device, pooled sensitivity and specificity will be calculated using a random effect model and presented in forest plots.
Discussion
The findings of our review will provide a comprehensive synthesis of the diagnostic accuracy of available digital technologies capable for diagnosing AF. Thus, this review will aid in the identification of which devices could be further trialed and implemented, particularly in a LMIC setting, to improve the early diagnosis of AF.
Trial registration
Systematic review registration:
PROSPERO registration number is CRD42021290542. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542.

背景
心房颤动（Atrial fibrillation，以下简称AF）是全球最常见的心律失常。AF可使中风风险升高5倍，但通过规范治疗可有效降低该风险。因此，早期诊断至关重要，却仍是全球性的医疗难题。在中低收入国家（low-and middle-income countries，以下简称LMICs），诊断设备匮乏、医疗资源不足进一步加剧了诊断障碍。可实现远程、低成本AF诊断的数字技术的快速发展，或可对中低收入国家大有裨益。但目前尚缺乏相关证据，以明确现有数字技术的种类，以及它们在诊断准确性方面的表现差异。本研究旨在系统评价所有可用于AF诊断的数字技术的诊断准确性。

方法
将在MEDLINE、Embase及Web of Science数据库中检索符合纳入标准的研究。将自由文本术语与对应的索引术语（若有）相结合，检索不受语言及出版时间限制。纳入成年（≥18岁）参与者的队列研究或横断面研究。仅纳入以12导联心电图（12-lead ECG）作为参考标准（对照），并报告灵敏度、特异度、诊断比值比（diagnostic odds ratio，DOR）、阳性预测值（positive predictive value，PPV）及阴性预测值（negative predictive value，NPV）结果的研究（或提供足够数据以计算上述指标的研究）。将由2名研究者独立评估文献的纳入资格，使用经过预试验验证的数据提取工具，并采用QUADAS-2工具评估偏倚风险。荟萃分析的可行性将根据按索引设备、诊断阈值及研究场景分组的研究间异质性进行评估。若某类索引设备可开展荟萃分析，则采用随机效应模型计算合并灵敏度与特异度，并以森林图呈现结果。

讨论
本综述的研究结果将全面综合现有可用于AF诊断的数字技术的诊断准确性数据。因此，本综述将有助于明确哪些设备可进一步开展临床试验并投入临床应用，尤其是在中低收入国家场景中，以改善AF的早期诊断工作。

试验注册
系统评价注册信息：
PROSPERO注册编号为CRD42021290542。相关记录链接：https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542。