Background

Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation.

Methods

For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm monitor for up to 30 days, with the data transmitted in near real time through a cellular connection. The AI algorithm was applied to the ECGs to divide patients into high-risk or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In a secondary analysis, trial participants were propensity-score matched (1:1) to individuals from the eligible but unenrolled population who served as real-world controls. This study is registered with ClinicalTrials.gov, NCT04208971.

Findings

1003 patients with a mean age of 74 years (SD 8·8) from 40 US states completed the study. Over a mean 22·3 days of continuous monitoring, atrial fibrillation was detected in six (1·6%) of 370 patients with low risk and 48 (7·6%) of 633 with high risk (odds ratio 4·98, 95% CI 2·11–11·75, p=0·0002). Compared with usual care, AI-guided screening was associated with increased detection of atrial fibrillation (high-risk group: 3·6% [95% CI 2·3–5·4] with usual care vs 10·6% [8·3–13·2] with AI-guided screening, p<0·0001; low-risk group: 0·9% vs 2·4%, p=0·12) over a median follow-up of 9·9 months (IQR 7·1–11·0).

Interpretation

An AI-guided targeted screening approach that leverages existing clinical data increased the yield for atrial fibrillation detection and could improve the effectiveness of atrial fibrillation screening.

Funding

Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.

中文翻译：

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人工智能引导的窦性心律心电图筛查房颤：一项前瞻性非随机介入试验

背景

以前的心房颤动筛查试验强调需要更有针对性的方法。我们进行了一项务实的研究，以评估人工智能 (AI) 算法引导的靶向筛查方法在识别以前未被识别的心房颤动方面的有效性。

方法

对于这项非随机干预试验，我们前瞻性地招募了具有卒中危险因素但没有已知心房颤动的患者，他们在常规实践中进行了心电图 (ECG)。参与者佩戴连续动态心律监测器长达 30 天，数据通过蜂窝连接近乎实时地传输。人工智能算法应用于心电图，将患者分为高风险或低风险组。主要结果是新诊断的心房颤动。在次要分析中，试验参与者与来自符合条件但未注册人群的个人进行倾向得分匹配 (1:1)，这些人作为现实世界的对照。该研究已在 ClinicalTrials.gov 注册，NCT04208971。

发现

来自美国 40 个州的 1003 名平均年龄为 74 岁 (SD 8·8) 的患者完成了这项研究。在平均 22·3 天的连续监测中，370 名低风险患者中的 6 名 (1·6%) 和 633 名高风险患者中的 48 名 (7·6%) 检测到心房颤动（优势比 4·98, 95 % CI 2·11–11·75, p=0·0002)。与常规护理相比，AI 引导的筛查与增加房颤检出率相关（高危组：常规护理的 3·6% [95% CI 2·3–5·4] vs 10·6% [8· 3–13·2] 采用 AI 引导的筛查，p<0·0001；低风险组：0·9% vs 2·4%，p=0·12)，中位随访时间为 9·9 个月(IQR 7·1–11·0)。

解释

人工智能引导的靶向筛查方法利用现有临床数据提高了房颤检测的产量，并可以提高房颤筛查的有效性。

资金

Mayo Clinic Robert D 和 Patricia E Kern 医疗保健提供科学中心。