美国梅奥诊所Peter A Noseworthy团队研究了人工智能引导下窦性心律心电图筛查房颤的效果。这一研究成果于2022年9月27日发表在《柳叶刀》杂志上。

先前的房颤筛查试验强调了需要更具针对性的方法。研究组进行了一项实用性研究，以评估人工智能（AI）算法引导的靶向筛查方法在识别先前未识别的房颤方面的有效性。

在这项非随机干预试验中，研究组前瞻性地招募有中风危险因素但先前无房颤的患者，这些患者在常规实践中做了心电图（ECG）。参与者佩戴长达30天的连续动态心律监测仪，通过手机连接近实时的传输数据。人工智能算法应用于心电图，将患者分为高风险组或低风险组。主要结局是新诊断的房颤。在二次分析中，试验参与者的倾向评分与来自符合条件但未登记人群的个体（1:1）相匹配，他们作为现实世界的对照。

来自美国40个州的1003名平均年龄为74岁的患者完成了这项研究。在平均22.3天的连续监测中，370名低风险患者中有6名（1.6%）检测到房颤，633名高风险患者中48名（7.6%），比值比为4.98。与常规护理相比，在中位随访9.9个月后，AI引导筛查与房颤的检出率增加相关（高危组：常规护理时为3.6%，AI指导筛查时为10.6%，差异显著；低危组：分别为0.9%与2.4%，差异不显著）。

研究结果表明，利用现有临床数据的人工智能导向靶向筛查方法提高了房颤的检测率，并可提高房颤筛查的有效性。

附：英文原文

Title: Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

Author: Peter A Noseworthy, Zachi I Attia, Emma M Behnken, Rachel E Giblon, Katherine A Bews, Sijia Liu, Tara A Gosse, Zachery D Linn, Yihong Deng, Jun Yin, Bernard J Gersh, Jonathan Graff-Radford, Alejandro A Rabinstein, Konstantinos C Siontis, Paul A Friedman, Xiaoxi Yao

Issue&Volume: 2022-09-27

Abstract:

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.

DOI: 10.1016/S0140-6736(22)01637-3

Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(22)01637-3/fulltext#seccestitle10