Detection of undiagnosed liver cirrhosis via AI-enabled electrocardiogram: a pragmatic, cluster-randomized clinical trial.

Here we conducted a pragmatic trial to assess whether an electrocardiogram (ECG)-based machine learning (ECG-ML) model enables early detection of advanced CLD.

在这里，我们进行了一项实用性试验，以评估基于心电图（ECG）的机器学习（ECG-ML）模型是否能够实现晚期CLD的早期检测。

In this trial, 98 primary care teams were cluster randomized to intervention (access to ECG-ML results; 123 clinicians) or usual care (122 clinicians).

在这项试验中，98个初级护理团队被随机分为干预组（可获取心电图机器学习结果；123名临床医生）或常规护理组（122名临床医生）。

Clinicians in the intervention arm were notified of a positive ECG-ML result, indicating higher risk of advanced CLD.

干预组的临床医生被告知阳性的心电图机器学习结果，这表明有更高的晚期慢性肝病（CLD）风险。

The primary endpoint was new diagnosis of CLD with advanced fibrosis within 180 days of ECG, confirmed by sequential liver disease assessments.

主要终点是在心电图（ECG）后180天内，通过连续的肝病评估确认的慢性肝病（CLD）伴有进展性纤维化的首次诊断。

A total of 15,596 adults underwent 12-lead ECGs as part of routine care and met inclusion criteria (N = 8,034 intervention and N = 7,562 control).

共有15,596名成人进行了12导联心电图（ECG）作为常规护理的一部分，并符合纳入标准（N = 8,034干预组和N = 7,562对照组）。

The intervention significantly increased new diagnoses of advanced CLD in the overall cohort (1.0% versus 0.5% in the control arm; odds ratio (OR) 2.09, 95% confidence interval(CI) 1.22-3.55, P = 0.007).

干预措施显著增加了整个队列中晚期慢性肝病（CLD）的新诊断（干预组为1.0%，对照组为0.5%；比值比（OR）为2.09，95%置信区间（CI）为1.22-3.55，P = 0.007）。

Among ECG-ML-positive patients, advanced CLD was more frequent in the intervention arm (4.4% versus 1.1%; OR 4.37, 95% CI 1.94-9.88, P < 0.001).

在心电图-机器学习（ECG-ML）阳性的患者中，干预组的晚期慢性肝病（CLD）更为常见（4.4%对比1.1%；比值比（OR）为4.37，95%置信区间（CI）为1.94-9.88，P < 0.001）。

The intervention also increased the detection of any fibrosis (secondary endpoint) in the overall cohort (1.7% versus 0.5%; OR 3.17, 95% CI 1.86-5.40, P < 0.001) and among ECG-ML-positive patients (8.4% versus 1.1%; OR 8.03, 95% CI 3.50-18.4, P < 0.001).

干预措施还增加了在整体队列中检测到任何纤维化（次要终点）的比例（1.7%对比0.5%；比值比3.17，95%置信区间1.86-5.40，P<0.001）以及在ECG-ML阳性患者中（8.4%对比1.1%；比值比8.03，95%置信区间3.50-18.4，P<0.001）。

The diagnostic yield below epidemiological estimates probably reflects variable clinician adherence to artificial intelligence-driven recommendations.

低于流行病学估计的诊断效率可能反映了临床医生对人工智能驱动建议的依从性不一。

These results demonstrate that an ECG-based machine learning model, followed by targeted testing based on risk factors, may aid case finding of advanced CLD in routine primary care.

这些结果表明，基于心电图的机器学习模型，结合基于风险因素的针对性测试，可能有助于在常规初级保健中发现晚期慢性肝病病例。

ClinicalTrials.gov registration: NCT05782283 .

ClinicalTrials.gov 注册：NCT05782283。