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<p>Advanced chronic liver disease (CLD) affects 2–5% of the general population, and accessible screening tools are needed in primary care. Here we conducted a pragmatic trial to assess whether an electrocardiogram (ECG)-based machine learning (ECG-ML) model enables early detection of advanced CLD. In this trial, 98 primary care teams were cluster randomized to intervention (access to ECG-ML results; 123 clinicians) or usual care (122 clinicians). Clinicians in the intervention arm were notified of a positive ECG-ML result, indicating higher risk of advanced CLD. The primary endpoint was new diagnosis of CLD with advanced fibrosis within 180 days of ECG, confirmed by sequential liver disease assessments. A total of 15,596 adults underwent 12-lead ECGs as part of routine care and met inclusion criteria (<i>N</i> = 8,034 intervention and <i>N</i> = 7,562 control). 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, <i>P</i> = 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, <i>P</i> &lt; 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, <i>P</i> &lt; 0.001) and among ECG-ML-positive patients (8.4% versus 1.1%; OR 8.03, 95% CI 3.50–18.4, <i>P</i> &lt; 0.001). The diagnostic yield below epidemiological estimates probably reflects variable clinician adherence to artificial intelligence-driven recommendations. 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. ClinicalTrials.gov registration: <a href="https://clinicaltrials.gov/study/NCT05782283">NCT05782283</a>.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Douglas A. Simonetto,
  • David Rushlow,
  • Kan Liu,
  • Alberto Calleri,
  • Blake A. Kassmeyer,
  • Ryan J. Lennon,
  • Puru Rattan,
  • Matthew E. Bernard,
  • Gagandeep Singh,
  • Mark E. Deyo-Svendsen,
  • Graham King,
  • Stephen K. Stacey,
  • Amy Olofson,
  • Alina Allen,
  • Joseph C. Ahn,
  • Paul A. Friedman,
  • Patrick S. Kamath,
  • Zachi I. Attia,
  • Peter A. Noseworthy,
  • Vijay H. Shah

摘要

Advanced chronic liver disease (CLD) affects 2–5% of the general population, and accessible screening tools are needed in primary care. Here we conducted a pragmatic trial to assess whether an electrocardiogram (ECG)-based machine learning (ECG-ML) model enables early detection of advanced CLD. In this trial, 98 primary care teams were cluster randomized to intervention (access to ECG-ML results; 123 clinicians) or usual care (122 clinicians). Clinicians in the intervention arm were notified of a positive ECG-ML result, indicating higher risk of advanced CLD. The primary endpoint was new diagnosis of CLD with advanced fibrosis within 180 days of ECG, confirmed by sequential liver disease assessments. 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). 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). 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). 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). The diagnostic yield below epidemiological estimates probably reflects variable clinician adherence to artificial intelligence-driven recommendations. 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. ClinicalTrials.gov registration: NCT05782283.