Puru Rattan, Joseph C Ahn, Beatriz Sordi Chara, Aidan F Mullan, Kan Liu, Zachi I Attia, Paul A Friedman, Alina Allen, Vijay H Shah, Patrick S Kamath, Peter A Noseworthy, Douglas A Simonetto
{"title":"用于检测晚期慢性肝病的心电图机器学习模型的训练和性能。","authors":"Puru Rattan, Joseph C Ahn, Beatriz Sordi Chara, Aidan F Mullan, Kan Liu, Zachi I Attia, Paul A Friedman, Alina Allen, Vijay H Shah, Patrick S Kamath, Peter A Noseworthy, Douglas A Simonetto","doi":"10.14309/ajg.0000000000003433","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD).</p><p><strong>Methods: </strong>A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier.</p><p><strong>Results: </strong>There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850-0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16-13.63).</p><p><strong>Discussion: </strong>An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.</p>","PeriodicalId":7608,"journal":{"name":"American Journal of Gastroenterology","volume":" ","pages":"2452-2456"},"PeriodicalIF":7.6000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Training and Performance of an Electrocardiogram-Enabled Machine Learning Model for Detection of Advanced Chronic Liver Disease.\",\"authors\":\"Puru Rattan, Joseph C Ahn, Beatriz Sordi Chara, Aidan F Mullan, Kan Liu, Zachi I Attia, Paul A Friedman, Alina Allen, Vijay H Shah, Patrick S Kamath, Peter A Noseworthy, Douglas A Simonetto\",\"doi\":\"10.14309/ajg.0000000000003433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD).</p><p><strong>Methods: </strong>A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier.</p><p><strong>Results: </strong>There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850-0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16-13.63).</p><p><strong>Discussion: </strong>An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.</p>\",\"PeriodicalId\":7608,\"journal\":{\"name\":\"American Journal of Gastroenterology\",\"volume\":\" \",\"pages\":\"2452-2456\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Gastroenterology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.14309/ajg.0000000000003433\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/3/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Gastroenterology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.14309/ajg.0000000000003433","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Training and Performance of an Electrocardiogram-Enabled Machine Learning Model for Detection of Advanced Chronic Liver Disease.
Introduction: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD).
Methods: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier.
Results: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850-0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16-13.63).
Discussion: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.
期刊介绍：
Published on behalf of the American College of Gastroenterology (ACG), The American Journal of Gastroenterology (AJG) stands as the foremost clinical journal in the fields of gastroenterology and hepatology. AJG offers practical and professional support to clinicians addressing the most prevalent gastroenterological disorders in patients.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
