Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial.

IF 7 1区医学 Q1 MEDICINE, GENERAL & INTERNAL

BMC Medicine Pub Date : 2025-06-09 DOI:10.1186/s12916-025-04190-z

Dung-Jang Tsai, Chin Lin, Wei-Ting Liu, Chiao-Chin Lee, Chiao-Hsiang Chang, Wen-Yu Lin, Yu-Lan Liu, Da-Wei Chang, Ping-Hsuan Hsieh, Chien-Sung Tsai, Yuan-Hao Chen, Yi-Jen Hung, Chin-Sheng Lin

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引用次数: 0

Abstract

Background: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early diagnosis of low EF among inpatient patients under non-cardiologist care.

Methods: We conducted a pragmatic randomized controlled trial at an academic medical center in Taiwan. 13,631 inpatient patients were randomized to either the intervention group (n = 6,840) receiving AI-generated ECG results or the control group (n = 6,791) following standard care. The primary outcome was the incidence of newly diagnosed low EF (≤ 50%) within 30 days following the ECG. Secondary outcomes included echocardiogram utilization rates, positive predictive value for low EF detection, and cardiology consultation rates. Statistical analysis included hazard ratios (HR) with 95% confidence intervals (CI) for time-to-event outcomes and chi-square tests for categorical variables.

Results: The intervention significantly increased the detection of newly diagnosed low EF in the overall cohort (1.5% vs. 1.1%, HR 1.50, 95% CI: 1.11-2.03, P = 0.023), with a more pronounced effect among AI-identified high-risk patients (13.0% vs. 8.9%, HR 1.55, 95% CI: 1.08-2.21). While overall echocardiogram utilization remained similar between groups (17.1% vs. 17.3%, HR 1.00, 95% CI: 0.92-1.09), the intervention group demonstrated higher positive predictive value for identifying low EF among patients receiving echocardiogram (34.2% vs. 20.2%, p < 0.001). Post-hoc analysis revealed increased cardiology consultation rates among high-risk patients in the intervention group (29.3% vs. 23.5%, p = 0.027).

Conclusions: Implementation of an AI-ECG algorithm enhanced the early diagnosis of low EF in the inpatient setting, primarily by improving diagnostic efficiency rather than increasing overall healthcare utilization. The tool was particularly effective in identifying high-risk patients who benefited from increased specialist consultation and more targeted diagnostic testing.

Trial registration: ClinicalTrials.gov Identifier: NCT05117970.

查看原文本刊更多论文

人工智能辅助诊断和预测低射血分数的心电图在住院部：一项实用的随机对照试验。

背景：尽管低射血分数（EF）是一种可治疗的疾病，但早期诊断仍然具有挑战性。本研究旨在评估基于心电图（ECG）的人工智能（AI）辅助临床决策支持工具在改善非心脏病专家护理的住院患者低EF的早期诊断中的有效性。方法：我们在台湾某学术医疗中心进行了一项实用的随机对照试验，将13631名住院患者随机分为干预组（n = 6840）和对照组（n = 6791），实验组接受人工智能生成的心电图结果，对照组接受标准治疗。主要终点为心电图检查后30天内新诊断的低EF发生率（≤50%）。次要结果包括超声心动图使用率、低EF检出率的阳性预测值和心脏病学咨询率。统计分析包括事件发生时间结局的风险比（HR）和95%置信区间（CI），以及分类变量的卡方检验。结果：在整个队列中，干预显著增加了新诊断的低EF的检出率（1.5%比1.1%,HR 1.50, 95% CI: 1.11-2.03, P = 0.023），在人工智能识别的高危患者中，干预的效果更为明显（13.0%比8.9%,HR 1.55, 95% CI: 1.08-2.21）。虽然两组之间超声心动图的总体利用率保持相似（17.1%对17.3%,HR 1.00, 95% CI: 0.92-1.09），但干预组在接受超声心动图检查的患者中识别低EF方面表现出更高的阳性预测值（34.2%对20.2%,p）。结论：实施AI-ECG算法增强了住院环境中低EF的早期诊断，主要是通过提高诊断效率而不是提高整体医疗保健利用率。该工具在识别高风险患者方面特别有效，这些患者受益于增加的专家咨询和更有针对性的诊断测试。试验注册：ClinicalTrials.gov标识符：NCT05117970。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Medicine 医学-医学：内科

CiteScore

13.10

自引率

1.10%

发文量

435

审稿时长

4-8 weeks

期刊介绍： BMC Medicine is an open access, transparent peer-reviewed general medical journal. It is the flagship journal of the BMC series and publishes outstanding and influential research in various areas including clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities. In addition to research articles, the journal also publishes stimulating debates, reviews, unique forum articles, and concise tutorials. All articles published in BMC Medicine are included in various databases such as Biological Abstracts, BIOSIS, CAS, Citebase, Current contents, DOAJ, Embase, MEDLINE, PubMed, Science Citation Index Expanded, OAIster, SCImago, Scopus, SOCOLAR, and Zetoc.