van de Leur Rutger R, van Sleuwen Meike T G M, Zwetsloot Peter-Paul M, van der Harst Pim, Doevendans Pieter A, Hassink Rutger J, van Es René
Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
Netherlands Heart Institute, Utrecht, The Netherlands.
Eur Heart J Digit Health. 2023 Nov 8;5(1):89-96. doi: 10.1093/ehjdh/ztad070. eCollection 2024 Jan.
Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.
Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%).
This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
能够正确解读心电图（ECG）的专业知识并非总是唾手可得。一种基于人工智能（AI）的分诊算法（DELTAnet），能够在心电图优先级排序方面为医生提供支持，有助于减轻当前心电图过度解读的后勤负担，并缩短急性和危及生命疾病的治疗时间。然而，此类人工智能算法临床应用的效果鲜有研究。
本前瞻性队列研究纳入了非心脏科接受心电图检查作为常规临床护理一部分的成年患者。DELTAnet用于将12导联心电图分类为以下分诊类别之一：正常、非急性异常、亚急性和急性。将其性能与基于最终临床诊断的分诊类别进行比较。此外，还研究了预测类别与临床结果之间的关联。共纳入1061例患者及心电图。将DELTAnet与临床分诊类别进行比较时，性能良好，平均一致性统计量为0.96（95%置信区间0.95 - 0.97）。此外，未遗漏任何需要改变政策或转诊至心脏病专家的心电图，且预测为急性但无需随访的病例数量有限（2.6%）。
本研究首次前瞻性地调查了基于心电图的人工智能分诊算法临床应用的影响。结果表明，DELTAnet在非心脏科医院科室用于12导联心电图分诊的临床实践中是有效且安全的。
