Acute abdominal pain (AAP) constitutes 5–10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed. However, diagnostic errors and delays remain common. Although various machine learning (ML) models have been proposed to enhance appendicitis detection, none have been seamlessly integrated into the ED workflows for AAP or are specifically designed to diagnose appendicitis as early as possible within the clinical decision-making process. To mimic daily clinical practice, this proof-of-concept study aims to develop ML models that support decision-making using comprehensive clinical data up to key decision points in the ED workflow to detect appendicitis in patients presenting with AAP. Data from the Dutch triage system at the ED, vital signs, complete medical history and physical examination findings and routine laboratory test results were retrospectively extracted from 350 AAP patients presenting to the ED of a Dutch teaching hospital from 2016 to 2023. Two eXtreme Gradient Boosting ML models were developed to differentiate cases with appendicitis from other AAP causes: one model used all data up to and including physical examination, and the other was extended with routine laboratory test results. The performance of both models was evaluated on a validation set (n = 68) and compared to the Alvarado scoring system as well as three ED physicians in a reader study. The ML models achieved AUROCs of 0.919 without laboratory test results and 0.923 with the addition of laboratory test results. The Alvarado scoring system attained an AUROC of 0.824. ED physicians achieved AUROCs of 0.894, 0.826, and 0.791 without laboratory test results, increasing to AUROCs of 0.923, 0.892, and 0.859 with laboratory test results. Both ML models demonstrated comparable high accuracy in predicting appendicitis in patients with AAP, outperforming the Alvarado scoring system. The ML models matched or surpassed ED physician performance in detecting appendicitis, with the largest potential performance gain observed in absence of laboratory test results. Integration could assist ED physicians in early and accurate diagnosis of appendicitis.

中文翻译：

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基于机器学习的阑尾炎预测急诊科急性腹痛患者


急性腹痛 （AAP） 占所有急诊科 （ED） 就诊的 5-10%，阑尾炎是 AAP 的常见病因，通常需要手术干预。AAP 症状和原因的可变性，加上识别阑尾炎的挑战，使及时干预变得复杂。为了评估阑尾炎的风险，已经开发了 Alvarado 评分等评分系统。然而，诊断错误和延迟仍然很常见。尽管已经提出了各种机器学习 （ML） 模型来增强阑尾炎检测，但没有一个模型被无缝集成到 AAP 的 ED 工作流程中，也没有一个是专门为在临床决策过程中尽早诊断阑尾炎而设计的。为了模拟日常临床实践，这项概念验证研究旨在开发 ML 模型，以支持使用全面的临床数据进行决策，直到 ED 工作流程中的关键决策点，以检测 AAP 患者的阑尾炎。回顾性提取了 2016 年至 2023 年在荷兰一家教学医院急诊科就诊的 350 名 AAP 患者的急诊科荷兰分诊系统数据、生命体征、完整的病史和体格检查结果以及常规实验室检查结果。开发了两个 eXtreme Gradient Boosting ML 模型来区分阑尾炎病例和其他 AAP 原因：一个模型使用包括体格检查在内的所有数据，另一个模型使用常规实验室测试结果进行扩展。在验证集 （n = 68） 上评估了两种模型的性能，并与 Alvarado 评分系统以及读者研究中的三名急诊医生进行了比较。ML 模型在没有实验室测试结果的情况下实现了 0.919 的 AUROCs，0.923 加上实验室测试结果。Alvarado 评分系统的 AUROC 为 0.824。急诊科医生在没有实验室检查结果的情况下达到 0.894、0.826 和 0.791 的 AUROCs，在有实验室检查结果的情况下增加到 0.923、0.892 和 0.859。两种 ML 模型在预测 AAP 患者阑尾炎方面表现出相当高的准确性，优于 Alvarado 评分系统。ML 模型在检测阑尾炎方面与急诊医生的表现相当或超过，在没有实验室测试结果的情况下观察到的潜在性能提升最大。整合可以帮助急诊科医生及早准确诊断阑尾炎。