Schipper Anoeska, Belgers Peter, O'Connor Rory, Jie Kim Ellis, Dooijes Robin, Bosma Joeran Sander, Kurstjens Steef, Kusters Ron, van Ginneken Bram, Rutten Matthieu
Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
Department of Radiology, Jeroen Bosch Hospital, 's Hertogenbosch, the Netherlands.
World J Emerg Surg. 2024 Dec 23;19(1):40. doi: 10.1186/s13017-024-00570-7.
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.
急性腹痛（AAP）占急诊科（ED）就诊病例的5%-10%，阑尾炎是常见的AAP病因，常需手术干预。AAP症状和病因的多样性，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，已开发了如阿尔瓦拉多评分等评分系统。然而，诊断错误和延误仍然很常见。尽管已提出各种机器学习（ML）模型来提高阑尾炎检测率，但尚无模型无缝集成到AAP的ED工作流程中，或专门设计用于在临床决策过程中尽早诊断阑尾炎。为模拟日常临床实践，本概念验证研究旨在开发ML模型，该模型利用ED工作流程中关键决策点之前的综合临床数据来支持决策，以检测AAP患者中的阑尾炎。
回顾性提取了2016年至2023年在一家荷兰教学医院急诊科就诊的350例AAP患者的荷兰分诊系统数据、生命体征、完整病史、体格检查结果和常规实验室检查结果。开发了两个极端梯度提升ML模型，以区分阑尾炎病例与其他AAP病因：一个模型使用直至并包括体格检查的所有数据，另一个模型扩展了常规实验室检查结果。在一个验证集（n = 68）上评估了两个模型的性能，并在一项读者研究中与阿尔瓦拉多评分系统以及三名急诊科医生进行了比较。
未添加实验室检查结果时，ML模型的曲线下面积（AUROC）为0.919，添加实验室检查结果后为0.923。阿尔瓦拉多评分系统的AUROC为0.824。急诊科医生在未添加实验室检查结果时的AUROC分别为0.894、0.826和0.791，添加实验室检查结果后分别增至0.923、0.892和0.859。
两个ML模型在预测AAP患者的阑尾炎方面均表现出相当高的准确性，优于阿尔瓦拉多评分系统。ML模型在检测阑尾炎方面与急诊科医生的表现相当或更优，在未进行实验室检查结果时观察到的潜在性能提升最大。整合ML模型可协助急诊科医生早期准确诊断阑尾炎。
