A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study

医学 内镜逆行胰胆管造影术 接收机工作特性 模式 机器学习 Boosting(机器学习) 内窥镜检查 危险分层 队列 人工智能 医学物理学 放射科 外科 内科学 胰腺炎 计算机科学 社会科学 社会学
作者
Steven N. Steinway,Bo‐Hao Tang,Brian Caffo,Venkata S. Akshintala,Jeremy Telezing,Aditya Ashok,Ayesha Kamal,Chung Yao Yu,Nitin Jagtap,James Buxbaum,Joseph Elmunzer,Sachin Wani,Mouen A. Khashab
出处
期刊:Endoscopy [Georg Thieme Verlag KG]
卷期号:56 (03): 165-171 被引量:5
标识
DOI:10.1055/a-2174-0534
摘要

Abstract Background Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities. Methods A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting model (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in the GBM. Results 1378 patients (mean age 43.3 years; 61.2% female) were included in the GBM and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM had accuracy of 71.5% (SD 2.5%) (AUC 0.79 [SD 0.06]) and performed better than the 2019 American Society for Gastrointestinal Endoscopy (ASGE) guidelines (accuracy 62.4% [SD 2.6%]; AUC 0.63 [SD 0.03]) and European Society of Gastrointestinal Endoscopy (ESGE) guidelines (accuracy 62.8% [SD 2.6%]; AUC 0.67 [SD 0.02]). The GBM correctly categorized 22% of patients directed to unnecessary ERCP by ASGE guidelines, and appropriately recommended as the next management step 48% of ERCPs incorrectly rejected by ESGE guidelines. Conclusions A machine learning-based tool was created, providing real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directed ERCP use than existing ASGE and ESGE guidelines, and has the potential to reduce morbidity associated with ERCP or missed choledocholithiasis.
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