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Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis

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Liver Research 2021年第4期5卷 224-231页

作者： Camellia Dalai John M Azizian Harry Trieu Anand Rajan Formosa C Chen Tien Dong simon w beaven James H.Tabibian UCLA-Olive View Internal Medicine Residency Program Department of MedicineOlive View-UCLA Medical CenterSylmarCAUSA Keck School of Medicine University of Southern CaliforniaLos AngelesCAUSA Department of Surgery David Geffen School of Medicine at UCLALos AngelesCAUSA Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLALos AngelesCAUSA Division of Gastroenterology Department of MedicineOlive View-UCLA Medical CenterSylmarCAUSA

Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance *** aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography(ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models(MLMs).Methods:Clinical data of consecutive patients undergoing first-ever ERCP for suspected chol-edocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained *** logistic regression was used to identify predictors of ERCP-confirmed *** were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound(US)imaging only as well as using all available noninvasive imaging(US,computed tomography,and/or magnetic reso-nance cholangiopancreatography).The diagnostic performance of American Society for Gastrointestinal Endoscopy(ASGE)“high-likelihood”criteria was compared to ***:we identified 270 patients(mean age 46 years,62.2%female,73.7%Hispanic/Latino,59%with noninvasive imaging positive for choledocholithiasis)with native papilla who underwent ERCP for suspected choledocholithiasis,of whom 230(85.2%)were found to have ERCP-confirmed *** regression identified choledocholithiasis on noninvasive imaging(odds ratio(OR)¼3.045,P¼0.004)and common bile duct(CBD)diameter on noninvasive imaging(OR¼1.157,P¼0.011)as predictors of ERCP-confirmed *** the various MLMs trained,the random forest-based MLM performed best;sensitivity was 61.4%and 77.3%and specificity was 100%and 75.0%,using US-only and using all available imaging,*** high-likelihood criteria demonstrated sensitivity of 90.9%and specificity of 25.0%;using cut-points achieving this specificity,MLMs achieved sensitivity up to 97.7%.Conclusions:MLMs using age,sex,race/ethnicity,presence of diabetes,fever,body mass index(BMI),total bilirubin,maximum CBD di

关键词： Machine learning models(MLMs) Endoscopic retrograde cholangiopancreatography(ERCP) Noninvasive imaging Bile duct disorders Common bile duct stones Gallstones

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