Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology  

作　　者：Simon Sirtl Michal Żorniak Eric Hohmann Georg Beyer Miriam Dibos Annika Wandel Veit Phillip Christoph Ammer-Herrmenau Albrecht Neesse Christian Schulz Jörg Schirra Julia Mayerle Ujjwal Mukund Mahajan 

机构地区：[1]Department of Medicine II,LMU University Hospital,Munich 81377,Germany [2]Department of Endoscopy,Maria Sklodowska-Curie National Research Institute of Oncology,Gliwice 44-113,Poland [3]Department of Internal Medicine II,School of Medicine,University Hospital Rechts der Isar,Technical University of Munich,Munich 81675,Germany [4]Department of Gastroenterology,Gastrointestinal Oncology and Endocrinology,University Medical Center,Göttingen 37075,Germany

出　　处：《World Journal of Gastroenterology》2023年第35期5138-5153,共16页世界胃肠病学杂志（英文版）

基　　金：the Deutsche Forschungsgemeinschaft(German Research Foundation),No.413635475 to Sirtl S;the LMU Munich Clinician Scientist Program;Żorniak M is supported by the United European Gastroenterology Research Fellowship.

摘　　要：BACKGROUND Biliary microlithiasis/sludge is detected in approximately 30%of patients with idiopathic acute pancreatitis(IAP).As recurrent biliary pancreatitis can be prevented,the underlying aetiology of IAP should be established.AIM To develop a machine learning(ML)based decision tool for the use of endosonography(EUS)in pancreatitis patients to detect sludge and microlithiasis.METHODS We retrospectively used routinely recorded clinical and laboratory parameters of 218 consecutive patients with confirmed AP admitted to our tertiary care hospital between 2015 and 2020.Patients who did not receive EUS as part of the diagnostic work-up and whose pancreatitis episode could be adequately explained by other causes than biliary sludge and microlithiasis were excluded.We trained supervised ML classifiers using H_(2)O.ai automatically selecting the best suitable predictor model to predict microlithiasis/sludge.The predictor model was further validated in two independent retrospective cohorts from two tertiary care centers(117 patients).RESULTS Twenty-eight categorized patients’variables recorded at admission were identified to compute the predictor model with an accuracy of 0.84[95%confidence interval(CI):0.791-0.9185],positive predictive value of 0.84,and negative predictive value of 0.80 in the identification cohort(218 patients).In the validation cohort,the robustness of the prediction model was confirmed with an accuracy of 0.76(95%CI:0.673-0.8347),positive predictive value of 0.76,and negative predictive value of 0.78(117 patients).CONCLUSION We present a robust and validated ML-based predictor model consisting of routinely recorded parameters at admission that can predict biliary sludge and microlithiasis as the cause of AP.

关 键 词：Acute pancreatitis Idiopathic acute pancreatitis Biliary pancreatitis MICROLITHIASIS SLUDGE ENDOSONOGRAPHY 

分 类 号：R576[医药卫生—消化系统]