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

引用

世界胃肠病学杂志:英文版 2023年第35期29卷 5138-5153页

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 Department of Medicine II LMU University HospitalMunich 81377Germany Department of Endoscopy Maria Sklodowska-Curie National Research Institute of OncologyGliwice 44-113Poland Department of Internal Medicine II School of MedicineUniversity Hospital Rechts der IsarTechnical University of MunichMunich 81675Germany Department of Gastroenterology Gastrointestinal Oncology and EndocrinologyUniversity Medical CenterGöttingen 37075Germany

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 *** To develop a machine learning(ML)based decision tool for the use of endosonography(EUS)in pancreatitis patients to detect sludge and *** 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 *** 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 *** trained supervised ML classifiers using H_(2)*** automatically selecting the best suitable predictor model to predict microlithiasis/*** 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

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