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©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology
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
Simon Sirtl, Michal Żorniak, Eric Hohmann, Georg Beyer, Christian Schulz, Jörg Schirra, Julia Mayerle, Ujjwal Mukund Mahajan, Department of Medicine II, LMU University Hospital, Munich 81377, Germany
Michal Żorniak, Department of Endoscopy, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice 44-113, Poland
Miriam Dibos, Annika Wandel, Veit Phillip, Department of Internal Medicine II, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich 81675, Germany
Christoph Ammer-Herrmenau, Albrecht Neesse, Department of Gastroenterology, Gastrointestinal Oncology and Endocrinology, University Medical Center, Göttingen 37075, Germany
Author contributions: Sirtl S, Żorniak M, Beyer G, Schulz C, Schirra J, Mayerle J, and Mahajan UM designed this study; Sirtl S, Żorniak M, Hohmann E, Dibos M, Wandel A, Phillip V, Ammer-Herrmenau C, Neesse A, Mayerle J, and Mahajan UM contributed to the data acquisition; Sirtl S, Żorniak M, Mayerle J, and Mahajan UM were involved in the data analysis, and manuscript and figure preparation; Mahajan UM participated in the algorithmic programming and statistical analysis; Beyer G, Schulz C, and Schirra J contributed to the technical advice; and all authors approved the final version of the manuscript.
Supported by 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.
Institutional review board statement: The study was approved by the Ethics Committee at LMU Munich (Project no.21 - 0126) and was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The Ethics Committees of the Technical University of Munich and the University Hospital of Göttingen gave their approval for the study to be conducted under the reference numbers 2022-628-S-KH (TUM) and 14/12/22 Ü (UMG).
Informed consent statement: Not necessary due to the retrospective study design.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All data relevant to the study are included in the article or uploaded as supplementary information.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Julia Mayerle, MD, Professor, Department of Medicine II, LMU University Hospital, Marchioninistraße 15, Munich 81377, Germany. julia.mayerle@med.uni-muenchen.de
Received: June 28, 2023
Peer-review started: June 28, 2023
First decision: July 23, 2023
Revised: August 6, 2023
Accepted: August 28, 2023
Article in press: August 28, 2023
Published online: September 21, 2023
ARTICLE HIGHLIGHTS
Research background
About 30% of acute pancreatitis (AP) cases classified as idiopathic actually have a biliary and thus monocausally treatable origin.
Research motivation
To date, there is no predictive score to differentiate between idiopathic and sludge- and microlithiasis-triggered acute biliary pancreatitis. Undiagnosed biliary pancreatitis aetiology poses the risk of overdiagnosis and additional patient burden. AP triggered by small biliary concrements (microlithiasis and sludge) is a particularly challenging diagnosis.
Research objectives
The aim of this study was to develop a machine-learning based prediction score for the presence of microlithiasis and sludge in AP patients. External score validation was performed at two university pancreas centres.
Research methods
The clinical and laboratory parameters of 218 AP patients were used to calculate a machine-learning based prediction model for the presence of sludge and microlithiasis. Forty-seven patients with endosonographic evidence of sludge and microlithiasis (and no other possible underlying pancreatitis aetiology) were used in the identification cohort and compared with 171 AP patients without endosonographic evidence of sludge and microlithiasis. We trained supervised machine learning classifiers using H2O.ai automatically selecting the best suitable predictor model to predict microlithiasis/sludge. An external pancreatitis cohort from two university pancreas centres with 117 patients was used for validation.
Research results
The score, constructed from a total of 28 simple variables to be collected in the early phase of pancreatitis-associated hospitalisation and validated externally at two university pancreas centres, can predict the presence of biliary sludge and microlithiasis with an accuracy of 0.7607 (95% confidence interval: 0.673-0.8347), positive predictive value of 0.7573, and negative predictive value of 0.7857.
Research conclusions
For the first time, we present a machine-learning based prediction score to differentiate between sludge- and microlithiasis-triggered AP and idiopathic pancreatitis. By using it in the early phase of pancreatitis-related hospitalisation, patient selection for or against the use of endosonography can support clinical decision-making.
Research perspectives
Upon prospective validation, the prediction score will aid in decision-making on which patient to subject to endosonography for diagnostic workup at a first episode of pancreatitis specifically to differentiate between sludge/microlithiasis-triggered and idiopathic AP.
