Copyright
©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
Processing time: 78 Days and 2 Hours
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 H2O.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.
Core Tip: Occult biliary lithiasis represents the largest monocausally treatable aetiology group within idiopathic acute pancreatitis cases. The identification of this subgroup protects patients from pancreatitis recurrences and over- or underdiagnosis. Based on 28 easy-to-collect and widely available patient variables, a machine learning-based prediction score can be used to predict the presence or absence of biliary sludge or microlithiasis in the context of pancreatitis hospitalisation. We provide a web-based prediction tool to select patients for endosonography to investigate microlithiasis or sludge as the cause of pancreatitis and treat them accordingly.
