| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
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 and Ujjwal Mukund Mahajan |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Deutsche Forschungsgemeinschaft (German Research Foundation) |
413635475 |
| United European Gastroenterology Research Fellowship |
|
|
| Corresponding Author |
Julia Mayerle, MD, Professor, Department of Medicine II, LMU University Hospital, Marchioninistraße 15, Munich 81377, Germany. julia.mayerle@med.uni-muenchen.de |
| Key Words |
Acute pancreatitis; Idiopathic acute pancreatitis; Biliary pancreatitis; Microlithiasis; Sludge; Endosonography |
| 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. |
| Publish Date |
2023-09-14 14:58 |
| Citation |
Sirtl S, Żorniak M, Hohmann E, Beyer G, Dibos M, Wandel A, Phillip V, Ammer-Herrmenau C, Neesse A, Schulz C, Schirra J, Mayerle J, Mahajan UM. Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology. World J Gastroenterol 2023; 29(35): 5138- 5153 |
| URL |
https://www.wjgnet.com/1007-9327/full/v29/i35/5138.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v29.i35.5138 |
