دورية أكاديمية
Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology.
العنوان: | Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology. |
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المؤلفون: | Sirtl S; Department of Medicine II, LMU University Hospital, Munich 81377, Germany., Żorniak M; Department of Medicine II, LMU University Hospital, Munich 81377, Germany.; Department of Endoscopy, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice 44-113, Poland., Hohmann E; Department of Medicine II, LMU University Hospital, Munich 81377, Germany., Beyer G; Department of Medicine II, LMU University Hospital, Munich 81377, Germany., Dibos M; Department of Internal Medicine II, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich 81675, Germany., Wandel A; Department of Internal Medicine II, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich 81675, Germany., Phillip V; Department of Internal Medicine II, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich 81675, Germany., Ammer-Herrmenau C; Department of Gastroenterology, Gastrointestinal Oncology and Endocrinology, University Medical Center, Göttingen 37075, Germany., Neesse A; Department of Gastroenterology, Gastrointestinal Oncology and Endocrinology, University Medical Center, Göttingen 37075, Germany., Schulz C; Department of Medicine II, LMU University Hospital, Munich 81377, Germany., Schirra J; Department of Medicine II, LMU University Hospital, Munich 81377, Germany., Mayerle J; Department of Medicine II, LMU University Hospital, Munich 81377, Germany. julia.mayerle@med.uni-muenchen.de., Mahajan UM; Department of Medicine II, LMU University Hospital, Munich 81377, Germany. |
المصدر: | World journal of gastroenterology [World J Gastroenterol] 2023 Sep 21; Vol. 29 (35), pp. 5138-5153. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Baishideng Publishing Group Country of Publication: United States NLM ID: 100883448 Publication Model: Print Cited Medium: Internet ISSN: 2219-2840 (Electronic) Linking ISSN: 10079327 NLM ISO Abbreviation: World J Gastroenterol Subsets: MEDLINE |
أسماء مطبوعة: | Publication: 2014- : Pleasanton, CA : Baishideng Publishing Group Original Publication: Beijing : WJG Press, c1998- |
مواضيع طبية MeSH: | Endosonography* , Pancreatitis, Chronic*, Humans ; Retrospective Studies ; Acute Disease ; Patient Selection ; Sewage ; Machine Learning |
مستخلص: | 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 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. Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article. (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.) |
References: | United European Gastroenterol J. 2020 Apr;8(3):340-350. (PMID: 32213015) Clin Gastroenterol Hepatol. 2011 Nov;9(11):995-1000.e3. (PMID: 21723238) Pancreatology. 2017 Mar - Apr;17(2):155-165. (PMID: 28159463) BMC Med. 2015 Jan 06;13:1. (PMID: 25563062) Gastroenterology. 2022 Jan;162(1):122-134. (PMID: 34571026) Arch Surg. 1989 Nov;124(11):1295-6. (PMID: 2818183) J Clin Gastroenterol. 2015 Aug;49(7):594-8. (PMID: 25127114) J Clin Gastroenterol. 2003 Sep;37(3):238-50. (PMID: 12960724) BMJ Open. 2016 Nov 14;6(11):e012799. (PMID: 28137831) J Med Life. 2015 Oct-Dec;8(4):523-6. (PMID: 26664483) Endoscopy. 2020 Nov;52(11):955-964. (PMID: 32557477) Pancreatology. 2013 Jul-Aug;13(4 Suppl 2):e1-15. (PMID: 24054878) Z Gastroenterol. 2022 Mar;60(3):419-521. (PMID: 35263785) Br J Surg. 2020 Feb;107(3):191-199. (PMID: 31875953) AJR Am J Roentgenol. 2018 Nov;211(5):1035-1043. (PMID: 30160978) Front Cell Infect Microbiol. 2022 Jun 10;12:886935. (PMID: 35755847) Am J Emerg Med. 2021 Jun;44:85-91. (PMID: 33582613) BMJ Open. 2020 Aug 20;10(8):e035504. (PMID: 32819938) Am J Gastroenterol. 2000 Oct;95(10):3008-9. (PMID: 11051405) Gastrointest Endosc. 2010 Jan;71(1):1-9. (PMID: 20105473) Clin J Gastroenterol. 2019 Dec;12(6):511-524. (PMID: 31041651) World J Gastroenterol. 2007 Dec 21;13(47):6296-313. (PMID: 18081217) |
فهرسة مساهمة: | Keywords: Acute pancreatitis; Biliary pancreatitis; Endosonography; Idiopathic acute pancreatitis; Microlithiasis; Sludge |
المشرفين على المادة: | 0 (Sewage) |
تواريخ الأحداث: | Date Created: 20230925 Date Completed: 20230926 Latest Revision: 20230926 |
رمز التحديث: | 20240628 |
مُعرف محوري في PubMed: | PMC10514757 |
DOI: | 10.3748/wjg.v29.i35.5138 |
PMID: | 37744295 |
قاعدة البيانات: | MEDLINE |
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