Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy

Autor: Erik W. Ingwersen, Wessel T. Stam, Bono J.V. Meijs, Joran Roor, Marc G. Besselink, Bas Groot Koerkamp, Ignace H.J.T. de Hingh, Hjalmar C. van Santvoort, Martijn W.J. Stommel, Freek Daams
Přispěvatelé: Surgery, CCA - Cancer Treatment and quality of life, Amsterdam Gastroenterology Endocrinology Metabolism, CCA - Cancer Treatment and Quality of Life, CCA - Imaging and biomarkers, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Surgery (United States). Mosby Inc.
Surgery. Mosby Inc.
Dutch Pancreatic Cancer Group 2023, ' Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy ', Surgery (United States) . https://doi.org/10.1016/j.surg.2023.03.012
ISSN: 0039-6060
Popis: BACKGROUND: Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreatoduodenectomy.METHODS: This retrospective observational study used nationwide data from 16 centers in the Dutch Pancreatic Cancer Audit between January 2014 and January 2021. The area under the curve of a machine learning and logistic regression model for clinically relevant postoperative pancreatic fistula and delayed gastric emptying were compared.RESULTS: Overall, 799 (16.3%) patients developed a postoperative pancreatic fistula, and 943 developed (19.2%) delayed gastric emptying. For postoperative pancreatic fistula, the area under the curve of the machine learning model was 0.74, and the area under the curve of the logistic regression model was 0.73. For delayed gastric emptying, the area under the curve of the machine learning model and logistic regression was 0.59.CONCLUSION: Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.
Databáze: OpenAIRE
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