[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118095-en":3,"doc-seo-118095-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118095,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",7,"Healthcare","Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy","Machine learning is increasingly promoted for predicting postoperative complications, yet it remains uncertain whether it outperforms logistic regression when using structured clinical data. After pancreatoduodenectomy, postoperative pancreatic fistula and delayed gastric emptying are the two most frequent complications and strongly affect patient condition and hospital length of stay. This retrospective multi-center analysis compared prediction performance of machine learning and logistic regression using nationwide audit data.","Surgery 174 (2023) 435e440  \nContents lists available at ScienceDirect  \nSurgery  \njournal [homepage: www.elsevier.com/locate/surg](homepage: www.elsevier.com/locate/surg)  \n| Best in Surgery\u003Cbr>Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy\u003Cbr>Erik W. Ingwersen, MDa,b,c, Wessel T. Stam, MDa,b,c, Bono J.V. Meijs, MDa,b,c, Joran Roor, MScd, Marc G. Besselink, MD, PhDb,c,e, Bas Groot Koerkamp, MD, PhDf, Ignace H.J.T. de Hingh, MD, PhDg, Hjalmar C. van Santvoort, MD, PhDh,\u003Cbr>Martijn W.J. Stommel, MD, PhDi, Freek Daams, MD, PhDa,b, *, for the Dutch Pancreatic Cancer Group |  |  |  |\n| --- | --- | --- | --- |\n| a Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands b Cancer Center Amsterdam, the Netherlands\u003Cbr>c Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands d SAS institute B. V., Huizen, the Netherlands\u003Cbr>e Department of Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands f Erasmus MC, University Medical Center Rotterdam, the Netherlands\u003Cbr>g Catharina Cancer Institute, the Netherlands h UMC Utrecht Cancer Center, the Netherlands i Radboud University Medical Center, the Netherlands |  |  |  |\n| a r t i c l e i n f o |  | a b s t r a c t |  |\n| Article history:\u003Cbr>Accepted 20 March 2023 Available online 5 May 2023 |  | 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.\u003Cbr>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.\u003Cbr>Conclusion: Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.\u003Cbr>© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). |  |\n\nIntroduction  \nPancreatoduodenectomy is a complex surgical procedure with considerable morbidity and a negative influence on short-term quality  \n* Reprint requests: Freek Daams, MD, PhD, Amsterdam UMC, Department of Surgery, De Boelelaan 1117, 1081 HV Amsterdam, PO Box 7075, 1007 MB Amsterdam, The Netherlands.  \nE-mail address: [f.daams@amsterdamUMC.nl](f.daams@amsterdamUMC.nl) (F. Daams).  \nof life.1 It is a substantial burden for the health care resource use and health expenditure, especially in those with a complicated postoperative recovery.2,3 Postoperative pancreatic fistula (POPF) and delayed gastric emptying (DGE) are the two most common, highimpact complications after pancreatoduodenectomy, with sizable effects on resource use and prolonged length of stay.4,5  \nBoth patients and clinicians would benefit from an accurate prediction of POPF and DGE, and if","cbCaib6PtLbUOupG","https://ap.wps.com/l/cbCaib6PtLbUOupG","pdf",310774,1,6,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What postoperative complications were targeted in this study after pancreatoduodenectomy?\",\"answer\":\"The study focused on postoperative pancreatic fistula and delayed gastric emptying, the two most common complications with major effects on patient condition and length of hospital stay.\"},{\"question\":\"How was the prediction performance compared between machine learning and logistic regression?\",\"answer\":\"Both approaches were built using structured clinical data from a nationwide audit, and the area under the curve was used to compare model performance for clinically relevant outcomes.\"},{\"question\":\"Did machine learning outperform logistic regression for predicting complications?\",\"answer\":\"No. Machine learning did not outperform logistic regression in predicting postoperative complications after pancreatoduodenectomy, including pancreatic fistula and delayed gastric emptying.\"}]","Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy | 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postoperative complications were targeted in this study after pancreatoduodenectomy?","Question",{"text":75,"@type":76},"The study focused on postoperative pancreatic fistula and delayed gastric emptying, the two most common complications with major effects on patient condition and length of hospital stay.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the prediction performance compared between machine learning and logistic regression?",{"text":80,"@type":76},"Both approaches were built using structured clinical data from a nationwide audit, and the area under the curve was used to compare model performance for clinically relevant outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Did machine learning outperform logistic regression for predicting complications?",{"text":84,"@type":76},"No. Machine learning did not outperform logistic regression in predicting postoperative complications after pancreatoduodenectomy, including pancreatic fistula and delayed gastric 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