{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:49:03Z","timestamp":1792597743583,"version":"4.5.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Na\u00efve Bayes (NB).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The prevalence of an SSI was 3.21% (404 of 12\u2009596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed\/expected (O\/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O\/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605241293696","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T01:56:00Z","timestamp":1731894960000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?"],"prefix":"10.1177","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7701-9098","authenticated-orcid":false,"given":"Pongsathorn","family":"Piebpien","sequence":"first","affiliation":[{"name":"Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amarit","family":"Tansawet","sequence":"additional","affiliation":[{"name":"Department of Research and Medical Innovation, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oraluck","family":"Pattanaprateep","sequence":"additional","affiliation":[{"name":"Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anuchate","family":"Pattanateepapon","sequence":"additional","affiliation":[{"name":"Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chumpon","family":"Wilasrusmee","sequence":"additional","affiliation":[{"name":"Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gareth J.","family":"Mckay","sequence":"additional","affiliation":[{"name":"Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen\u2019s University Belfast, Belfast, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Attia","sequence":"additional","affiliation":[{"name":"School of Medicine and Public Health, and Hunter Medical Research Institute, University of Newcastle, New Lambton, New South Wales, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ammarin","family":"Thakkinstian","sequence":"additional","affiliation":[{"name":"Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"bibr1-03000605241293696","unstructured":"Centers for Disease Control and Prevention. Surgical Site Infection (SSI), https:\/\/www.cdc.gov\/surgical-site-infections\/about\/?CDC_AAref_Val=https:\/\/www.cdc.gov\/hai\/ssi\/ssi.html) (2010, accessed 29 April 2018 )."},{"key":"bibr2-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1089\/sur.2016.186"},{"key":"bibr3-03000605241293696","doi-asserted-by":"publisher","DOI":"10.4067\/S0716-10182015000200004"},{"key":"bibr4-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.10428"},{"key":"bibr5-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1089\/sur.2015.223"},{"key":"bibr6-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-016-3598-7"},{"key":"bibr7-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1308\/003588415X14181254788845"},{"key":"bibr8-03000605241293696","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-014-4078-8"},{"key":"bibr9-03000605241293696","unstructured":"Anderson DJ, Sexton DJ. Overview of control measures for prevention of surgical site infection in adults. Berman RS, Cochrane A (section eds). 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