{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:27:19Z","timestamp":1787318839552,"version":"build-2736575974"},"reference-count":30,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The American Surgeon\u2122"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Conversion of sleeve gastrectomy to Roux-en-Y gastric bypass is indicated primarily for unsatisfactory weight loss or gastroesophageal reflux disease (GERD). This study aimed to use a comprehensive database to define predictors of 30-day reoperation, readmission, reintervention, or mortality. An artificial neural network (ANN) was employed to optimize prediction of the composite endpoint (occurrence of 1+ morbid event).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Areview of 8895 patients who underwent conversion for weight-related or GERD-related indications was performed using the 2021 MBSAQIP national dataset. Demographics, comorbidities, laboratory values, and other factors were assessed for bivariate and subsequent multivariable associations with the composite endpoint ( P \u2264 .05). Factors considered in the multivariable model were imputed into a three-node ANN with 20% randomly withheld for internal validation, to optimize predictive accuracy. Models were compared using receiver operating characteristic (ROC) curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>39% underwent conversion for weight considerations and 61% for GERD. Rates of 30-day reoperation, readmission, reintervention, mortality, and the composite endpoint were 3.0%, 7.1%, 2.1%, .1%, and 9.1%, respectively. Of the nine factors associated with the composite endpoint on bivariate analysis, only non-white race ( P &lt; .001; odds ratio 1.4), lower body-mass index ( P &lt; .001; odds ratio .22), and therapeutic anticoagulation ( P = .001; odds ratio 2.0) remained significant upon multivariable analysis. Areas under ROC curves for the multivariable regression, ANN training, and validation sets were .587, .601, and .604, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Identification of risk factors for morbidity after conversion offers critical information to improve patient selection and manage postoperative expectations. ANN models, with appropriate clinical integration, may optimize prediction of morbidity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00031348241227182","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T10:30:01Z","timestamp":1704882601000},"page":"1202-1210","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of 30-Day Morbidity and Mortality After Conversion of Sleeve Gastrectomy to Roux-en-Y Gastric Bypass: Use of an Artificial Neural Network"],"prefix":"10.1177","volume":"90","author":[{"given":"Adam W.","family":"Scott","sequence":"first","affiliation":[{"name":"School of Medicine, University of Minnesota, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stuart K.","family":"Amateau","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Minnesota, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel B.","family":"Leslie","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Minnesota, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayeed","family":"Ikramuddin","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Minnesota, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric S.","family":"Wise","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Minnesota, Minneapolis, MN, 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