{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T19:30:16Z","timestamp":1793388616239,"version":"4.5.0"},"reference-count":23,"publisher":"American Medical Association (AMA)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Surg"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:sec id=\"ab-soi260019-4\">\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Despite advances in surgical techniques and care, postoperative complications are prevalent and affect up to 15% of patients who undergo inpatient surgery. Analytic models for surgical risk estimation can benefit from incorporating complex electronic health record data across multiple, diverse settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-soi260019-5\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To test the hypothesis that applying the MySurgeryRisk framework, developed and prospectively validated on a single-center dataset, to a large multicenter dataset will enhance generalizability without degrading the predictive performance achieved by the original, single-center model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-soi260019-6\">\n                    <jats:title>Design, Setting, and Participants<\/jats:title>\n                    <jats:p>This retrospective, longitudinal, multicenter cohort analysis included 508\u202f097 encounters from 366\u202f875 adult patients admitted and underwent major inpatient operations at 14 health care institutions within the OneFlorida\u2009+\u2009network from 2012 through 2023. Models were trained on a development set (2012-2020; n\u2009=\u2009358\u202f216 encounters) and evaluated on a validation set (2020-2023; n\u2009=\u2009149\u202f881 encounters). These data were analyzed from January 2025 to February 2026.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-soi260019-7\">\n                    <jats:title>Main Outcomes and Measures<\/jats:title>\n                    <jats:p>Using the feature selection and transformation methods validated in the MySurgeryRisk framework, eXtreme Gradient Boosting models were developed and validated to predict the postoperative risk of intensive care unit (ICU) admission, postoperative mechanical ventilation (MV), postoperative acute kidney injury (AKI), and in-hospital mortality. Performance was primarily evaluated using area under the receiver operating characteristics curve (AUROC) values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-soi260019-8\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 366\u202f875 total patients who underwent 508\u202f097 major inpatient operations, the mean (SD) age was 59 (18) years; 190\u202f799 (52%) were women and 176\u202f076 were men (48%). The prevalence of complications was 8% (n\u2009=\u200942\u202f302) for ICU admission, 4% (n\u2009=\u200920\u202f435) for MV, 7% (n\u2009=\u200936\u202f027) for AKI, and 1% (n\u2009=\u20095131) for in-hospital mortality. AUROC values were as follows: ICU admission, 0.93 (95% CI, 0.93-0.93); MV, 0.94 (95% CI, 0.94-0.94); AKI, 0.92 (95% CI, 0.92-0.92); and in-hospital mortality, 0.95 (95% CI, 0.94-0.95). Model predictive performance was comparable with previously validated MySurgeryRisk models. Primary procedure code and clinician-specific factors were consistently the most influential variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-soi260019-9\">\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>A model using routinely collected variables from a large multicenter cohort was developed and validated, demonstrating accurate prediction of postoperative complications and death with generalizability across a large network of health care institutions. Procedure type and clinician-specific factors were the most influential contributors to predicted risk, providing insights into factors influencing surgical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1001\/jamasurg.2026.1112","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:30:58Z","timestamp":1777476658000},"page":"619","source":"Crossref","is-referenced-by-count":3,"title":["MySurgeryRisk Model Predictions of Postoperative Complications and Mortality"],"prefix":"10.1001","volume":"161","author":[{"given":"Yuanfang","family":"Ren","sequence":"first","affiliation":[{"name":"Intelligent Clinical Care Center, University of Florida, Gainesville, Florida"},{"name":"Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, 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Transplantation, Department of Medicine, University of Florida, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler J.","family":"Loftus","sequence":"additional","affiliation":[{"name":"Intelligent Clinical Care Center, University of Florida, Gainesville, Florida"},{"name":"Department of Surgery, University of Florida, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Shickel","sequence":"additional","affiliation":[{"name":"Intelligent Clinical Care Center, University of Florida, Gainesville, Florida"},{"name":"Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parisa","family":"Rashidi","sequence":"additional","affiliation":[{"name":"Intelligent Clinical Care Center, University of Florida, Gainesville, Florida"},{"name":"Department of Biomedical 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