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Predictive models could help clinicians identify individuals with obesity and overweight at greater risk for obesity-related complications, enabling personalized shared decision-making about obesity treatment. We previously developed a set of predictive models of adverse clinical outcomes of obesity and overweight using electronic health record (EHR) data of a large health system in Massachusetts. However, these models have not been validated on data from other sources.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We validated 12 predictive models for cardiometabolic, oncologic and musculoskeletal complications of excess weight using a linked EHR and claims dataset of a national patient cohort (Optum\n                      <jats:sup>\u00ae<\/jats:sup>\n                      Market Clarity) that included adults 18\u201380 years of age with body mass index (BMI)\u2009\u2265\u200925\u00a0kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      between 2007 and 2023.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A 5% randomly selected validation dataset included 437,223 patients (51.9% women) with a mean age of 51.4 years and a mean BMI of 31.9\u00a0kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      who were followed for a mean of 5.7 years. Most (11\/12) models achieved Harrell C-index\u2009\u2265\u20090.70. Most (11\/12) models had either 5-year or 10-year calibration slope within 0.3 of the ideal value of 1.0; 7 out of 12 had either 5-year or 10-year calibration slope that was not significantly different from 1.0; all had intercepts very close to 0.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A set of EHR-based predictive models of adverse outcomes of obesity and overweight was generalizable to a large national cohort. These findings suggest potential clinical and population health applications for predictive models in treatment of individual patients with obesity \/ overweight, as well as in population management and quality improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12902-026-02399-4","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:22:38Z","timestamp":1783686158000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive models of adverse outcomes of overweight or obesity: a nationwide cohort validation"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8609-564X","authenticated-orcid":false,"given":"Alexander","family":"Turchin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Shubina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fritha J.","family":"Morrison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia N.","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa M.","family":"Neff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Kan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"2399_CR1","unstructured":"Centers for Disease Control and Prevention. 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The study was approved by the Mass General Brigham institutional review board (protocol # 2021P003735), and the requirement for informed consent was waived.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Turchin reports consulting for Novo Nordisk and Proteomics International; research and travel support from Eli Lilly and Company; and research support from Novo Nordisk. Ahmad, Neff and Kan are employees and stockholders of Eli Lilly and Company. None of the other authors report any conflicts of interest.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"265"}}