{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:59:29Z","timestamp":1790657969582,"version":"4.1.0"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Type 2 diabetes mellitus (T2DM) combined with cardiovascular disease (CVD) is a leading cause of in-hospital mortality among older patients (\u2265 65 years). However, there is currently a lack of predictive models specifically designed to estimate in-hospital mortality in this high-risk population. It is crucial to provide clinicians with an effective tool to improve patient prognostic assessment and reduce in-hospital mortality rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study collected data from older patients with T2DM combined with CVD across six medical institutions in China between January 2012 and December 2024. The predictive performance of 16 composite indicators for in-hospital mortality risk was evaluated using receiver operating characteristic (ROC) curves. Key clinical variables were pinpointed by combining the least absolute shrinkage and selection operator (LASSO) method with multivariable logistic regression analysis. A nomogram was constructed based on these key variables and subsequently subjected to internal and external validation. Additionally, subgroup analyses were conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 42,693 older patients with T2DM and CVD were ultimately included in this study. The ROC curve revealed that neutrophil percentage to albumin ratio (NPAR) was the best predictor of in-hospital mortality risk [area under the curve (AUC)\u2009=\u20090.858]. Based on independent risk factors identified by LASSO-multivariate logistic regression, the in-hospital mortality risk nomogram [including age, pulmonary infection, NPAR, uric acid to high-density lipoprotein cholesterol ratio (UHR), and aspartate aminotransferase to alanine aminotransferase ratio (AST\/ALT)] was successfully constructed and validated with excellent predictive performance (AUC: 0.904, 0.878, and 0.889 in training, internal validation, and external validation sets, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study developed a reliable metabolic-inflammatory health-related nomogram for predicting in-hospital mortality risk among older patients with T2DM combined with CVD. The model demonstrated robust performance and offers a simple yet personalized predictive tool for clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1914467","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:46:15Z","timestamp":1790657175000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a novel metabolic-inflammatory health-related nomogram for predicting in-hospital mortality in older patients with type 2 diabetes mellitus combined with cardiovascular disease"],"prefix":"10.3389","volume":"13","author":[{"given":"Juntao","family":"Tan","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence Medicine, Chongqing Medical University","place":["Chongqing, 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