{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:16:58Z","timestamp":1787026618414,"version":"build-2736575974"},"reference-count":83,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to identify potential independent risk factors for rheumatoid arthritis (RA)- related mortality and develop a nomogram model to predict individualized mortality risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 310 RA patients from the National Health and Nutrition Examination Survey (NHANES) during 1999 - 2018. We applied LASSO, univariate, and multivariate logistic regression analyses to determine risk factors in the training cohort and construct a nomogram model. Calibration plots evaluated the nomogram\u2019s accuracy. Finally, we established the nomogram\u2019s clinical utility through DCA and performed internal validation within the training cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 310 patients, 140 experienced RA - related deaths, corresponding to a mortality rate of 45.16%. Within the training cohort, age, heart failure, and systemic inflammatory response index (SIRI) emerged as independent predictors of RA - related mortality. A nomogram model, constructed through multivariable logistic analysis, demonstrated an AUC of 0. 852 (95% CI: 0. 799 - 0. 904) in the training cohort and an AUC of 0. 904 (95% CI: 0. 846 - 0. 963) in the validation cohort. The calibration curve revealed a strong agreement between predicted and actual probabilities. In both training and validation cohorts, DCA highlighted the nomogram\u2019s significant net benefits for predicting RA - related mortality risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates age, heart failure, and SIRI\u2019s ability to predict RA mortality with good discrimination and clinical utility. The model gives clinicians a simple tool to quickly identify high - risk RA patients, promoting early intervention, personalized treatment, and better prognosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1592958","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T01:25:27Z","timestamp":1744853127000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and validation of a prognostic nomogram for predicting mortality risk in adult rheumatoid arthritis: an analysis of NHANES 1999\u20132018 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