Development and Validation of a Prognostic Nomogram for Predicting Mortality Risk in Adult Rheumatoid Arthritis: An Analysis of NHANES 1999-2018 Data

Xiao Chen Haibo Gong Jing Chen ^* Yuan Luo

• The First People's Hospital of Neijiang, Neijiang, China

Objective: 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. Methods: 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's accuracy. Finally, we established the nomogram's clinical utility through DCA and performed internal validation within the training cohort. Results: 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's significant net benefits for predicting RA -related mortality risk. Conclusions: This study demonstrates age, heart failure, and SIRI's 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.