Development and validation of a prognostic nomogram for predicting mortality risk in adult rheumatoid arthritis: an analysis of NHANES 1999–2018 data

TL;DR

This study developed a tool to predict mortality risk in rheumatoid arthritis patients using data from 1999 to 2018.

Contribution

A new nomogram model was created to predict RA-related mortality using age, heart failure, and SIRI.

Findings

Age, heart failure, and SIRI were identified as independent predictors of RA-related mortality.

The nomogram model showed strong discrimination with an AUC of 0.852 in training and 0.904 in validation.

Calibration and decision curve analysis confirmed the model's accuracy and clinical utility.

Abstract

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.

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.

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.

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.

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