Risk Factor Analysis and Predictive Nomogram for Heart Valve Calcification in Rheumatoid Arthritis.

BACKGROUND

To develop a predictive model for cardiac valve calcification (CVC) in rheumatoid arthritis (RA) patients using a novel nomogram approach.

METHODS

We analyzed data from patients diagnosed with RA at the Department of Rheumatology and Immunology, Nanchong Central Hospital, between January 1, 2020, and October 31, 2023. Data were gathered on patient demographics, disease characteristics, laboratory tests, and imaging findings. Patients were randomly divided into a training set (n = 210) and a validation set (n = 140), in a ratio of 6:4, respectively. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify risk predictors. Meanwhile, both single-factor and multi-factor logistic regression analyses were conducted to ascertain the risk factors associated with cardiac valve calcification. A predictive model was constructed using R software and validated through Bootstrap techniques. The performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 350 RA patients were included in the study, of whom 67 (19.1%) were diagnosed with CVC. Multivariate analysis identified several significant risk factors for CVC, including hypertension (odds ratio (OR) = 15.496, 95% confidence interval (CI): 4.373-54.916; p < 0.01), age (OR = 1.118, 95% CI: 1.003-1.246; p = 0.043), disease duration (OR = 1.238, 95% CI: 1.073-1.427; p = 0.003), and elevated erythrocyte sedimentation rate (ESR) (OR = 1.026, 95% CI: 1.006-1.047; p = 0.012). The predictive model demonstrated excellent discriminatory performance, with an AUC of 0.9474 (95% CI: 0.9044-0.9903) in the training set. The model also showed strong internal validity (C-index = 0.947) and maintained robust performance in external validation (AUC = 0.9390; 95% CI: 0.8880-0.9893). Calibration analysis further confirmed the predictive accuracy and reliability of the model.

CONCLUSION

The developed model can effectively identify RA patients at high risk for CVC. This tool provides a scientific basis for clinical decision-making and has significant potential for enhancing patient management and outcomes.