Integrating clinical features, inflammatory markers, and immune profiles: a Yunke-based nomogram model for rheumatoid arthritis prognosis

TL;DR

This study creates a predictive model to help doctors tailor treatment for rheumatoid arthritis patients using clinical data, inflammation markers, and immune profiles.

Contribution

The first nomogram integrating clinical, inflammatory, and immune parameters for prognosis in Yunke-combined therapy for rheumatoid arthritis.

Findings

Six independent predictors were identified, including rheumatoid factor, CRP, swollen joints, TNF-α, IL-6, and CD3+ T cells.

The nomogram showed strong discrimination with C-indexes of 0.883 in training and 0.823 in validation cohorts.

The model achieved high sensitivity and specificity in predicting treatment response and supports personalized therapeutic monitoring.

Abstract

To develop a prognostic nomogram integrating clinical, inflammatory, and immune parameters for rheumatoid arthritis (RA) patients receiving Yunke-drug combination therapy, facilitating personalized treatment decisions.

We retrospectively analyzed 304 RA patients (2010–2024) divided into training (n = 213) and validation (n = 91) cohorts. Predictor selection through univariate/multivariate logistic regression informed nomogram construction. Model performance was assessed via ROC curves, calibration plots, and decision curve analysis (DCA).

Six independent predictors emerged: elevated rheumatoid factor (OR = 1.32, 1.08–1.62), CRP > 10 mg/L (OR = 2.14, 1.45–3.16), ≥4 swollen joints (OR = 1.87, 1.22–2.88), TNF-α > 8.1 pg./mL (OR = 2.05, 1.33–3.17), IL-6 > 15 pg./mL (OR = 1.94, 1.25–3.01), and CD3 + T cells <650/μL (OR = 1.76, 1.15–2.70) (all p < 0.05). The nomogram showed strong discrimination (C-index: 0.883 training; 0.823 validation) with AUCs of 0.881 (0.804–0.958) and 0.823 (0.679–0.966). Sensitivity/specificity reached 94.3%/90.7% (training) versus 78.3%/81.2% (validation). DCA confirmed clinical utility across probability thresholds (15–85%).

This first multifactorial nomogram for Yunke-combined therapy integrates joint assessments, serum biomarkers, cytokine profiles, and cellular immunity indicators. Demonstrated predictive accuracy (30.5% training; 29.7% validation response rates) supports its potential for therapeutic monitoring. While internally validated, multicenter studies are required to confirm generalizability. The model establishes a framework for precision RA management, with implications for dose optimization and resistance mechanism research.