Tongue feature-based model for assessing disease activity in patients with rheumatoid arthritis

Tongue features, which are emerging imaging-based biomarkers, have been integrated into predictive models for various diseases. However, their role in assessing rheumatoid arthritis (RA) activity remains unexplored. This study aims to develop a clinically applicable model for assessing RA activity by analyzing the relationship between tongue features and laboratory indicators.

We enrolled 227 patients who visited the Department of Traditional Chinese Medicine Rheumatology, China-Japan Friendship Hospital, from April 2021 to March 2023. Patients were stratified into remission/low-activity (n = 75) and moderate/high activity (n = 152) groups. Multivariable logistic regression was used to develop two predictive models: Model 1 (based on laboratory parameters) and Model 2 (Model 1 plus tongue features). Both models were presented as nomograms and web-based calculators. Model discrimination was evaluated using receiver operating characteristic curves, calibrated via calibration plots, and clinical utility was determined using decision curve analysis.

Multivariable logistic regression identified white blood cell (WBC), hemoglobin (HGB), platelets (PLT), and IgA as predictors in Model 1, while Model 2 incorporated WBC, HGB, greasy coating and sublingual varicosity. Model 2 outperformed Model 1, achieving an area under the curve of 0.846 (95% confidence interval = 0.740-0.951), with a sensitivity of 0.63 and specificity of 0.826. A nomogram and online calculator were developed from this optimized model for clinical use.

We have developed a preliminary RA disease activity assessment model integrating tongue features and laboratory parameters. This model shows high accuracy and considerable potential for clinical utility.