Salivary Gland Ultrasound Score and Hyperglobulinemia in an Age-Stratified Prediction Model for Positive Labial Focus Score in Sjögren Disease.

OBJECTIVE

To develop and validate a noninvasive predictive model for labial focus score (FS) ≥ 1 in patients suspected of having Sjögren disease (SjD) by integrating salivary gland ultrasound (SGUS) findings with clinical variables.

METHODS

A novel semiquantitative SGUS scoring system was applied to 449 prospectively recruited patients. Multivariable logistic regression analysis was conducted to identify independent predictors of FS ≥ 1, which were subsequently incorporated into a risk stratification matrices model. The model underwent both internal (n = 139) and external validation (n = 57).

RESULTS

The derivation cohort included 449 patients (mean age 44.0 years, 94.7% female), with 284 (63.3%) showing labial FS ≥ 1. Patients with FS ≥ 1 were older and had higher total SGUS scores and hyperglobulinemia. Multivariable logistic regression identified the SGUS score (odds ratio [OR] 1.44), age (OR 1.51), and hyperglobulinemia (OR 1.91) as independent outcome predictors. This led to an age-stratified prediction model categorizing patients into high-, moderate-, and low-risk groups across 3 age brackets. This model showed strong predictive value for labial FS ≥ 1 in both high-risk and low-risk populations (area under the curve [AUC] 0.91, 95% CI 0.87-0.96, P < 0.001), with specificity of 81%, sensitivity of 95%, positive predictive value of 92%, and negative predictive value of 87%. The model performed well in both internal validation (AUC 0.90) and external validation (AUC 0.83).

CONCLUSION

Our SGUS-based predictive model provides an exploratory, hypothesis-generating tool for guiding labial minor salivary gland biopsy decisions in SjD by integrating imaging with clinical variables, enabling personalized risk stratification. It effectively identifies high-risk patients needing biopsy and low-risk patients who can avoid unnecessary procedures, offering a significant diagnostic advance.