Ultrasonography of major salivary glands compared with parotid and labial gland biopsy and classification criteria in patients with clinically suspected primary Sjögren's syndrome.

OBJECTIVE

To assess the validity of ultrasound of major salivary glands (sUS) compared with parotid and labial gland biopsies, sialometry, anti-SSA/Ro antibody status and classification criteria in patients clinically suspected with primary Sjögren's syndrome (pSS).

METHODS

103 consecutive outpatients with clinically suspected pSS underwent sUS. Parenchymal echogenicity, homogeneity, hypoechogenic areas, hyperechogenic reflections and clearness of salivary gland border were scored according to the Hocevar scoring system. Total ultrasound score was calculated as the sum of these domains (range 0-48).

RESULTS

Absolute agreement between sUS and parotid (83%) and labial (79%) gland biopsy outcome was good. Negative sUS predicts negative parotid gland biopsy, and positive sUS predicts positive labial gland biopsy. Compared with the American European Consensus Group (AECG) classification, sUS showed an absolute agreement of 82%, sensitivity of 71% and specificity of 92%. Compared with the American College of Rheumatology (ACR) classification, absolute agreement was 86%, sensitivity was 77% and specificity was 92%. Compared with the ACR-European League Against Rheumatism (EULAR) classification, absolute agreement was 80%, sensitivity was 67% and specificity was 94%. Positive sUS predicts classification, but negative sUS does not exclude classification. The combination of positive sUS with presence of anti-SSA/Ro antibodies or negative sUS with absence of anti-SSA/Ro antibodies showed a high predictive value for classification as pSS or non-pSS.

CONCLUSION

In our prospective inception cohort study derived from daily clinical practice, absolute agreement between sUS and salivary gland biopsies was slightly higher for parotid compared with labial gland biopsies. The combination of positive sUS and presence of anti-SSA/Ro antibodies highly predicts classification according to the AECG, ACR and ACR-EULAR classification criteria.