Nomogram Based on Inflammatory Factor to Predict Therapeutic Response of Thrombocytopenia in Patients with Primary Sjögren's Syndrome.

OBJECTIVE

Thrombocytopenia is a common manifestation of blood system involvement in primary Sjögren's syndrome (pSS) patients, and the treatment approach involves glucocorticoids and immune agents. However, a proportion of patients do not respond well to this therapy and failed to achieve remission. Accurate prediction of therapeutic response in pSS patients with thrombocytopenia is of great significance for improving the prognosis. This study aims to analyze the influencing factors of no remission to treatment in pSS patients with thrombocytopenia and establish an individualized nomogram to predict the treatment response of patients.

MATERIALS AND METHODS

The demographic data, clinical manifestations and laboratory examinations of 119 patients with thrombocytopenia pSS in our hospital were retrospectively analyzed. According to the 30-day treatment response, patients were divided into remission group and non-remission group. Logistic regression was used to analyze the influencing factors related to the treatment response of patients, and then a nomogram was further established. The discriminative ability and clinical benefit of the nomogram were evaluated by receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis (DCA).

RESULTS

After treatment, there were 80 patients in the remission group and 39 in the non-remission group. Comparative analysis and multivariate logistic regression analysis identified hemoglobin (=0.023), C3 level (=0.027), IgG level (=0.040), and bone marrow megakaryocyte counts (=0.001) as independent predictors of treatment response. The nomogram was constructed based on the above four factors, and the C-index of the model was 0.882 ( 0.810-0.934). The calibration curve and DCA proved that the model has better performance.

CONCLUSION

The nomogram incorporating hemoglobin, C3 level, IgG level, and bone marrow megakaryocyte counts could be used as an auxiliary tool to predict the risk of treatment non-remission in pSS patients with thrombocytopenia.