Gan Minzhi, Peng Yong, Zhu Mengya, Ying Ying
Department of Rheumatology, Ningbo NO.2 Hospital, Ningbo, Zhejiang, 315010, People's Republic of China.
J Inflamm Res. 2023 Jun 12;16:2449-2459. doi: 10.2147/JIR.S414320. eCollection 2023.
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.
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).
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.
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.
血小板减少是原发性干燥综合征（pSS）患者血液系统受累的常见表现，治疗方法包括糖皮质激素和免疫制剂。然而，一部分患者对这种治疗反应不佳，未能实现缓解。准确预测pSS血小板减少患者的治疗反应对于改善预后具有重要意义。本研究旨在分析pSS血小板减少患者治疗未缓解的影响因素，并建立个体化列线图以预测患者的治疗反应。
回顾性分析我院119例pSS血小板减少患者的人口统计学数据、临床表现和实验室检查结果。根据30天治疗反应，将患者分为缓解组和未缓解组。采用逻辑回归分析与患者治疗反应相关的影响因素，进而建立列线图。通过受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）评估列线图的判别能力和临床效益。
治疗后，缓解组有80例患者，未缓解组有39例患者。比较分析和多因素逻辑回归分析确定血红蛋白（=0.023）、C3水平（=0.027）、IgG水平（=0.040）和骨髓巨核细胞计数（=0.001）为治疗反应的独立预测因素。基于上述四个因素构建列线图，模型的C指数为0.882（0.810 - 0.934）。校准曲线和DCA证明该模型具有更好的性能。
纳入血红蛋白、C3水平、IgG水平和骨髓巨核细胞计数的列线图可作为预测pSS血小板减少患者治疗未缓解风险的辅助工具。
