A nomogram based on coagulation markers for predicting meige syndrome risk - Chen, Yuanyuan; Sun, Yuehua; Zhang, Xinyu; Chen, Yang; Zhao, Shuai; Shi, Mengyao; Lv, Xuefeng; Lian, Wenping; Wang, Zhongquan; Wu, Yongjun; Ma, Yajie; Liu, Gang; et al. (Frontiers in Aging Neuroscience, 2025)
BackgroundMeige syndrome (MS) is a rare adult-onset cranial dystonia associated with complex neuropathological mechanisms. Recent studies have shown that abnormal coagulation plays a vital role in the pathological and physiological mechanisms of neurological disease and injury. However, the association between coagulation markers and MS remains unclear.MethodsData of 493 patients with MS and 684 healthy controls (HCs) were recruited from the Department of Clinical Laboratory of the Third People’s Hospital of Henan Province. Differences in coagulation markers were compared between different groups. Patients with MS were randomly divided into training and test cohorts. Univariate and multivariate regression analyses were used to assess independent risk factors for MS. The assumption of linearity of independent variables and the log-odds was assessed by Box-Tidwell transformation. A nomogram was constructed based on these independent risk factors. The value of the area under the receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow test and decision curve analysis (DCA) were used to comprehensively evaluate the performance of the model.ResultsSeven coagulation markers differed significantly between the MS and HC groups. The platelet count (PLT) and plateletcrit (PCT) of MS2 patients were higher than those of MS1 patients. The activated partial thromboplastin time (APTT) was significantly elevated in patients with severe blepharospasm. Among the seven markers, APTT and fibrinogen (Fib) showed the highest diagnostic performance for MS, with AUCs of 0.7761 and 0.6464, respectively (P < 0.0001). Univariate and multivariate logistic regression analysis further revealed that PT%, Fib, PDW and INR were independent risk factors of MS. Based on these independent predictors, we constructed a risk prediction nomogram of MS. The ROC curve showed that the model had good discriminative performance for the diagnosis (training cohort: AUC = 0.748, 95% CI 0.713–0.782; test cohort: AUC = 0.746, 95% CI 0.697–0.795). Finally, Hosmer-Lemeshow test, calibration curves and DCA curves showed the excellent accuracy of the nomogram.ConclusionThis study provides evidence of the potential role of coagulation abnormalities in MS pathophysiology. The constructed nomogram is a quick and effective screening tool for assessing the risk of MS, thereby contributing to the diagnosis and management of MS.