A Nomogram Based on Coagulation Markers for Predicting Meige Syndrome Risk

Yuanyuan Chen¹Yuehua Sun²Xinyu Zhang¹Yang Chen³Gang Liu¹Shuai Zhao¹Mengyao Shi⁴Xuefeng Lv⁵Wenping Lian¹Zhongquan Wang¹Yajie Ma¹Mengle Peng^1*

• ¹The Third People's Hospital of Henan Province, Zhengzhou, China
• ²Zhongnan Hospital of Wuhan University, Wuhan, China
• ³Zhengzhou University College of Medicine, Zhengzhou, China
• ⁴Duke University School of Medicine, Durham, United States
• ⁵The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China

Background: Meige 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. Methods: Data 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 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. Results: Seven 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 Fib, PDW and MPV were independent risk factors of MS. Based on these three 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. Conclusion: This 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.