Analysis of risk factors for Meige syndrome and construction and validation of a clinical prediction nomogram model

Meige syndrome (MS) is a craniocervical dystonia characterized by blepharospasm and oromandibular dystonia. Its etiology remains unclear, and clinical diagnosis is often delayed. Currently, there is a lack of effective risk prediction tools, making early intervention challenging.

To systematically analyze the risk factors for MS and develop and validate a clinical prediction nomogram model based on clinical indicators to facilitate early risk assessment.

A retrospective case-control study was conducted, enrolling 450 confirmed MS patients and 450 controls from the Third People's Hospital of Henan Province between January 2021 and December 2023. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors, and a nomogram prediction model was constructed based on regression coefficients. The model's discriminative ability, calibration, and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

Multivariate analysis revealed that a history of thyroid disease (OR = 12.797), psychiatric disorders (OR = 6.892), and head/face surgery (OR = 3.466) were independent risk factors for MS, while female sex (OR = 1.87) and cerebrovascular disease (OR = 1.999) were moderate-risk factors. Notably, smoking (OR = 0.411), alcohol consumption (OR = 0.396), and diabetes (OR = 0.534) showed protective associations. The constructed nomogram model demonstrated strong predictive performance in both the training and validation sets (AUC = 0.789 and 0.800, respectively). Calibration curves indicated high consistency between predicted and observed probabilities, and DCA confirmed its clinical applicability.

We developed and validated a clinical prediction nomogram for MS incorporating eight independent predictors: history of thyroid disorders, psychiatric disorders, head/face surgery, female sex, cerebrovascular disease, as well as protective factors including smoking, alcohol consumption, and diabetes. The model provides a quantifiable tool for early risk stratification and targeted intervention in clinical practice. However, further optimization and validation through multicenter prospective studies are warranted.