Runing Fu,1,* Wenping Lian,1,* Bohao Zhang,2,* Gang Liu,3 Xinyu Feng,1 Yingjie Zhu,1 Jiuan Zhou,1 Xinyu Zhang,4 Shukai Wang,1 Huijuan Huo,1 Daxin Wang,1 Cui Liu,1 Saisai Gao,1 Yajie Ma,4 Mengle Peng1,* 

1Department of Clinical Laboratory, The Third People’s Hospital of Henan Province, Zhengzhou, Henan, 450006, People’s Republic of China; 2Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450000, People’s Republic of China; 3The Center of Meige Syndrome, The Third People’s Hospital of Henan Province, Zhengzhou, Henan, 450006, People’s Republic of China; 4Department of Medical Affair, the Third People’s Hospital of Henan Province, Zhengzhou, Henan, 450006, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Mengle Peng; Yajie Ma, Email pengmengle1990@126.com; szsyywk@sina.com

Purpose: Inflammatory markers are known to be associated with many diseases, but their role in Meige syndrome (MS) remains unclear. This study aimed to develop and validate a nomogram for the risk prediction of MS based on inflammatory markers.
Patient Data and Methods: Data from 448 consecutive patients with MS at the Third People’s Hospital of Henan Province between January 2022 and December 2023 were retrospectively reviewed. The MS cohort was randomly divided into separate training and validation sets. A nomogram was constructed using a multivariate logistic regression model based on data from the training set. The model’s performance was validated through cross-validation, receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA).
Results: A total of five predictors, including red blood cell distribution width (RDW), hemoglobin (HGB), high-density lipoprotein cholesterol (HDL-C), the lymphocyte-to-monocyte ratio (LMR), and the systemic immune‐inflammation index (SII), were identified using multivariate logistic regression from a total of 11 variables. The cross-validation results indicated the stability of the model constructed with the above five predictors. The model showed moderate predictive ability, with an area under the ROC curve of 0.767 in the training set and 0.735 in the validation set. The calibration curve and DCA results indicate that the model has strong consistency and significant potential for clinical application.
Conclusion: We constructed a nomogram based on five risk predictors, RDW, HGB, HDL-C, the LMR and the SII, to predict MS and enhance the predictive accuracy for identifying MS risk.

Keywords: Meige syndrome, inflammatory marker, nomogram, LMR, SII