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Development and Validation of a Nomogram Based on Inflammatory Markers for Risk Prediction in Meige Syndrome Patients
Runing Fu1, Wenping Lian1, Bohao Zhang2
1Department of Clinical Laboratory, The Third People's Hospital of Henan Province, Zhengzhou, Henan, 450006, People's Republic of China.
Journal of Inflammation Research
|October 30, 2024
Summary
A new nomogram predicts Meige syndrome (MS) risk using inflammatory markers like red blood cell distribution width (RDW) and hemoglobin (HGB). This tool aids in early MS identification and risk assessment.
Area of Science:
- Medical Research
- Clinical Diagnostics
- Biomarker Discovery
Background:
- Inflammatory markers are linked to various diseases, but their specific role in Meige syndrome (MS) requires further elucidation.
- Understanding these associations is crucial for improving diagnostic and prognostic capabilities for MS.
Purpose of the Study:
- To develop and validate a predictive nomogram for Meige syndrome (MS) risk.
- To identify key inflammatory markers that can serve as predictors for MS.
Main Methods:
- Retrospective analysis of 448 Meige syndrome (MS) patients.
- Development of a nomogram using multivariate logistic regression on a training set.
- Validation of the nomogram using cross-validation, ROC curve analysis, calibration curves, and decision curve analysis (DCA).
Main Results:
- Five significant predictors were identified: red blood cell distribution width (RDW), hemoglobin (HGB), high-density lipoprotein cholesterol (HDL-C), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII).
- The nomogram demonstrated moderate predictive ability with an AUC of 0.767 (training set) and 0.735 (validation set).
- The model exhibited strong consistency and potential for clinical application based on calibration and DCA.
Conclusions:
- A validated nomogram incorporating RDW, HGB, HDL-C, LMR, and SII was successfully developed for Meige syndrome (MS) risk prediction.
- This tool enhances predictive accuracy for identifying individuals at risk of MS.
- The nomogram shows promise for clinical utility in assessing MS risk.
