Fu Runing, Lian Wenping, Zhang Bohao, Liu Gang, Feng Xinyu, Zhu Yingjie, Zhou Jiuan, Zhang Xinyu, Wang Shukai, Huo Huijuan, Wang Daxin, Liu Cui, Gao Saisai, Ma Yajie, Peng Mengle
Department of Clinical Laboratory, The Third People's Hospital of Henan Province, Zhengzhou, Henan, 450006, People's Republic of China.
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450000, People's Republic of China.
J Inflamm Res. 2024 Oct 25;17:7721-7731. doi: 10.2147/JIR.S481649. eCollection 2024.
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.
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).
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.
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.
炎症标志物与多种疾病相关，但其在梅杰综合征（MS）中的作用尚不清楚。本研究旨在开发并验证一种基于炎症标志物的MS风险预测列线图。
回顾性分析2022年1月至2023年12月期间河南省第三人民医院448例连续的MS患者的数据。将MS队列随机分为训练集和验证集。基于训练集数据，使用多因素逻辑回归模型构建列线图。通过交叉验证、受试者工作特征（ROC）曲线分析、校准曲线分析和决策曲线分析（DCA）对模型性能进行验证。
通过对总共11个变量进行多因素逻辑回归分析，共确定了5个预测因子，包括红细胞分布宽度（RDW）、血红蛋白（HGB）、高密度脂蛋白胆固醇（HDL-C）、淋巴细胞与单核细胞比值（LMR）和全身免疫炎症指数（SII）。交叉验证结果表明，由上述5个预测因子构建的模型具有稳定性。该模型显示出中等的预测能力，训练集中ROC曲线下面积为0.767，验证集中为0.735。校准曲线和DCA结果表明，该模型具有较强的一致性和显著的临床应用潜力。
我们构建了一个基于RDW、HGB、HDL-C、LMR和SII这5个风险预测因子的列线图，用于预测MS并提高识别MS风险的预测准确性。
