Su Zixuan, You Yayan, Cheng Shengnan, Huang Jiahui, Liang Xueqing, Wang Xinghua, Jiang Fagang
Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Department of Ophthalmology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, Zhejiang, China.
BMC Endocr Disord. 2025 Mar 26;25(1):83. doi: 10.1186/s12902-025-01895-3.
This study aims to identify indicators of disease activity in patients with graves' orbitopathy (GO) by examining the microstructural characteristics of meibomian glands (MGs) and developed a diagnostic model.
We employed in vivo confocal microscopy (IVCM) to examine MGs in GO patients. Patients classified in the active phase were determined based on the clinical activity score (CAS). The research employed the least absolute shrinkage and selection operator (LASSO) method to select key indicators. Subsequently, a logistic regression model was constructed to predict GO disease activity.
A total of 45 GO patients, corresponding to 90 eyes, were included in this study. A Lasso regression algorithm was utilized to select the predictor variables. Five predictor variables were included in our diagnostic model ultimately. The area under the curve (AUC) for the training set model reached 0.959, and for the validation set was 0.969. The training set and validation set models both demonstrated high accuracy in calibration. Finally, a Nomogram chart was constructed to visualize the diagnostic model.
We constructed a diagnostic model based on microstructural indicators of MGs obtained through IVCM and offered a clinical utility for assessing GO disease activity, aiding in the diagnosis and selection of treatment strategies for GO.
本研究旨在通过检查睑板腺（MGs）的微观结构特征，识别格雷夫斯眼眶病（GO）患者的疾病活动指标，并建立一个诊断模型。
我们采用体内共聚焦显微镜（IVCM）检查GO患者的MGs。根据临床活动评分（CAS）确定处于活动期的患者。本研究采用最小绝对收缩和选择算子（LASSO）方法来选择关键指标。随后，构建逻辑回归模型来预测GO疾病活动。
本研究共纳入45例GO患者，共90只眼。利用Lasso回归算法选择预测变量。最终，我们的诊断模型纳入了5个预测变量。训练集模型的曲线下面积（AUC）达到0.959，验证集为0.969。训练集和验证集模型在校准方面均显示出高准确性。最后，构建了列线图来直观展示诊断模型。
我们基于通过IVCM获得的MGs微观结构指标构建了一个诊断模型，为评估GO疾病活动提供了临床实用价值，有助于GO的诊断和治疗策略的选择。
