Work

Predictive modeling of graves’ orbitopathy activity based on meibomian glands analysis using in vivo confocal microscopy

Year: 2025

Type: article

Abstract: 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. What is known? • Several studies have demonstrated an association between Graves’ Orbitopathy (GO) severity and increased ocular surface exposure. In vivo confocal microscopy revealed distinct microstructural characteristics of Meibomian Glands (MGs) in GO patients compared to healthy individuals. • The Clinical Activity Score (CAS) serves as a widely adopted tool for evaluating GO activity. However, its precision is compromised by inter-assessor variability, particularly when non-ophthalmologists are involved in the assessment. What is new? • This study introduces a novel diagnostic model for evaluating GO activity by utilizing In Vivo Confocal Microscopy (IVCM) to quantify changes in the microstructure of MGs. • The predictive model achieved strong performance with an Area Under the Curve (AUC) of 0.959 for the training set and 0.969 for the validation set, underscoring its reliability, repeatability, and generalizability, establishing it as a valuable tool to aid in the diagnosis of GO.

Source: BMC Endocrine Disorders

DOI: 10.1186/s12902-025-01895-3