The aim of this study was to select the characteristic indicators for the diag-nosis of meibomian gland dysfunction (MGD) and establish a model to diagnose MGD.

From March to November 2016, 923 MGD (923 eyes) and 114 cases of healthy volunteers (114 eyes) were collected in 23 multi-center medical institutions including Beijing Tongren Hospital, affiliated to the Capital Medical University, Beijing Institute of Ophthalmology, The First Hospital of Xi′an Shaanxi Province, Chinese Academy of Medical Sciences and the First Hospital affiliated to Jinan University. Among of them, there were 330 male (eyes) and 684 female (684 eyes) with an average age (42.5±13.9) years-old (ranged from 18 to 88 years-old). Demographic information and self-assessment of ocular surface disease index(OSDI) were collected through questionnaires. The tear film break-up time (TBUT), fluoreciin staining of conjunctiva, tear secretion test (SchirmerⅠ), infrared photography of eyelid gland, tear film lipid layer thickness (LLT), eyelid gland opening and secretion trait score were performed. All MGD patients were randomly divided into 70% model group and 30% checking group. According to clinical diagnosis, the results of each index diagnosis were determined positive and negative. Data was expressed in cases and percentages. Chi-square test was used for comparison between groups. The area under the curve (AUC) was used to check the diagnostic efficiency of indices for MGD through receiver operating characteristic curve (ROC) analysis. A discriminant model was established for effective indicators by Logistic regression model. Then, 30% patients with MGD and healthy controls were discriminated to verify the efficiency of the diagnostic model.

In 70% model group, there were 402 (62.2%), 91 (79.8%) females in patients with MGD and healthy controls, respectively with statistically significant differences (χ²=196.189, P<0.05). In 70% model group, the number of positive results consistent with the clinical diagnosis in OSDI scores, fluoreciin staining of conjunctiva, SchirmerⅠ, loss rate of meibomian gland, LLT, eyelid gland opening and secretion trait score, TBUT were 478 (74.0%), 180 (27.9%), 492 (76.2%), 604 (93.5%), 512 (79.4%), 504 (78.0%), 575 (89.0%), and 583 (90.4%) cases, respectively with statistically significant differences (χ²=44.46, 442.54, 25.20, 17.54, 12.52, 6.91, 13.90, 1.75; P<0.05). ROC curve analysis shown that AUC value of OSDI scores, fluoreciin staining of conjunctiva, eyelid gland opening and secretion trait score, TBUT were less than 0.800 (P<0.05), while these effective indices were established by the Logistic discriminant model, such as gender (X₁), OSDI (X₂), fluoreciin staining of conjunctiva (X₃), TBUT (X₄), eyelid gland opening (X₅), and secretion trait score (X₆), and then AUC value was 0.800, which had better diagnostic efficiency. The formula was as follows: Y=1/(1+ e^-Z), Z= -4.546+ 1.069X₁ -0.020X₂+ 0.286X₃ -0.553X₄-0.220X₅+ 0.124X₆. When the diagnostic model predictive value was greater than 0.196, it was defined as a positive diagnosis; the sensitivity of the diagnosis of MGD was 83.4%, and the specificity was 61.4%. In 30% checking group, the number of positive results consistent with the clinical diagnosis were 229 (82.7%) and 44 (40.6%) with non-significant difference (χ²=0.17, P>0.05). The sensitivity and specificity of the discriminant results were 82.7% and 61.4%, respectively.

Abnormal eyelid margin, lack of tear lipid layer, tear film instability, corneal epithelial fluorescein staining are helpful for the diagnosis of MGD. The Logistic discriminant model established based on these indicators has high sensitivity and specificity.