ABSTRACT
PURPOSE: To describe and evaluate a comprehensive grading system for meibomian gland (MG) digital infrared images developed for the Dry Eye Assessment and Management (DREAM) Study. METHODS: Cross-sectional study. Reading Center (RC) certified readers independently evaluated MG features of both lids from meibography images of dry eye disease subjects. Dropout areas were measured using planimetry software. Inter-reader and grade-regrade agreement and comparison of meiboscale scores (Meiboscale©; Pult) from clinical centers to RC percent dropout and of MG features with clinical parameters were evaluated. RESULTS: Among 551 eyes of 277 patients at baseline, 62 (11%) upper lid and 5 (1%) lower lid images were missing. Lid eversion was poor in 63 (13%) of upper lids compared to 15 (3%) of lower lids. Intraclass correlation for inter-reader and grade-regrade agreement was moderate to substantial for most MG features. MG features were more frequent in the upper lid (pâ¯<â¯0.001), except for dropout glands, gaps, fluffy gland areas and dropout areas. Clinic meiboscale score was associated with RC percent dropout (pâ¯<â¯0.001), a clinic score of 0% having a mean RC score of 19%, and a clinic score of >75% having a mean RC score of 66%. MG plugging was associated with ghost glands (pâ¯=â¯0.009), dropout glands (pâ¯<â¯0.001) and a composite severity score (pâ¯=â¯0.02); turbid and absent secretions were associated with ghost glands (pâ¯=â¯0.046). CONCLUSION: RC readers identified MG features with good reproducibility. Upper lids had more MG features. RC dropout areas correlated well with clinic meiboscale scores. Ghost glands were associated with paste like and absent meibomian secretions.
