Abstract
BACKGROUND: Glaucoma is one of the most frequent vision-threatening eye diseases. It is frequently associated with excessive intraocular pressure (IOP), which can cause vision loss and damaged optic nerves. The main objective of this study was to model time to blindness of glaucoma patients by using appropriate statistical models. Study Design. A Retrospective Community-Based Longitudinal Study design was applied. Materials and Procedures. The data were obtained from Ophthalmology Department of JUSH from the period of January 2016 to August 2020. The glaucoma patient's information was extracted from the patient card and 321 samples were included in the study. To discover the factors that affect time to blindness of glaucoma patients', researchers used the Accelerated Failure Time (AFT) model. RESULTS: 81.3 percent of the 321 glaucoma patients were blind. Unilaterally and bilaterally blinded female and male glaucoma patients were 24.92 and 56.38%, respectively. After glaucoma disease was confirmed, the median time to the blindness of both eyes and one eye was 12 months. The multivariable log-logistic accelerated failure-time model fits the glaucoma patient's time to blind dataset well. The result showed that the chance of blindness of glaucoma patients who have absolute stage of glaucoma, medium duration of diagnosis, long duration of diagnosis, and IOP greater than 21 mmHg were high with parameters (ϕ = 2.425, p value = 0.049, 95% CI [2.249, 2.601]), (ϕ = 1.505, p value = 0.001, 95% CI [0.228, 0.589]), (ϕ = 3.037, p value = 0.001, 95% C.I [2.850, 3.22]) and (ϕ 0.851, p value = 0.034, 95% C.I [0.702, 0.999]), respectively. CONCLUSION: The multivariable log-logistic accelerated failure time model evaluates the prognostic factors of time to blindness of glaucoma patients. Under this finding, duration of diagnosis, IOP, and stage of glaucoma were a key determinant factors of time to blindness of glaucoma patients'. Finally, the log-logistic accelerated failure-time model was the best-fitted parametric model based on AIC and BIC values.