Prcis: The prevalence of glaucoma in the adult population included in this study was 2.3%. Normal values of routine eye examinations are provided including age and sex variations.
Purpose: The purpose of this study was to analyze the prevalence of glaucoma in a very large database.
Methods: Retrospective analysis of medical records of patients examined at the Medical Survey Institute of a tertiary care university referral center between 2001 and 2020. A natural language process (NLP) algorithm identified patients with a diagnosis of glaucoma. The main outcome measures included the prevalence and age distribution of glaucoma. The secondary outcome measures included the prevalence and distribution of visual acuity (VA), intraocular pressure (IOP), and cup-to-disc ratio (CDR).
Results: Data were derived from 184,589 visits of 36,762 patients (mean age: 52 y, 68% males). The NLP model was highly sensitive in identifying glaucoma, achieving an accuracy of 94.98% (area under the curve=93.85%), and 633 of 27,517 patients (2.3%) were diagnosed as having glaucoma with increasing prevalence in older age. The mean VA was 20/21, IOP 14.4±2.84 mm Hg, and CDR 0.28±0.16, higher in males. The VA decreased with age, while the IOP and CDR increased with age.
Conclusions: The prevalence of glaucoma in the adult population included in this study was 2.3%. Normal values of routine eye examinations are provided including age and sex variations. We proved the validity and accuracy of the NLP model in identifying glaucoma.
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