Big Data analysis of vision screening standards used to evaluate fitness to drive

Prevalence of common and rare ophthalmic findings in adults attending a medical survey institute

PURPOSE

To examine the ophthalmic data from a large database of people attending a general medical survey institute, and to investigate ophthalmic findings of the eye and its adnexa, including differences in age and sex.

METHODS

Retrospective analysis including medical data of all consecutive individuals whose ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects examined at a single general medical survey institute.

RESULTS

Data were derived from 184,589 visits of 3676 patients (mean age 52 years, 68% males). The prevalence of the following eye pathologies were extracted. Eyelids: blepharitis (n = 4885, 13.3%), dermatochalasis (n = 4666, 12.7%), ptosis (n = 677, 1.8%), ectropion (n = 73, 0.2%), and xanthelasma (n = 160, 0.4%). Anterior segment: pinguecula (n = 3368, 9.2%), pterygium (n = 852, 2.3%), and cataract or pseudophakia (n = 9381, 27.1%). Cataract type (percentage of all phakic patients): nuclear sclerosis (n = 8908, 24.2%), posterior subcapsular (n = 846, 2.3%), and capsular anterior (n = 781, 2.1%). Pseudophakia was recorded for 697 patients (4.6%), and posterior subcapsular opacification for 229 (0.6%) patients. Optic nerve head (ONH): peripapillary atrophy (n = 4947, 13.5%), tilted disc (n = 3344, 9.1%), temporal slope (n = 410, 1.1%), ONH notch (n = 61, 0.2%), myelinated nerve fiber layer (n = 94, 0.3%), ONH drusen (n = 37, 0.1%), optic pit (n = 3, 0.0%), and ON coloboma (n = 4, 0.0%). Most pathologies were more common in males except for ONH, and most pathologies demonstrated a higher prevalence with increasing age.

CONCLUSIONS

Normal ophthalmic data and the prevalences of ocular pathologies were extracted from a very large database of subjects seen at a single medical survey institute.

Big Data Analysis of Glaucoma Prevalence in Israel

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.