Big Data Analysis of Glaucoma Prevalence in Israel

Daphna Landau Prat, Ofira Zloto, Noa Kapelushnik, Ari Leshno, Eyal Klang, Sigal Sina, Shlomo Segev, Shahar Soudry, Guy J. Ben Simon

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Précis: 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.

Original languageEnglish
Pages (from-to)962-967
Number of pages6
JournalJournal of Glaucoma
Volume32
Issue number11
DOIs
StatePublished - 1 Nov 2023
Externally publishedYes

Keywords

  • artificial intelligence
  • big data
  • cup-to-disc ratio
  • glaucoma
  • intraocular pressure
  • machine learning
  • natural language processing
  • visual acuity

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