Glaucoma Prediction Models Based on Ocular and Systemic Findings

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

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma. Methods: Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma. Results: A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%). Conclusions: Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).

Original languageEnglish
Pages (from-to)29-38
Number of pages10
JournalOphthalmic Research
Volume67
Issue number1
DOIs
StatePublished - 18 Dec 2023
Externally publishedYes

Keywords

  • Artificial intelligence
  • Glaucoma prediction model
  • Machine learning
  • Ocular predictors of glaucoma
  • Systemic factors and glaucoma

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