Glaucoma Prediction Models Based on Ocular and Systemic Findings

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).

Keywords: Artificial intelligence; Glaucoma prediction model; Machine learning; Ocular predictors of glaucoma; Systemic factors and glaucoma.