Date Published:
2022 Jul 08
Abstract:
Purpose: We evaluated racial/ethnic differences in primary open-angle glaucoma (POAG) defined by machine-learning-derived regional visual field (VF) loss patterns. Methods: Participants (N = 209,036) from the Nurses' Health Study (NHS; 1980-2018), Nurses' Health Study II (NHS2; 1989-2019), and Health Professionals Follow-Up Study (HPFS; 1986-2018) who were ≥40 years of age and free of glaucoma were followed biennially. Incident POAG cases (n = 1946) with reproducible VF loss were confirmed with medical records. Total deviation information from the earliest reliable glaucomatous VF for each POAG eye (n = 2564) was extracted, and machine learning analyses were used to identify optimal solutions ("archetypes") for regional VF loss patterns. Each POAG eye was assigned a VF archetype based on the highest weighting coefficient. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using per-eye Cox proportional hazards models. Results: We identified 14 archetypes: four representing advanced loss patterns, nine of early loss, and one of no VF loss. Compared to non-Hispanic whites, black participants had higher risk of early VF loss archetypes (HR = 1.98; 95% CI, 1.48-2.66) and even higher risk for advanced loss archetypes (HR = 6.17; 95% CI, 3.69-10.32; P-contrast = 0.0002); no differences were observed for Asians or Hispanic whites. Hispanic white participants had significantly higher risks of POAG with paracentral defects and advanced superior loss; black participants had significantly higher risks of all advanced loss archetypes and three early loss patterns, including paracentral defects. Conclusions: Blacks, compared to non-Hispanic whites, had higher risks of POAG with early central and advanced VF loss. Translational Relevance: In POAG, risks of VF loss regional patterns derived from machine learning algorithms showed racial differences.
