Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Harvard Ophthalmology AI Lab, Schepens Research Eye Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Transl Vis Sci Technol. 2022 Jul 8;11(7):21. doi: 10.1167/tvst.11.7.21.
We evaluated racial/ethnic differences in primary open-angle glaucoma (POAG) defined by machine-learning-derived regional visual field (VF) loss patterns.
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.
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.
Blacks, compared to non-Hispanic whites, had higher risks of POAG with early central and advanced VF loss.
In POAG, risks of VF loss regional patterns derived from machine learning algorithms showed racial differences.
我们评估了基于机器学习得出的区域性视野（VF）丧失模式的原发性开角型青光眼（POAG）的种族/民族差异。
来自护士健康研究（NHS；1980-2018 年）、护士健康研究 II（NHS2；1989-2019 年）和健康专业人员随访研究（HPFS；1986-2018 年）的参与者（N=209036）年龄≥40 岁且无青光眼，每两年随访一次。通过病历确认具有可重复 VF 丧失的复发性 POAG 病例（n=1946）。从每个 POAG 眼最早可靠的青光眼 VF 中提取总偏差信息（n=2564），并使用机器学习分析确定区域性 VF 丧失模式的最佳解决方案（“原型”）。根据最高权重系数，为每个 POAG 眼分配 VF 原型。使用每只眼 Cox 比例风险模型估计多变量调整后的风险比（HR）和 95%置信区间（CI）。
我们确定了 14 个原型：四个代表晚期丧失模式，九个代表早期丧失模式，一个代表无 VF 丧失模式。与非西班牙裔白人相比，黑人参与者发生早期 VF 丧失原型的风险更高（HR=1.98；95%CI，1.48-2.66），发生晚期丧失原型的风险甚至更高（HR=6.17；95%CI，3.69-10.32；P-对比=0.0002）；亚洲人和西班牙裔白人则没有差异。西班牙裔白人参与者发生旁中心缺损和晚期上侧损失的 POAG 风险显著升高；黑人参与者发生所有晚期丧失原型和三种早期丧失模式（包括旁中心缺损）的风险显著升高。
与非西班牙裔白人相比，黑人发生早期中央和晚期 VF 丧失的 POAG 风险更高。
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