机器学习衍生的基线视野模式可预测青光眼在高眼压治疗研究中的发病。
Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.
机构信息
Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States.
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States.
出版信息
Invest Ophthalmol Vis Sci. 2024 Feb 1;65(2):35. doi: 10.1167/iovs.65.2.35.
PURPOSE
The Ocular Hypertension Treatment Study (OHTS) identified risk factors for primary open-angle glaucoma (POAG) in patients with ocular hypertension, including pattern standard deviation (PSD). Archetypal analysis, an unsupervised machine learning method, may offer a more interpretable approach to risk stratification by identifying patterns in baseline visual fields (VFs).
METHODS
There were 3272 eyes available in the OHTS. Archetypal analysis was applied using 24-2 baseline VFs, and model selection was performed with cross-validation. Decomposition coefficients for archetypes (ATs) were calculated. A penalized Cox proportional hazards model was implemented to select discriminative ATs. The AT model was compared to the OHTS model. Associations were identified between ATs with both POAG onset and VF progression, defined by mean deviation change per year.
RESULTS
We selected 8494 baseline VFs. Optimal AT count was 19. The highest prevalence ATs were AT9, AT11, and AT7. The AT-based prediction model had a C-index of 0.75 for POAG onset. Multivariable models demonstrated that a one-interquartile range increase in the AT5 (hazard ratio [HR] = 1.14; 95% confidence interval [CI], 1.04-1.25), AT8 (HR = 1.22; 95% CI, 1.09-1.37), AT15 (HR = 1.26; 95% CI, 1.12-1.41), and AT17 (HR = 1.17; 95% CI, 1.03-1.31) coefficients conferred increased risk of POAG onset. AT5, AT10, and AT14 were significantly associated with rapid VF progression. In a subgroup analysis by high-risk ATs (>95th percentile or <75th percentile coefficients), PSD lost significance as a predictor of POAG in the low-risk group.
CONCLUSIONS
Baseline VFs, prior to detectable glaucomatous damage, contain occult patterns representing early changes that may increase the risk of POAG onset and VF progression in patients with ocular hypertension. The relationship between PSD and POAG is modified by the presence of high-risk patterns at baseline. An AT-based prediction model for POAG may provide more interpretable glaucoma-specific information in a clinical setting.
目的
眼高血压治疗研究（OHTS）确定了眼高血压患者原发性开角型青光眼（POAG）的风险因素，包括模式标准差（PSD）。原型分析是一种无监督机器学习方法，通过识别基线视野（VF）中的模式，可能提供一种更具解释性的风险分层方法。
方法
OHTS 中有 3272 只眼。使用 24-2 基线 VF 进行原型分析，交叉验证进行模型选择。计算原型（AT）的分解系数。实施惩罚 Cox 比例风险模型以选择有区别的 AT。将 AT 模型与 OHTS 模型进行比较。根据每年平均偏差变化，确定与 POAG 发病和 VF 进展相关的 AT 之间的关联。
结果
我们选择了 8494 个基线 VF。最佳 AT 计数为 19。最常见的 AT 是 AT9、AT11 和 AT7。基于 AT 的预测模型对 POAG 发病的 C 指数为 0.75。多变量模型表明，AT5（风险比 [HR] = 1.14；95%置信区间 [CI]，1.04-1.25）、AT8（HR = 1.22；95% CI，1.09-1.37）、AT15（HR = 1.26；95% CI，1.12-1.41）和 AT17（HR = 1.17；95% CI，1.03-1.31）的四分位距增加一个单位，POAG 发病风险增加。AT5、AT10 和 AT14 与快速 VF 进展显著相关。在高风险 ATs（>95 百分位数或<75 百分位数系数）亚组分析中，在低风险组中，PSD 作为 POAG 的预测因子失去了意义。
结论
在可检测到青光眼损伤之前，基线 VF 中包含潜在的模式，代表可能增加眼高血压患者 POAG 发病和 VF 进展风险的早期变化。在基线时存在高风险模式的情况下，PSD 与 POAG 的关系发生改变。基于 AT 的 POAG 预测模型可能在临床环境中提供更具解释性的青光眼特定信息。
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