Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.
Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.
复制标题
在高眼压治疗研究中，机器学习衍生的基线视野模式可预测未来青光眼的发病情况。
DOI：
10.1167/iovs.65.2.35
复制
发表时间：
2024
影响因子：
4.4
通讯作者：
Zebardast,Nazlee
中科院分区：
文献类型：
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作者：
Singh,RishabhK;Smith,Sophie;Fingert,John;Gordon,Mae;Kass,Michael;Scheetz,Todd;Segrè,AyelletV;Wiggs,Janey;Elze,Tobias;Zebardast,Nazlee
目的：高眼压治疗研究（OHTS）确定了高眼压患者原发性开角型青光眼（POAG）的危险因素，包括模式标准差（PSD）。原型分析是一种无监督的机器学习方法，可以通过识别基线视野（VFs）中的模式，为风险分层提供更可解释的方法。方法：OHTS有3272只眼。采用24-2基线VFs进行原型分析，并通过交叉验证进行模型选择。计算了原型的分解系数。采用惩罚Cox比例风险模型选择判别性ATs。将AT模型与OHTS模型进行了比较。通过每年的平均偏差变化来确定ATs与POAG发病和VF进展之间的关联。结果：我们选择了8494个基线VFs。最佳AT数为19。AT9、AT11、AT7患病率最高。基于at的POAG发病预测模型的c指数为0.75。多变量模型显示，AT5（风险比[HR]= 1.14； 95%可信区间[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）系数的四分位数范围增加，表明POAG发病风险增加。AT5、AT10和AT14与VF快速进展显著相关。在高风险ATs的亚组分析（bb0 95百分位系数或< 75百分位系数）中，PSD作为低风险组POAG的预测因子失去了显著性。结论：在可检测到青光眼损害之前，基线VF包含代表早期变化的隐性模式，可能增加高眼压患者POAG发作和VF进展的风险。PSD和POAG之间的关系被基线时存在的高危模式所改变。基于at的POAG预测模型可以在临床环境中提供更多可解释的青光眼特异性信息。
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.