PDF(Pubmed)
Abstract：
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■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，包括模式标准偏差(PSD)。原型分析，一种无监督的机器学习方法，通过识别基线视野（VF）中的模式，可以提供更可解释的风险分层方法。
■OHTS中有3272只眼睛。原型分析使用24-2个基线VFs，并通过交叉验证进行模型选择。计算了原型（AT）的分解系数。实施了惩罚的Cox比例风险模型来选择判别性ATs。将AT模型与OHTS模型进行比较。确定了ATs与POAG发作和VF进展之间的关联，由每年的平均偏差变化定义。
■我们选择了8494个基线VF。最优AT计数为19。AT8患病率最高的是AT9、AT11和AT7。基于AT的预测模型对POAG发作的C指数为0.75。多变量模型表明AT5的四分位数范围增加（危险比[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），AT17(HR=1.17；95%CI，1.03-1.31)系数导致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的亚组分析中（>95百分位数或<75百分位数系数），在低风险组中，PSD作为POAG的预测指标失去了意义。
■基线VFs，在可检测到青光眼损伤之前，包含代表早期变化的隐匿性模式，这些变化可能会增加高眼压患者POAG发作和VF进展的风险。基线时高风险模式的存在改变了PSD和POAG之间的关系。基于AT的POAG预测模型可以在临床环境中提供更可解释的青光眼特异性信息。
■OHTS中有3272只眼睛。原型分析使用24-2个基线VFs，并通过交叉验证进行模型选择。计算了原型（AT）的分解系数。实施了惩罚的Cox比例风险模型来选择判别性ATs。将AT模型与OHTS模型进行比较。确定了ATs与POAG发作和VF进展之间的关联，由每年的平均偏差变化定义。
■我们选择了8494个基线VF。最优AT计数为19。AT8患病率最高的是AT9、AT11和AT7。基于AT的预测模型对POAG发作的C指数为0.75。多变量模型表明AT5的四分位数范围增加（危险比[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），AT17(HR=1.17；95%CI，1.03-1.31)系数导致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的亚组分析中（>95百分位数或<75百分位数系数），在低风险组中，PSD作为POAG的预测指标失去了意义。
■基线VFs，在可检测到青光眼损伤之前，包含代表早期变化的隐匿性模式，这些变化可能会增加高眼压患者POAG发作和VF进展的风险。基线时高风险模式的存在改变了PSD和POAG之间的关系。基于AT的POAG预测模型可以在临床环境中提供更可解释的青光眼特异性信息。
