Duke Eye Center, Department of Ophthalmology, Duke University, Durham, North Carolina, USA.
Department of Ophthalmology, State University of Campinas, Campinas, São Paulo, Brazil.
Am J Ophthalmol. 2019 Jan;197:45-52. doi: 10.1016/j.ajo.2018.09.002. Epub 2018 Sep 17.
To propose a new methodology for classifying patient-reported outcomes in glaucoma and for quantifying the amount of visual field damage associated with disability in the disease.
Cross-sectional study.
A total of 263 patients with glaucoma were included. Vision-related disability was assessed by the National Eye Institute Visual Function Questionnaire (NEI VFQ-25). A latent class analysis (LCA) model was applied to analyze NEI VFQ-25 data and patients were divided into mutually exclusive classes according to their responses to the questionnaires. Differences in standard automated perimetry (SAP) mean deviation (MD) and integrated binocular mean sensitivity (MS) values between classes were investigated. The optimal number of classes was defined based on goodness-of-fit criteria, interpretability, and clinical utility.
The model with 2 classes, disabled and nondisabled, had the best fit with an entropy of 0.965, indicating excellent separation of classes. The disabled group had 48 (18%) patients, whereas 215 (82%) patients were classified as nondisabled. The average MD of the better eye in the disabled group was -5.98 dB vs -2.51 dB in the nondisabled group (P < .001). For the worse eye, corresponding values were -13.36 dB and -6.05 dB, respectively (P < .001).
Application of an LCA model allowed categorization of patient-reported outcomes and quantification of visual field levels associated with disability in glaucoma. A damage of approximately -6 dB for SAP MD, indicating relatively early visual field loss, may already be associated with significant disability if occurring in the better eye.
提出一种新的方法来对青光眼患者报告的结局进行分类，并量化与该疾病残疾相关的视野损害程度。
横断面研究。
共纳入 263 例青光眼患者。采用国家眼科研究所视觉功能问卷（NEI VFQ-25）评估与视觉相关的残疾情况。应用潜在类别分析（LCA）模型分析 NEI VFQ-25 数据，并根据患者对问卷的回答将其分为互斥的类别。研究了不同类别间标准自动视野计（SAP）平均偏差（MD）和综合双眼平均敏感度（MS）值的差异。基于拟合优度标准、可解释性和临床实用性来定义最佳类别数。
具有残疾和非残疾两类的模型拟合度最佳，其熵为 0.965，表明类别间具有极好的分离度。残疾组有 48 例（18%）患者，而非残疾组有 215 例（82%）患者。残疾组较好眼的平均 MD 为-5.98 dB，而非残疾组为-2.51 dB（P<0.001）。对于较差眼，相应的值分别为-13.36 dB 和-6.05 dB（P<0.001）。
应用 LCA 模型可对患者报告的结局进行分类，并量化与青光眼残疾相关的视野水平。SAP MD 大约-6 dB 的损害，表明视野的早期损失，如果发生在较好眼，可能已经与显著的残疾相关。
