Department of Preventive Medicine, Tokai University School of Medicine, Kanagawa, Japan.
Department of Ophthalmology, The Jikei University School of Medicine, Tokyo, Japan.
Transl Vis Sci Technol. 2022 Aug 1;11(8):8. doi: 10.1167/tvst.11.8.8.
To develop and validate a risk score assessable in real-time using only retinal thickness-related values measured by spectral domain optical coherence tomography alone for use in population-based glaucoma mass screenings.
A total of 7572 participants (aged 35-74 years) underwent spectral domain optical coherence tomography examination annually between 2016 to 2021 in a population-based setting. We selected 284 glaucoma cases and 284 controls, matched by age and sex, from 11,487 scans in 2016. We conducted multivariable logistic regression with backward stepwise selection of retinal thickness-related variables to develop the diagnostic models. The developed risk scores were applied to all participants in 2018 (9720 eyes), and we randomly selected 723 scans for validation. Additional validation using the Humphrey field analyzer was conducted on 129 eyes in 2020. We assessed the models using sensitivity, specificity, the area under the receiver operating characteristic curve and positive and negative predictive values.
The best-predicting model achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.96-0.98) with a sensitivity of 0.93 and specificity of 0.91. The validation dataset showed a positive predictive value of 90.8% for high-risk scorers, corresponding to 6.2% of the population, and negative predictive value of 88.2% for low-risk scorers, corresponding to 85.2%. Sensitivity and specificity for glaucoma diagnosis were 0.85 and 0.91, when we set the risk score cut-off at 90 points out of 100.
This risk score could be used as a valid index for glaucoma screening in a population-based setting.
The score is feasible by installing a simple computer application on an existing spectral domain optical coherence tomography and will help to improve the accuracy and efficiency of glaucoma screening.
开发并验证一种风险评分系统，该系统仅使用谱域光相干断层扫描测量的视网膜厚度相关值即可实时评估，用于基于人群的青光眼大规模筛查。
共有 7572 名参与者（年龄 35-74 岁）在 2016 年至 2021 年期间在基于人群的环境中每年接受谱域光相干断层扫描检查。我们从 2016 年的 11487 次扫描中选择了 284 例青光眼病例和 284 例匹配年龄和性别的对照者。我们进行了多变量逻辑回归，使用视网膜厚度相关变量的逐步后退选择来开发诊断模型。将开发的风险评分应用于 2018 年的所有参与者（9720 只眼），并随机选择 723 次扫描进行验证。2020 年还使用 Humphrey 视野分析仪对 129 只眼进行了额外验证。我们使用敏感性、特异性、接收者操作特征曲线下的面积以及阳性和阴性预测值来评估模型。
最佳预测模型的接收者操作特征曲线下面积为 0.97（95%置信区间，0.96-0.98），敏感性为 0.93，特异性为 0.91。验证数据集显示，高危评分者的阳性预测值为 90.8%，对应于人群的 6.2%，低危评分者的阴性预测值为 88.2%，对应于人群的 85.2%。当我们将风险评分截止值设定为 100 分中的 90 分时，用于诊断青光眼的敏感性和特异性分别为 0.85 和 0.91。
该风险评分可作为基于人群的青光眼筛查的有效指标。
本文的难点在于对专业词汇的把握，如“spectral domain optical coherence tomography”（谱域光相干断层扫描），“diagnostic models”（诊断模型），“receiver operating characteristic curve”（接收者操作特征曲线），“positive and negative predictive values”（阳性和阴性预测值）等。因此，在翻译时，我结合上下文，查阅相关资料，力求译文专业、准确。
