Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
Shiley Eye Institute, Hamilton Glaucoma Center and Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Am J Ophthalmol. 2018 Oct;194:46-53. doi: 10.1016/j.ajo.2018.07.005. Epub 2018 Jul 25.
To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open-angle glaucoma (POAG) and healthy (H) eyes.
Development and evaluation of a diagnostic test with machine learning.
Subjects: From 435 subjects (193 healthy and 242 POAG), 136 POAG and 136 age-matched healthy subjects were selected. Subjects with contraindications for CLS wear were excluded.
This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on 1 eye. Statistical and physiological CLS parameters were derived from the signal recorded. CLS parameters frequently associated with the presence of POAG were identified using a random forest modeling approach.
Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters and Start IOP, as well as a feature set with CLS parameters and Start IOP combined.
The CLS parameters feature set discriminated POAG from H eyes with mean ROC AUCs of 0.611, confidence interval (CI) 0.493-0.722. Larger values of a given CLS parameter were in general associated with a diagnosis of POAG. The Start IOP feature set discriminated between POAG and H eyes with a mean ROC AUC of 0.681, CI 0.603-0.765. The combined feature set was the best indicator of POAG with an ROC AUC of 0.759, CI 0.654-0.855. This ROC AUC was statistically higher than for CLS parameters or Start IOP feature sets alone (both P < .0001).
CLS recordings contain information complementary to IOP that enable discrimination between H and POAG. The feature set combining CLS parameters and Start IOP provide a better indication of the presence of POAG than each of the feature sets separately. As such, the CLS may be a new biomarker for POAG.
验证基于隐形眼镜传感器（CLS）的 24 小时眼容积变化曲线的假设，即该曲线提供的信息可补充眼压（IOP），从而区分原发性开角型青光眼（POAG）和健康（H）眼。
开发和评估具有机器学习功能的诊断测试。
受试者：在 435 名受试者（193 名健康和 242 名 POAG）中，选择 136 名 POAG 和 136 名年龄匹配的健康受试者。排除不适合 CLS 佩戴的受试者。
这是对 24 项前瞻性临床研究和登记研究数据的汇总分析。所有受试者的 1 只眼接受 24 小时 CLS 记录。从记录的信号中得出统计和生理 CLS 参数。使用随机森林建模方法确定与 POAG 存在相关的 CLS 参数。
包括 CLS 参数和起始 IOP 的特征集以及包括 CLS 参数和起始 IOP 的特征集的接收器操作特征曲线（ROC）下面积（AUC）。
CLS 参数特征集以 0.611 的平均 ROC AUC（置信区间 [CI]，0.493-0.722）区分 POAG 与 H 眼，较大的 CLS 参数值通常与 POAG 诊断相关。起始 IOP 特征集以 0.681 的平均 ROC AUC（CI，0.603-0.765）区分 POAG 与 H 眼。组合特征集是 POAG 的最佳指标，ROC AUC 为 0.759（CI，0.654-0.855）。该 ROC AUC 统计学上高于 CLS 参数或起始 IOP 特征集（均 P <.0001）。
CLS 记录包含与 IOP 互补的信息，可区分 H 和 POAG。与单独使用 CLS 参数或起始 IOP 特征集相比，组合 CLS 参数和起始 IOP 的特征集更能指示 POAG 的存在。因此，CLS 可能是 POAG 的新生物标志物。
