Department of Ophthalmology and Visual Sciences, Washington University School of Medicine in St Louis, St Louis, Missouri.
Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, Missouri.
JAMA Ophthalmol. 2020 Jul 1;138(7):780-788. doi: 10.1001/jamaophthalmol.2020.1902.
The contribution of long-term intraocular pressure (IOP) variability to the development of primary open-angle glaucoma is still controversial.
To assess whether long-term IOP variability data improve a prediction model for the development of primary open-angle glaucoma (POAG) in individuals with untreated ocular hypertension.
DESIGN, SETTING, AND PARTICIPANTS: This post hoc secondary analysis of 2 randomized clinical trials included data from 709 of 819 participants in the observation group of the Ocular Hypertension Treatment Study (OHTS) followed up from February 28, 1994, to June 1, 2002, and 397 of 500 participants in the placebo group of the European Glaucoma Prevention Study (EGPS) followed up from January 1, 1997, to September 30, 2003. Data analyses were completed between January 1, 2019, and March 15, 2020.
The original prediction model for the development of POAG included the following baseline factors: age, IOP, central corneal thickness, vertical cup-disc ratio, and pattern SD. This analysis tested whether substitution of baseline IOP with mean follow-up IOP, SD of IOP, maximum IOP, range of IOP, or coefficient of variation IOP would improve predictive accuracy.
The C statistic was used to compare the predictive accuracy of multivariable landmark Cox proportional hazards regression models for the development of POAG.
Data from the OHTS consisted of 97 POAG end points from 709 of 819 participants (416 [58.7%] women; 177 [25.0%] African American and 490 [69.1%] white; mean [SD] age, 55.7 [9.59] years; median [range] follow-up, 6.9 [0.96-8.15] years). Data from the EGPS consisted of 44 POAG end points from 397 of 500 participants in the placebo group (201 [50.1%] women; 397 [100%] white; mean [SD] age, 57.8 [9.76] years; median [range] follow-up, 4.9 [1.45-5.76] years). The C statistic for the original prediction model was 0.741. When a measure of follow-up IOP was substituted for baseline IOP in this prediction model, the C statistics were as follows: mean follow-up IOP, 0.784; maximum IOP, 0.781; SD of IOP, 0.745; range of IOP, 0.741; and coefficient of variation IOP, 0.729. The C statistics in the EGPS were similarly ordered. No measure of IOP variability, when added to the prediction model that included mean follow-up IOP, age, central corneal thickness, vertical cup-disc ratio, and pattern SD, increased the C statistic by more than 0.007 in either cohort.
Evidence from the OHTS and the EGPS suggests that long-term variability does not add substantial explanatory power to the prediction model as to which individuals with untreated ocular hypertension will develop POAG.
长期眼压（IOP）变异性对原发性开角型青光眼发展的贡献仍存在争议。
评估长期 IOP 变异性数据是否能改善未经治疗的高眼压症患者发生原发性开角型青光眼（POAG）的预测模型。
设计、地点和参与者：这是对眼科高血压治疗研究（OHTS）观察组的 709 名参与者中的 709 名（281 名女性；58.7%）和欧洲青光眼预防研究（EGPS）安慰剂组的 397 名参与者中的 397 名（100%）进行的 2 项随机临床试验的事后二次分析，随访时间为 1994 年 2 月 28 日至 2002 年 6 月 1 日和 1997 年 1 月 1 日至 2003 年 9 月 30 日。数据分析于 2019 年 1 月 1 日至 2020 年 3 月 15 日完成。
POAG 发生的原始预测模型包括以下基线因素：年龄、IOP、中央角膜厚度、垂直杯盘比和模式标准差。本分析测试了用平均随访 IOP、IOP 的标准差、最大 IOP、IOP 范围或 IOP 的变异系数替代基线 IOP 是否会提高预测准确性。
C 统计量用于比较多变量标志 Cox 比例风险回归模型对 POAG 发生的预测准确性。
OHTS 数据包括 709 名参与者中的 97 个 POAG 终点（416 名女性；25.0%的非洲裔美国人，69.1%的白人；平均[标准差]年龄为 55.7[9.59]岁；中位数[范围]随访时间为 6.9[0.96-8.15]年）。EGPS 数据包括 500 名安慰剂组参与者中的 44 个 POAG 终点（201 名女性；100%的白人；平均[标准差]年龄为 57.8[9.76]岁；中位数[范围]随访时间为 4.9[1.45-5.76]年）。原始预测模型的 C 统计量为 0.741。当在该预测模型中用随访 IOP 的某个指标替代基线 IOP 时，C 统计量如下：平均随访 IOP，0.784；最大 IOP，0.781；IOP 的标准差，0.745；IOP 范围，0.741；IOP 的变异系数，0.729。EGPS 的 C 统计量也同样排列。在 OHTS 和 EGPS 中，没有任何 IOP 变异性指标，当添加到包括平均随访 IOP、年龄、中央角膜厚度、垂直杯盘比和模式 SD 的预测模型中时，在任何队列中都没有增加超过 0.007 的 C 统计量。
OHTS 和 EGPS 的证据表明，长期变异性并不能为预测模型提供实质性的解释能力，该模型预测哪些未经治疗的高眼压症患者会发展为 POAG。
