Perucho-González Lucía, Sáenz-Francés Federico, Morales-Fernández Laura, Martínez-de-la-Casa José María, Méndez-Hernández Carmen D, Santos-Bueso Enrique, Brookes John L, García-Feijoó Julián
Ophthalmology Department, Clinico San Carlos University Hospital, Sanitary Research Institute of the San Carlos Clinical Hospital, Madrid, Spain.
Glaucoma Department, Moorfields Eye Hospital & Great Ormond Street Hospital for Children, London, UK.
Acta Ophthalmol. 2017 Mar;95(2):e107-e112. doi: 10.1111/aos.13212. Epub 2016 Aug 29.
To determine whether a set of ocular morphometric and biomechanical variables are able to discriminate between healthy volunteers and patients suffering from primary congenital glaucoma (PCG).
Case-control study in which 66 patients with PCG and 94 age-matched healthy subjects were evaluated using ocular response analyser (ORA) to record corneal biomechanical properties. Topographic corneal variables were obtained using the Pentacam in both groups. To determine the ability to discern between both groups, a multivariate binary logistic model was constructed. The outcome was the diagnosis of PCG and the predictors; the corneal variables analysed along with their first-term interactions. Sensitivity and specificity of this model along with the area under the receiver characteristic operating curve (AUC of ROC) were determined.
The best model to discriminate between both groups included the following predictors: corneal hysteresis (CH), corneal resistance factor (CRF), posterior maximum elevation (PME), anterior maximum elevation (AME) and central corneal thickness (CCT). This model, for a cut-point of 50%, presents a sensitivity of 86.67%, a specificity of 86.89% and an AUC of the ROC curve of 93.16% [95% confidence interval (CI): 88.97-97.35]. The adjusted odds ratios of those predictors which showed a significant discriminating capacity were as follows: for CH, 0.27 (95% confidence interval: 0.15-0.46); for CRF, 2.13 (95% CI: 1.33-3.40); for PME, 1.06 (95% CI: 1.01-1.12); and for AME, 1.35 (95% CI: 1.10-1.66).
Corneal hysteresis (CH), CRF, PME and AME are able to discern between patients with PCG and healthy controls. This fact suggests that there are structural and biomechanical differences between these groups.
确定一组眼部形态学和生物力学变量是否能够区分健康志愿者和原发性先天性青光眼(PCG)患者。
进行病例对照研究,使用眼反应分析仪(ORA)评估66例PCG患者和94例年龄匹配的健康受试者,以记录角膜生物力学特性。两组均使用Pentacam获取角膜地形图变量。为了确定区分两组的能力,构建了多变量二元逻辑模型。结果是PCG的诊断,预测因素为分析的角膜变量及其一阶交互作用。确定了该模型的敏感性和特异性以及受试者特征操作曲线下面积(ROC曲线的AUC)。
区分两组的最佳模型包括以下预测因素:角膜滞后(CH)、角膜阻力因子(CRF)、后表面最大高度(PME)、前表面最大高度(AME)和中央角膜厚度(CCT)。该模型在切点为50%时,敏感性为86.67%,特异性为86.89%,ROC曲线的AUC为93.16%[95%置信区间(CI):88.97 - 97.35]。显示出显著区分能力的那些预测因素的调整比值比如下:CH为0.27(95%置信区间:0.15 - 0.46);CRF为2.13(95% CI:1.33 - 3.40);PME为1.06(95% CI:1.01 - 1.12);AME为1.35(95% CI:1.10 - 1.66)。
角膜滞后(CH)、CRF、PME和AME能够区分PCG患者和健康对照。这一事实表明这些组之间存在结构和生物力学差异。