Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan; Nagoya Eye Clinic, Nagoya, Japan.
Nagoya Eye Clinic, Nagoya, Japan.
Am J Ophthalmol. 2020 Jul;215:127-134. doi: 10.1016/j.ajo.2020.02.017. Epub 2020 Feb 28.
Screening of early-stage keratoconus using auto-keratometer parameters.
Evaluation of a screening approach.
At 5 major centers in Japan, we enrolled 123 eyes of 123 patients with Amsler-Krumeich classification stage 1 (<50 years of age [average 26.36 ± 8.68 years]; 84/39 male/female) and 205 eyes of 205 healthy subjects (average age 26.20 ± 7.34 years, 139/66 male/female). Participants were divided 2:1 into a prediction group and an application group. In the prediction group, multivariate logistic regression analysis was performed with keratoconus diagnosis as the dependent variable, and auto-keratometer parameters including average K, steep K, flat K, astigmatism, and astigmatic axis (no, with-the-rule, against-the-rule, and oblique) as independent variables. The diagnostic probability determined by regression analysis was defined as the keratometer keratoconus index. The cutoff value was determined from the receiver operating characteristic curve. This prediction equation was evaluated in the application group. Our primary outcome measure was the accuracy of the prediction equation for discriminating keratoconus from normal eyes.
The selected explanatory variables were steep K (partial regression coefficient [β] 1.284, odds ratio [OR] 3.610), flat K (β -0.618, OR 0.539), and with-the-rule astigmatism (β -3.163, OR 0.042). The area under the receiver operating characteristic curve of keratometer keratoconus index was 0.90, which was significantly better than individual parameters (P < .001). The sensitivity and specificity values in the application group were 85.0% and 86.7%, respectively.
Although the sensitivity/specificity was not high, the new prediction equation using auto-keratometer-derived parameters enabled better discrimination of early-stage keratoconus than the isolated parameters.
使用自动角膜曲率计参数筛查早期圆锥角膜。
筛查方法的评估。
在日本的 5 个主要中心，我们招募了 123 名 Amsler-Krumeich 1 级（<50 岁[平均 26.36±8.68 岁]；84/39 男/女）和 205 名健康受试者（平均年龄 26.20±7.34 岁，139/66 男/女）的 123 只眼和 205 只眼。参与者以 2:1 的比例分为预测组和应用组。在预测组中，以角膜圆锥体诊断为因变量，以自动角膜曲率计参数（包括平均 K 值、陡峭 K 值、平坦 K 值、散光和散光轴（无、顺规、逆规和斜轴））为自变量，进行多元逻辑回归分析。由回归分析确定的诊断概率定义为角膜曲率计圆锥角膜指数。从受试者工作特征曲线确定临界值。该预测方程在应用组中进行评估。我们的主要观察指标是预测方程区分正常眼和圆锥角膜的准确性。
所选的解释变量是陡峭的 K 值（偏回归系数[β]1.284，比值比[OR]3.610）、平坦的 K 值（β-0.618，OR 0.539）和顺规散光（β-3.163，OR 0.042）。角膜曲率计圆锥角膜指数的受试者工作特征曲线下面积为 0.90，明显优于各参数（P<0.001）。在应用组中，灵敏度和特异性值分别为 85.0%和 86.7%。
虽然敏感性/特异性不高，但使用自动角膜曲率计参数得出的新预测方程能够更好地区分早期圆锥角膜，优于孤立的参数。
