Keratoconus Screening Using Values Derived From Auto-Keratometer Measurements: A Multicenter Study.

PURPOSE: Screening of early-stage keratoconus using auto-keratometer parameters.

DESIGN

Evaluation of a screening approach.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.