A Combined Biomechanical and Tomographic Model for Identifying Cases of Subclinical Keratoconus.

PURPOSE

To develop a combined biomechanical and tomographic model for identifying eyes with subclinical keratoconus (SKC) that are categorized as normal or borderline in the Pentacam Belin/Ambrósio Enhanced Ectasia Display.

METHODS

This case-control study comprised 62 eyes with SKC and randomly selected eyes of 186 age-matched healthy controls. SKC was defined as the presence of the following: 1) normal topography, topometric indices, and slit lamp; 2) normal or borderline Belin/Ambrósio Enhanced Ectasia Display D index, back and front elevation difference; and 3) keratoconus in the fellow eye. Stepwise logistic regression analysis was performed to identify the best variable combination for detecting SKC cases from Ocular Response Analyzer and Pentacam parameters. Receiver operating characteristic curve analysis was used to determine the predictive accuracy [area under the curve (AUC)] of the model. Based on the predictors in the final logistic regression model, a linear equation was derived using the discriminant function analysis.

RESULTS

The final model (AUC: 0.948, sensitivity: 87.1%, and specificity: 91.4%) chose corneal hysteresis (CH) and D index from a total of 63 candidate variables. The final model had a higher AUC compared with D (0.933, P = 0.053) and CH (0.80, P < 0.001) alone. According to the discriminant function analysis, a higher CH was required with increasing D index to classify an eye as normal.

CONCLUSIONS

The proposed combined model provided varying cutoffs for CH and D as a function of the other. The probability plot as a function of CH and D index may be used for identifying eyes with SKC.