Corneal Enantiomorphism in Normal and Keratoconus Eyes

Tuesday, April 29, 2014: 8:11 AM
Room 151A (Boston Convention and Exhibition Center)
Alain Saad, MD, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France
Emmanuel Guilbert, MD, Rothschild Foundation, Paris, France
Alice Grise-Dulac, MD, Fondation Rothschild, Paris, France
Jean Luc Febbraro, MD, Rothschild Foundation, Paris, France
Damien Gatinel, MD, Fondation Rothschild, Paris, France

Narrative Responses:

Purpose
To evaluate the ability to discriminate between normal and keratoconus corneas by analyzing intereye corneal asymmetry parameters with regards to corneal topography and tomography and define a score of similarity that outlines the normal range of asymmetry between right and left eyes.

Methods
Prospective, non-randomized, evaluation of diagnostic test study in which 102 normal corneas of 51 patients and 64 keratoconic corneas of 32 patients were included. Topographic and tomographic parameters of the right and left eye were extracted from an elevation and Placido based corneal topography. Asymmetry was determined by subtracting the right eye value from the left eye value for each variable and by considering the absolute value of the result. A discriminant function was constructed in order to separate between the normal and the keratoconic group.

Results
The mean intereye asymmetry differences were statistically significant (p< 0.001) for the following variables: SimK Max and Min, Irregularity at 3 and 5mm, Central and thinnest (TP) pachymetry, Anterior and Posterior BFS, Anterior and Posterior Elevation of the TP, Averages of thickness values of the points located at 0.5, 1,1.5, and 2mm of the TP. There were no intereye asymmetry differences regarding the decentration of the TP and the pachymetry at 2.5, 3, 3.5 and 4mm from the TP. The discriminant function reached an area under the ROC curve of 0.992, a sensitivity of 94% and a specificity of 100%.

Conclusion
A discriminant function constructed from intereye difference of three corneal indices may be accurate and useful for the topography-based detection of advanced keratoconus. In the future, incorporating such data in an automated artificial intelligence may improve the detection ability of earlier forms of keratoconus.