Identification of Disease Progression Specific Biomarkers and Their Pharmacologic Modulation for Keratoconus.

The screening of keratoconus involves keratoconus related clinical signs like retinoscopy scissors reflex, Munson sign, stromal thinning, Vogt's striae, and Fleischer's ring, but corneal topography is the most useful method in the diagnosis of keratoconus, especially in the absence of clinical signs.

Several devices are currently available for detecting early keratoconus by measuring anterior and posterior corneal topography and elevation(Mihaltz et al. 2009; Ishii et al. 2012). Corneal topographic and tomographic techniques which generate color-coded maps and topographic indices, are the most sensitive devices for confirming the diagnosis of keratoconus(Rabinowitz 1998; Rao et al. 2002) were used for diagnosis in this study. In addition, videokeratography has been shown to identify Forme fruste keratoconus (FFKC) in the absence of clinical signs of keratoconus. Videokeratographic indices such as the Klyce/Maeda criteria, the Rabinowitz criteria, and others have been developed to quantitatively analyze videokeratography and screen for keratoconus(Rao et al. 2002). These indices have been shown to identify keratoconus with a high degree of sensitivity and specificity. The Orbscan II is a three-dimensional slit-scan topography system for analysis of the corneal surfaces and anterior chamber and has been used on all patients in the study. It uses calibrated video and a scanning slit beam to measure x, y, and z locations of several thousand points. These points are used to construct topographic maps(Rao et al. 2002). The Pentacam (Oculus Inc) is a corneal tomographer technology which generates data on topograophy and elevation of anterior and posterior using a rotating Scheimpflug camera which has also been used on all subjects enrolled in this study. Various diagnostic parameters are available for keratoconus diagnosis depending on what mode of topography is being used. Maeda and Klyce designed a system to detect keratoconus. The system, which is based on linear discriminant analysis and a binary decision tree, identifies the map as representing keratoconus or nonkeratoconus and, based on a value from the discriminant analysis (the KPI), assigns the map an index expressed as a percentage that suggests the severity of keratoconus. At this time, however, Keratoconus Severity Index (KSI) and the Amsler-Krumeich classification are the most popular methods for grading keratoconus severity(Mihaltz et al. 2009; Ishii et al. 2012). KSI is based on indices estimated by a curvature map using Placido disk-based corneal topography, and Amsler-Krumeich classification defines the stage of keratoconus using biomicroscopy, mean central keratometry reading, spherical and cylindrical refraction change, and corneal thickness(Mihaltz et al. 2009; Ishii et al. 2012). This is the index that has been used for the final gradation of keratoconus stages of all subjects in this study.