Abstract
Purpose:To determine the predictive ability of different data measured by the Galilei dual Scheimpflug analyzer in differentiating subclinical keratoconus and keratoconus from normal corneas.Methods:This prospective comparative study included 136 normal eyes, 23 eyes with subclinical keratoconus, and 51 keratoconic eyes. In each eye, keratometric values, pachymetry, elevation parameters and surface indices were evaluated. Receiver operating characteristic (ROC) curves were calculated and quantified by using the area under the curve (AUC) to compare the sensitivity and specificity of the measured parameters and to identify optimal cutoff points for differenciating subclinical keratoconus and keratoconus from normal corneas. Several model structures including keratometric, pachymetric, elevation parameters and surface indices were analyzed to find the best model for distinguishing subclinical and clinical keratoconus. The data sets were also examined using the non-parametric “classification and regression tree” (CRT) technique for the three diagnostic groups.Results:Nearly all measured parameters were strong enough to distinguish keratoconus. However, only the radius of best fit sphere and keratometry readings had an acceptable predictive accuracy to differentiate subclinical keratoconus. Elevation parameters and surface indices were able to differentiate keratoconus from normal corneas in 100% of eyes. Meanwhile, none of the parameter sets could effectively discriminate subclinical keratoconus; a 3-factor model including keratometric variables, elevation data and surface indices provided the highest predictive ability for this purpose.Conclusion:Surface indices measured by the Galilei analyzer can effectively differentiate keratoconus from normal corneas. However, a combination of different data is required to distinguish subclinical keratoconus.
Highlights
The diagnosis of subclinical keratoconus and keratoconus was based on clinical slit lamp findings and characteristic patterns based on Placido disk corneal topography (Tomey, EM‐3000, version 4.20, Nagoya, Japan)
corrected distance visual acuity (CDVA) was comparable between normal subjects (0.0 ± 0.11 logMAR) and those with subclinical keratoconus (0.04 ± 0.12 logMAR, P = 0.68)
CDVA was significantly lower in the keratoconus group (0.34 ± 0.28 logMAR) as compared to normal subjects (P < 0.001) and subjects with subclinical keratoconus (P < 0.001)
Summary
The terms forme fruste keratoconus, subclinical keratoconus and keratoconus suspect have been used to designate early stages of keratoconus which do not become manifest on biomicroscopy, but demonstrate. Galilei Imaging in Subclinical KCN and KCN; Feizi et al subtle topographic features comparable to those of clinical keratoconus.[1,2,3] Studies suggest that subclinical or clinical keratoconus is found in 1 to 6% of myopic patients undergoing refractive surgery.[4,5,6,7] Several corneal imaging techniques have evolved, mainly to distinguish subclinical or clinical keratoconus among patients scheduled for refractive surgery because operation on an undetected keratoconic cornea is a major cause of post‐refractive surgery ectasia.[8]
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