Purpose: To define a new method for grading severity of keratoconus, the Keratoconus Severity Score (KSS).
Methods: A rationale for grading keratoconus severity was developed using common clinical markers plus 2 corneal topographic indices, creating a 0 to 5 severity score. An initial test set of 1012 eyes, including normal eyes, eyes with abnormal corneal and topographic findings but not keratoconus, and eyes with keratoconus having a wide range of severity, was used to determine cutpoints for the KSS. Validation set 1, comprising data from 128 eyes, was assigned a KSS and compared with a clinician's ranking of severity termed the "gold standard" to determine if the scale fairly represented how a clinician would grade disease severity. kappa statistics, sensitivity, and specificity were calculated. A program was developed to automate the determination of the score. This was tested against a manual assignment of KSS in 2121 (validation set 2) eyes from the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study, as well as normal eyes and abnormal eyes without keratoconus. Ten percent of eyes underwent repeat manual assignment of KSS to determine the variability of manual assignment of a score.
Results: From initial assessments, the KSS used 2 corneal topography indices: average corneal power and root mean square (RMS) error for higher-order Zernike terms derived from the first corneal surface wavefront. Clinical signs including Vogt striae, Fleischer rings, and corneal scarring were also included. Last, a manual interpretation of the map pattern was included. Validation set 1 yielded a kappa statistic of 0.904, with sensitivities ranging from 0.64 to 1.00 and specificities ranging from 0.93 to 0.98. The sensitivity and specificity for determining nonkeratoconus from keratoconus were both 1.00. Validation set 2 showed kappa statistics of 0.94 and 0.95 for right and left eyes, respectively. Test-retest analysis yielded kappa statistics of 0.84 and 0.83 for right and left eyes, respectively.
Conclusion: A simple and reliable grading system for keratoconus was developed that can be largely automated. Such a grading scheme could be useful in genetic studies for a complex trait such as keratoconus requiring a quantitative measure of disease presence and severity.