Keratoconus diagnosis with optical coherence tomography–based pachymetric scoring system

Abstract

Purpose: To develop an optical coherence tomography (OCT) pachymetry map–based keratoconus risk scoring system.

Settings: Doheny Eye Institute, University of Southern California, Los Angeles, California, and Brass Eye Center, New York, New York, USA; Department of Ophthalmology, Affiliated Eye Hospital of Wenzhou Medical College, Wenzhou, China.

Design: Cross-sectional study.

Methods: Fourier-domain OCT was used to acquire corneal pachymetry maps in normal and keratoconus subjects. Pachymetric variables were minimum, minimum−median, superior–inferior (S–I), superonasal–inferotemporal (SN–IT), and the vertical location of the thinnest cornea (Ymin). A logistic regression formula and a scoring system were developed based on these variables. Keratoconus diagnostic accuracy was measured by the area under the receiver operating characteristic (ROC) curve.

Results: One hundred thirty-three eyes of 67 normal subjects and 82 eyes from 52 keratoconus subjects were recruited. The keratoconus logistic regression formula = 0.543 × minimum + 0.541 × (S–I) − 0.886 × (SN–IT) + 0.886 × (minimum–median) + 0.0198 × Ymin. The formula gave better diagnostic power with the area under the ROC than the best single variable (formula = 0.975, minimum = 0.942; P<.01). The diagnostic power with the area under the ROC of the keratoconus risk score (0.949) was similar to that of the formula (P=.08).

Conclusion: The OCT corneal pachymetry map–based logistic regression formula and the keratoconus risk scoring system provided high accuracy in keratoconus detection. These methods may be useful in keratoconus screening.

Conflict of interest statement

Financial and proprietary interest:

Oregon Health and Science University (OHSU), David Huang, Yan Li, and Maolong Tang have a significant financial interest in Optovue, Inc. (Fremont, CA, USA), a company that may have a commercial interest in the results of this research and technology. These potential conflicts of interest have been reviewed and managed by OHSU. Robert Brass receives speaker honoraria from Optovue, Inc. Bing Qin, Shihao Chen, Qinmei Wang, Xinbo Zhang and Xiaoyu Wang have no proprietary interest in the topic of this manuscript.

Figures

Figure 1

Optical coherence tomography corneal pachymetry map of a keratoconus subject showing pachymetric variables. The five individual pachymetric variables are 1. minimum = location of minimum corneal thickness (marked as ※), 2. minimum-median = the minimum corneal thickness minus the mean corneal thickness averaged from central 5 mm diameter, 3. The S-I: The average thickness of the superior (S) octant minus that of the inferior (I) octant, 4. The SN-IT: The average thickness of the SN octant minus that of the IT octant (variables 3 and 4 are measured within 2–5mm diameters, marked as red circles on the right), 5. Ymin = vertical location of the minimum (marked as red double arrows on the left).

Figure 2

Distribution of eyes evaluated with the keratoconus risk scoring system table.

Figure 3

Diagnostic analysis of the keratoconus logistic regression formula and the keratoconus risk score. The keratoconus logistic regression formula had an area under the receiver operating characteristic curve (AROC) value of 0.975. The keratoconus risk score had an AROC value of 0.949 (P = 0.08).