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. 2014 Jan 30:14:11.
doi: 10.1186/1471-2415-14-11.

Biomechanical parameters of the cornea measured with the Ocular Response Analyzer in normal eyes

Aachal Kotecha  1 , Richard A Russell, Angelos Sinapis, Sayeh Pourjavan, Dimitros Sinapis, David F Garway-Heath
Affiliations

Biomechanical parameters of the cornea measured with the Ocular Response Analyzer in normal eyes

Aachal Kotecha et al. BMC Ophthalmol. .

Abstract

Background: To evaluate the relationships between Reichert Ocular Response Analyzer (ORA) parameters corneal hysteresis (CH) and corneal response factor (CRF) and ocular dimensions, age and intraocular pressure.

Methods: Two hundred and twelve eyes of 212 participants with no ocular pathology had CH and CRF measured with the ORA. Intraocular pressure (IOP) was measured with the Dynamic Contour tonometer and central corneal thickness (CCT) was also evaluated. Partial least squares linear regression (PLSLR) analyses were performed to examine the relationships between each response variable, CH and CRF, and the predictor variables age, corneal curvature (CC), axial length (AL), CCT and IOP.

Results: CH was positively associated with CCT and negatively associated with age (scaled coefficients: CCT 0.62, p < 0.0001; age -0.55, p <0.0001; r2 = 0.25). CRF was positively associated with CCT and DCT IOP and negatively associated with age and AL (scaled coefficients: CCT 0.89, p < 0.0001; DCT IOP 0.46, p < 0.01; age - 0.60, p < 0.0001; AL -0.37, p < 0.01; r2 = 0.43). There was no significant association between CC and CH or CRF.

Conclusions: The study suggests that age and CCT are strongly associated with CH and CRF, and that the latter is also influenced by AL and IOP. However, the variables studied could explain only 25% and 43% of the measured variation in CH and CRF, respectively, suggesting other factors also affect the values of these measurements.

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Figures

Figure 1
Figure 1
Cross-validated RMSEP curves for PLSLR models. These graphs illustrate the effect of the number of components on the precision of the CH (A) and CRF (B) regression models. In both models, the prediction error was minimized using four components.
Figure 2
Figure 2
Scatterplot showing the prediction performance of the PLSLR in the test dataset. Graphs show the PLSLR prediction performance of CH (A) and CRF (B). The dashed line represents the line of unity. If the prediction model was 100% accurate, all data points would fall on this line. The dotted lines indicate the 95% confidence limits of the Normal distribution; these are ±1.24 and ±1.50 for CH and CRF, respectively. Compared with the CH model, predicted values of CRF are close to observed values, indicating that the studied variables can explain a large proportion of the variation in CRF measurements.
See this image and copyright information in PMC

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