Corneal Biomechanics in Ectatic Diseases: Refractive Surgery Implications.

BACKGROUND: Ectasia development occurs due to a chronic corneal biomechanical decompensation or weakness, resulting in stromal thinning and corneal protrusion. This leads to corneal steepening, increase in astigmatism, and irregularity. In corneal refractive surgery, the detection of mild forms of ectasia pre-operatively is essential to avoid post-operative progressive ectasia, which also depends on the impact of the procedure on the cornea. METHOD: The advent of 3D tomography is proven as a significant advancement to further characterize corneal shape beyond front surface topography, which is still relevant. While screening tests for ectasia had been limited to corneal shape (geometry) assessment, clinical biomechanical assessment has been possible since the introduction of the Ocular Response Analyzer (Reichert Ophthalmic Instruments, Buffalo, USA) in 2005 and the Corvis ST (Oculus Optikgerate GmbH, Wetzlar, Germany) in 2010. Direct clinical biomechanical evaluation is recognized as paramount, especially in detection of mild ectatic cases and characterization of the susceptibility for ectasia progression for any cornea. CONCLUSIONS: The purpose of this review is to describe the current state of clinical evaluation of corneal biomechanics, focusing on the most recent advances of commercially available instruments and also on future developments, such as Brillouin microscopy.(undefined)info:eu-repo/semantics/publishedVersio

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

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; r§ssup§2§esup§ = 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; r§ssup§2§esup§ = 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