PURPOSE: The Ocular Response Analyzer (ORA) is a newly introduced tonometer that uniquely measures and then integrates corneal biomechanical data into its intraocular pressure (IOP) estimates in an effort to improve accuracy of IOP assessment. This study was devised to investigate whether ORA-derived IOP and corneal biomechanical variables might be useful in discriminating between subjects with and without primary open-angle glaucoma (GLC). METHODS: All patients seen in the Albuquerque VAMC eye clinic over a 10-week period who demonstrated acceptable ORA signal profiles were retrospectively identified. In subjects classified as normal (NML), ocular hypertension (OH), glaucoma suspect (GS), and GLC, the following variables were compared: age, ethnicity, Goldmann IOP, central corneal thickness (CCT), and ORA-derived data: Goldmann-correlated IOP (IOPg), corneal-compensated IOP (IOPcc), corneal resistance factor (CRF), corneal hysteresis (CH), and difference between IOPcc and IOPg (DIOP; IOPcc - IOPg). RESULTS: Right eyes in 71 NML, 58 OH, 70 GS, and 99 GLC subjects were studied. Using analysis of variance, higher mean age, higher mean DIOP, and lower mean CH were found in the GLC group compared with OH, GS, and NML groups. In multivariate regression analyses, factors that independently discriminated between groups were: age, IOPcc, and DIOP (GLC vs. NML); age and IOPcc (GLC vs. GS); age and CRF (GLC vs. OH). When DIOP was left out of the models, CH replaced DIOP in the GLC vs. NML analysis with nearly equal statistical power. CONCLUSIONS: Our results suggest that ORA-generated parameters may be useful for differentiating subjects with and without GLC. Furthermore, the discriminatory power of each ORA variable seems to depend on the diagnostic groups that are being compared. Finally, our findings also suggest that measured IOP may be significantly underestimated in glaucoma patients compared with non-glaucoma patients.
PURPOSE: The Ocular Response Analyzer (ORA) is a newly introduced tonometer that uniquely measures and then integrates corneal biomechanical data into its intraocular pressure (IOP) estimates in an effort to improve accuracy of IOP assessment. This study was devised to investigate whether ORA-derived IOP and corneal biomechanical variables might be useful in discriminating between subjects with and without primary open-angle glaucoma (GLC). METHODS: All patients seen in the Albuquerque VAMC eye clinic over a 10-week period who demonstrated acceptable ORA signal profiles were retrospectively identified. In subjects classified as normal (NML), ocular hypertension (OH), glaucoma suspect (GS), and GLC, the following variables were compared: age, ethnicity, Goldmann IOP, central corneal thickness (CCT), and ORA-derived data: Goldmann-correlated IOP (IOPg), corneal-compensated IOP (IOPcc), corneal resistance factor (CRF), corneal hysteresis (CH), and difference between IOPcc and IOPg (DIOP; IOPcc - IOPg). RESULTS: Right eyes in 71 NML, 58 OH, 70 GS, and 99 GLC subjects were studied. Using analysis of variance, higher mean age, higher mean DIOP, and lower mean CH were found in the GLC group compared with OH, GS, and NML groups. In multivariate regression analyses, factors that independently discriminated between groups were: age, IOPcc, and DIOP (GLC vs. NML); age and IOPcc (GLC vs. GS); age and CRF (GLC vs. OH). When DIOP was left out of the models, CH replaced DIOP in the GLC vs. NML analysis with nearly equal statistical power. CONCLUSIONS: Our results suggest that ORA-generated parameters may be useful for differentiating subjects with and without GLC. Furthermore, the discriminatory power of each ORA variable seems to depend on the diagnostic groups that are being compared. Finally, our findings also suggest that measured IOP may be significantly underestimated in glaucomapatients compared with non-glaucomapatients.
Authors: Carolina N Susanna; Alberto Diniz-Filho; Fábio B Daga; Bianca N Susanna; Feilin Zhu; Nara G Ogata; Felipe A Medeiros Journal: Am J Ophthalmol Date: 2018-01-02 Impact factor: 5.258