Journal of glaucoma · 2013
Factors influencing intermethod agreement between goldmann applanation, pascal dynamic contour, and ocular response analyzer tonometry.
Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Sullivan-Mee Michael, Lewis Sarah E, Pensyl Denise, Gerhardt Gretchen, Halverson Kathy D, Qualls Clifford
Аннотация
PURPOSE: To examine factors that influence intraocular pressure (IOP) measurement agreement between Goldmann applanation (GAT), Ocular Response Analyzer (ORA), and Pascal Dynamic Contour tonometers (DCT).
PATIENTS AND METHODS: In subjects who were diagnosed with primary open-angle glaucoma, ocular hypertension, glaucoma suspect, and normal, we used ORA, DCT, and GAT to obtain corneal hysteresis (CH), corneal resistance factor (CRF), ocular pulse amplitude, and 4 IOP values (ORA-IOPcc; ORA-IOPg; DCT-IOP; and GAT-IOP.) We also obtained corneal curvature, corneal thickness, axial length, retinal nerve fiber layer thickness, visual field parameters, diabetes diagnostic status, and topical IOP-lowering treatment data. Analysis of variance, Bland-Altman, and regression analyses were used to examine IOP agreement and associated factors.
RESULTS: In 243 eyes of the 243 subjects, mean DCT-IOP (18.73±4.92) was not different from mean ORA-IOPcc (18.96±5.41) but both were significantly higher than ORA-IOPg (16.97±5.49) and GAT-IOP (16.37±4.97). In multivariate regression models, intermethod differences between IOPg, IOPcc, and DCT-IOP were explained almost completely by variations in CH, CRF, and level of IOP (r(2)=0.98 to 0.99); conversely, intermethod variability between GAT-IOP and the other 3 IOP metrics was only partially explained by the factors evaluated in this study (r(2)=0.31 to 0.65).
CONCLUSIONS: Consistent with other studies, we found that the 4 IOP variables examined in this study are not interchangeable. The most consistent confounders of IOP measurement agreement were the ORA-measured corneal parameters, CH and CRF. Thus, accounting for these factors may be important in efforts to obtain accurate transcorneal estimates of IOP.
PATIENTS AND METHODS: In subjects who were diagnosed with primary open-angle glaucoma, ocular hypertension, glaucoma suspect, and normal, we used ORA, DCT, and GAT to obtain corneal hysteresis (CH), corneal resistance factor (CRF), ocular pulse amplitude, and 4 IOP values (ORA-IOPcc; ORA-IOPg; DCT-IOP; and GAT-IOP.) We also obtained corneal curvature, corneal thickness, axial length, retinal nerve fiber layer thickness, visual field parameters, diabetes diagnostic status, and topical IOP-lowering treatment data. Analysis of variance, Bland-Altman, and regression analyses were used to examine IOP agreement and associated factors.
RESULTS: In 243 eyes of the 243 subjects, mean DCT-IOP (18.73±4.92) was not different from mean ORA-IOPcc (18.96±5.41) but both were significantly higher than ORA-IOPg (16.97±5.49) and GAT-IOP (16.37±4.97). In multivariate regression models, intermethod differences between IOPg, IOPcc, and DCT-IOP were explained almost completely by variations in CH, CRF, and level of IOP (r(2)=0.98 to 0.99); conversely, intermethod variability between GAT-IOP and the other 3 IOP metrics was only partially explained by the factors evaluated in this study (r(2)=0.31 to 0.65).
CONCLUSIONS: Consistent with other studies, we found that the 4 IOP variables examined in this study are not interchangeable. The most consistent confounders of IOP measurement agreement were the ORA-measured corneal parameters, CH and CRF. Thus, accounting for these factors may be important in efforts to obtain accurate transcorneal estimates of IOP.