ABSTRACT
Purpose: To study the relationship between intraocular pressure (IOP) and mean ocular perfusion pressure (MOPP) in patients with POAG and NTG. The secondary objective was to identify other contributory ischemic factors. Methods: This was an observational cross?sectional study from a tertiary eye hospital in patients who underwent full?day diurnal variation of tension (DVT). Blood pressure (BP) and IOP measurements were done every 3 h over 24 h. Mean arterial pressure (MAP) and MOPP were calculated. The nocturnal dip in BP was assessed; patients were classified as non?dippers, dippers, and over?dippers. The circadian MOPP fluctuation (CMF) was calculated using the Kruskal–Wallis test, and its relationship with type and severity of visual field was assessed. Results: In total, 149 patients were evaluated; 109 were classified as NTG, and 40 were classified as POAG. A nocturnal dip in BP was noted in 20% of NTG and 17.5% of POAG. The MAP was found to be lower in patients with NTG than POAG. In the NTG subgroup, we found that 20% of patients were over?dippers, 32% were dippers, and 48% were non?dippers. The CMF showed a greater fluctuation for over?dippers (P = 0.004 for the RE and 0.003 for the LE) than dippers and non?dippers. A weak positive correlation of CMF with the severity of fields was found. Conclusion: A 24?h monitoring of IOP, BP, MOPP, and assessment of systemic risk factors for primary glaucoma acts as an invaluable tool for the comprehensive management of NTG despite the limitations posed by DVT and BP recording
ABSTRACT
Objective: To study the correlation and effect of sequential measurement of intraocular pressure (IOP) with Goldmann applanation tonometer (GAT), ocular response analyzer (ORA), dynamic contour tonometer (DCT), and Corvis ST. Setting and Design: Observational cross‑sectional series from the comprehensive clinic of a tertiary eye care center seen during December 2012. Methods: One hundred and twenty‑five study eyes of 125 patients with normal IOP and biomechanical properties underwent IOP measurement on GAT, DCT, ORA, and Corvis ST; in four different sequences. Patients with high refractive errors, recent surgeries, glaucoma, and corneal disorders were excluded so as to rule out patients with evident altered corneal biomechanics. Statistical Analysis: Linear regression and Bland–Altman using MedCalc software. Results: Multivariate analysis of variance with repeated measures showed no influence of sequence of device use on IOP (P = 0.85). Linear regression r2 between GAT and Corvis ST, Corvis ST and Goldmann‑correlated IOP (IOPg), and DCT and Corvis ST were 0.37 (P = 0.675), 0.63 (P = 0.607), and 0.19 (P = 0.708), respectively. The Bland–Altman agreement of Corvis ST with GAT, corneal compensated IOP, and IOPg was 2 mmHg (−5.0 to + 10.3), −0.5 mmHg (−8.1 to 7.1), and 0.5 mmHg (−6.2 to 7.1), respectively. Intraclass correlation coefficient for repeatability ranged from 0.81 to 0.96. Conclusions: Correlation between Corvis ST and ORA was found to be good and not so with GAT. However, agreement between the devices was statistically insignificant, and no influence of sequence was observed.