December 2010
Volume 51, Issue 12
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Physiology and Pharmacology  |   December 2010
Retrobulbar Blood Flow Velocities in Open Angle Glaucoma and Their Association with Mean Arterial Blood Pressure
Author Affiliations & Notes
  • Gerhard Garhöfer
    From the Departments of Clinical Pharmacology and
  • Gabriele Fuchsjäger-Mayrl
    From the Departments of Clinical Pharmacology and
    Ophthalmology and
  • Clemens Vass
    From the Departments of Clinical Pharmacology and
    Ophthalmology and
  • Berthold Pemp
    From the Departments of Clinical Pharmacology and
    Ophthalmology and
  • Anton Hommer
    From the Departments of Clinical Pharmacology and
    the Department of Ophthalmology, Sanatorium Hera, Vienna, Austria.
  • Leopold Schmetterer
    From the Departments of Clinical Pharmacology and
    the Center for Biomedical Engineering and Physics, Medical University of Vienna, Vienna, Austria; and
  • Corresponding author: Leopold Schmetterer, Department of Clinical Pharmacology, Medical University of Vienna, Währinger Gürtel 18–20, A-1090, Vienna, Austria; leopold.schmetterer@meduniwien.ac.at
Investigative Ophthalmology & Visual Science December 2010, Vol.51, 6652-6657. doi:10.1167/iovs.10-5490
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      Gerhard Garhöfer, Gabriele Fuchsjäger-Mayrl, Clemens Vass, Berthold Pemp, Anton Hommer, Leopold Schmetterer; Retrobulbar Blood Flow Velocities in Open Angle Glaucoma and Their Association with Mean Arterial Blood Pressure. Invest. Ophthalmol. Vis. Sci. 2010;51(12):6652-6657. doi: 10.1167/iovs.10-5490.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: A number of previous studies have shown that blood velocities in retrobulbar arteries, as assessed with color Doppler imaging (CDI), are reduced in primary open angle glaucoma (POAG) patients, indicative of reduced blood flow to the eye. In the present study, the authors hypothesized that patients with POAG show an abnormal association between retrobulbar blood flow velocities as assessed with CDI and blood pressure, indicative of vascular dysregulation.

Methods.: A total of 252 POAG patients and 198 healthy age-matched control subjects were included. Mean flow velocity (MFV) in the ophthalmic artery (OA), posterior ciliary artery (PCA), and central retinal artery (CRA) were measured with CDI. Mean arterial blood pressure was measured noninvasively using automated oscillometry, and intraocular pressure was measured using Goldmann applanation tonometry.

Results.: Intraocular pressure was increased in POAG patients compared with healthy controls (P < 0.01). Mean arterial blood pressure was not different between groups. All blood flow velocities were significantly reduced in POAG patients compared with healthy control subjects (P < 0.01 each). The correlation between MFV and mean arterial blood pressure in the CRA was stronger in POAG subjects than in healthy control subjects.

Conclusions.: These data indicate that blood flow velocities in retrobulbar vessels are reduced in POAG patients compared with healthy control subjects. In addition, an abnormal correlation between blood velocities and mean arterial blood pressure was found in POAG. This suggests vascular dysregulation and supports the concept that reduced ocular blood flow in glaucoma is not solely a consequence of the disease.

Glaucoma is a major cause for legal blindness worldwide. Although increased intraocular pressure (IOP) has been identified as the most important risk factor for the progression of the disease, in a large proportion of patients it progresses despite therapeutic IOP reduction. 1 There is evidence that factors independent of IOP may contribute to the pathogenesis of the disease. 
Most prominently, vascular dysregulation has been implicated in the pathogenesis of glaucoma. 2,3 In particular, vascular dysregulation, or the inability of the perfusion system to adapt to the blood flow need of the tissue or to changes in perfusion pressure, may lead to chronically low or unstable ocular perfusion. This, in turn, may cause ischemia, oxidative stress, or both, possibly leading to glaucomatous damage of the optic nerve head. Recently, this pathogenetic concept has gained much support by data showing that low ocular perfusion pressure is a risk factor for the prevalence, incidence, and progression of glaucoma. 1  
As stated, one key feature of vascular dysregulation is that the affected tissue loses its ability for autoregulation. Autoregulation, commonly defined as the ability of a vascular bed to adapt to changes in perfusion pressure, guarantees stable blood flow despite changes in blood pressure or IOP. Although several authors have suggested that patients with glaucoma show signs of faulty autoregulation, only a few studies have investigated autoregulation in glaucoma patients. 
One reason for the sparse evidence is that autoregulation of the ocular vasculature is difficult to assess, and clear criteria to classify the status of autoregulation as normal or impaired are still missing. One of the most widely used methodological approaches to assess autoregulation in other vascular beds, including the brain, is to use the correlation between pressure and flow as a measure for the dependency of blood flow on perfusion pressure. 4 Along this line of thought no correlation between blood flow and perfusion pressure would indicate an ideal autoregulation, keeping blood flow constant despite different perfusion pressures. 4 In contrast, a significant association between the two variables can be interpreted as faulty autoregulation. 
Using the latter approach, we have recently shown that patients with primary open angle glaucoma and ocular hypertension show an abnormal association between optic nerve head blood flow, as assessed with scanning laser Doppler flowmetry, and blood pressure and between ocular fundus pulsation amplitude and blood pressure regulation. 5 The present study was performed to test the hypothesis that patients with glaucoma show an altered autoregulation of retrobulbar blood flow compared with healthy subjects. For this purpose, we compared the association between retrobulbar flow velocities as measured with color Doppler imaging (CDI) and ocular perfusion pressure between patients with primary open angle glaucoma (POAG) and healthy control subjects. 
Subjects and Methods
Subjects
The study protocol was approved by the Ethics Committee of the Medical University of Vienna and followed the guidelines of Good Clinical Practice and the Declaration of Helsinki. Two hundred fifty-three patients with POAG and 198 healthy age- and sex-matched subjects were included. All subjects signed written informed consent and passed an ophthalmologic examination. In patients with open angle glaucoma, a visual field test was performed. 
Inclusion and Exclusion Criteria
For patients with POAG, inclusion criteria were open angle glaucoma defined as pathologic optic disc appearance and characteristic visual field loss, ametropia <3 diopters, and anisometropia <1 diopter. An abnormal visual field was defined by glaucoma hemifield test results outside normal limits, corrected pattern SD with P < 0.05, or both. 6 Any of the following excluded a glaucoma patient from participation in the trial: exfoliation glaucoma, pigmentary glaucoma, history of acute angle closure, mean deviation (MD) of visual field testing (Humphrey 30–2 program) of −10 dB or worse, intraocular surgery within the past 6 months, ocular inflammation, or infection within the past 3 months. Further exclusion criteria were ocular disease that might interfere with the purposes of the study other than glaucoma or blood donation within the past 3 weeks. 
As a control group, 198 healthy subjects who were comparable in sex and age distribution were included. Subjects were excluded if the ophthalmic examination revealed the presence of any clinically relevant ocular disease. 
Experimental Paradigm
After a short resting period to obtain stable hemodynamic conditions, systemic blood pressure and IOP were measured. This was followed by the assessment of retrobulbar blood flow velocities using CDI. 
Color Doppler Imaging
Retrobulbar blood flow was measured using a high-resolution ultrasound system (GE Vingmed Vivid 7 scanner; GE Medical Systems, Waukesha, WI). This noninvasive method is based on the back-scattering of ultrasound by the formed elements in the blood vessels. Measurement of the frequency shifts caused by the Doppler effect yields information about blood velocity. Peak systolic flow velocity (PSV) and end-diastolic flow velocity (EDV) of the arteries were assessed using a 7.5-MHz probe with a pulsed Doppler device in the ophthalmic artery (OA), the central retinal artery (CRA), and the posterior ciliary arteries (PCA). 7 In addition, the mean flow velocity (MFV) was calculated as the integral of the Doppler curve/duration of the cardiac cycle and resistance index as (PSV − EDV)/PSV. 
Blood Pressure Measurement
Systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were measured on the upper arm by an automated oscillometric device (HP-CMS patient monitor; Hewlett Packard, Palo Alto, CA). Pulse rate was automatically recorded from a finger pulse-oxymetric device. 
Intraocular Pressure
A slit-lamp mounted Goldmann applanation tonometer was used to measure IOP. Before each measurement, 1 drop of 0.4% benoxinate hydrochloride combined with 0.25% sodium fluorescein was used for local anesthesia of the cornea. 
Calculation of Ocular Perfusion Pressure
Ocular perfusion pressure (OPP) was calculated as ⅔ MAP − IOP. 
Statistical Analysis
Data are presented as mean ± SD. Baseline differences between groups were assessed using one-way ANOVA. Correlation analysis between perfusion pressure and blood velocity was calculated using Pearson correlation analysis. Significance tests between correlation coefficients have been performed using Fisher's r to Z transformation. P < 0.05 was considered the level of significance. For all statistical analyses, absolute data were used. Calculations were performed using data mining and statistical analysis software (Statistica; StatSoft, Tulsa, OK). 
Results
Patient Characteristics and IOP
Baseline characteristics of patients and healthy subjects are given in Table 1. No significant differences between the glaucoma group and the healthy group were observed in terms of age and sex distribution. As expected, IOP was slightly higher in patients with POAG than in healthy subjects (P < 0.001; Table 1). All glaucoma patients were receiving IOP-lowering therapy. Details of glaucoma medications are given in Table 2
Table 1.
 
Baseline Characteristics of Both Groups
Table 1.
 
Baseline Characteristics of Both Groups
Healthy Control Subjects (n = 198) Glaucoma Subjects (n = 253) P
Sex, M/F 132/120 106/92 0.99
Age, y 68.4 ± 8.5 68.1 ± 8.6 0.87
IOP, mmHg 15.3 ± 2.1 16.2 ± 2.1 <0.001
PSVCRA, cm/s 10.9 ± 1.3 9.9 ± 1.5 <0.001
EDVCRA, cm/s 2.5 ± 0.3 2.2 ± 0.2 <0.001
MFVCRA, cm/s 5.3 ± 0.6 4.6 ± 0.6 <0.001
RICRA 0.77 ± 0.02 0.80 ± 0.03 <0.001
PSVPCA, cm/s 13.4 ± 1.0 12.4 ± 1.6 <0.001
EDVPCA, cm/s 3.6 ± 0.5 3.0 ± 0.5 <0.001
MFVPCA, cm/s 6.9 ± 0.6 6.1 ± 0.8 0.003
RIPCA 0.73 ± 0.03 0.75 ± 0.04 <0.001
PSVOA, cm/s 54.6 ± 6.5 52.5 ± 7.8 <0.001
EDVOA, cm/s 8.8 ± 0.7 7.8 ± 1.1 <0.001
MFVOA, cm/s 24.1 ± 2.3 22.7 ± 3.1 <0.001
RIOA 0.84 ± 0.02 0.85 ± 0.02 <0.001
SBP, mmHg 147 ± 18 145 ± 15 0.15
DBP, mmHg 82 ± 13 80 ± 10 0.09
MAP, mmHg 125 ± 15 123 ± 12 0.11
OPP, mmHg 68 ± 11 66 ± 8 0.009
PR, 1/min 76 ± 10 74 ± 10 0.10
C/D ratio 0.61 ± 0.13
MD, dB −3.8 ± 2.7
Table 2.
 
Topical Treatment of Glaucoma Patients
Table 2.
 
Topical Treatment of Glaucoma Patients
Topical Medication Subjects Treated (n)
β-Receptor antagonists 132
Carboanhydrase inhibitors 37
Prostaglandins 99
α-Receptor agonists 19
Hemodynamics
No difference in SBP and MAP was observed between the two groups (Table 1). DBP tended to be lower in the glaucoma group, but this effect failed to reach the level of significance (P = 0.09). As shown in Table 1, OPP was slightly reduced in patients with glaucoma compared with healthy subjects (P = 0.009). 
Similarly, PSV, EDV, and MFV were lower in POAG patients than in healthy control subjects in all studied vessels (P < 0.001 for all variables; Table 1). In contrast, resistance indexes (RIs) of all retrobulbar vessels were higher in glaucoma patients than in healthy subjects (P < 0.001 for all variables; Table 1). 
An association between blood velocities was found between OPP and blood velocities in both groups for all measured retrobulbar vessels (Table 3; Fig. 1). As shown in Table 3, this association was, however, higher in POAG patients than in the healthy control group. There was a significantly stronger correlation between MFV of the CRA and OPP in glaucoma subjects compared with healthy subjects (P < 0.001). This difference was also seen in the PSV of the CRA (P = 0.00123) and, as a tendency, in EDV (P = 0.0549) of the CRA. A stronger correlation between velocity and OPP was also observed in the PCA and the OA. However, in these vascular beds, only PSV (PCA; P = 0.0083) and EDV (OA; P = 0.014) reached the level of significance. All group comparisons are reported in detail in Table 3. In contrast, in both study groups, RI in the retrobulbar vessels was not associated with OPP. 
Table 3.
 
Correlation Coefficients among OPP, Blood Velocity, and RI as Measured in Central Retinal, Posterior Ciliary, and Ophthalmic Arteries for Both Healthy Control and Glaucoma Subjects
Table 3.
 
Correlation Coefficients among OPP, Blood Velocity, and RI as Measured in Central Retinal, Posterior Ciliary, and Ophthalmic Arteries for Both Healthy Control and Glaucoma Subjects
Healthy Control Subjects (n = 198) Glaucoma Subjects (n = 253) Comparison among Groups (P)
PSVCRA r = 0.16 r = 0.37 0.0123
P = 0.026 P < 0.001
y = 9.56 + 0.019x y = 5.49 + 0.07x
EDVCRA r = 0.18 r = 0.35 0.0549
P = 0.011 P < 0.001
y = 2.21 + 0.004x y = 1.29 + 0.01x
MFVCRA r = 0.18 r = 0.41 0.001
P = 0.012 P < 0.001
y = 4.66 + 0.009x y = 2.69 + 0.03x
RICRA r = 0.01 r = 0.12
P = 0.89 P = 0.067
y = 0.77 + 0.00002x y = 0.77 + 0.0003x
PSVPCA r = 0.24 r = 0.46 0.0083
P = 0.001 P < 0.001
y = 11.81 + 0.023x y = 6.68 + 0.09x
EDVPCA r = 0.24 r = 0.29 0.5755
P = 0.001 P < 0.001
y = 2.9 + 0.012x y = 1.8 + 0.02x
MFVPCA r = 0.28 r = 0.44 0.0536
P < 0.001 P < 0.001
y = 5.84 + 0.015x y = 3.43 + 0.04x
RIPCA r = −0.11 r = 0.06
P = 0.09 P = 0.383
y = 0.75 − 0.0004x y = 0.74 + 0.00025x
PSVOA r = 0.33 r = 0.44 0.177
P < 0.001 P < 0.001
y = 40.88 + 0.2x y = 25.8 + 0.41x
EDVOA r = 0.21 r = 0.42 0.014
P = 0.003 P < 0.001
y = 7.83 + 0.014x y = 4.3 + 0.05x
MFVOA r = 0.35 r = 0.47 0.13
P < 0.001 P < 0.001
y = 18.85 + 0.076x y = 11.45 + 0.17x
RIOA r = 0.19 r = 0.05
P = 0.007 P = 0.411
y = 0.81 + 0.0003x y = 0.84 + 0.00013x
Figure 1.
 
Correlations among OPP and MFV in the CRA, PCA, and OA of healthy controls and POAG patients.
Figure 1.
 
Correlations among OPP and MFV in the CRA, PCA, and OA of healthy controls and POAG patients.
Discussion
The data of the present study indicate that there is a more pronounced association between retrobulbar flow velocities and OPP in POAG patients compared with healthy age- and sex-matched control subjects. This indicates vascular dysregulation in patients with POAG. Furthermore, our study confirms previous results that retrobulbar hemodynamics as measured using CDI are reduced in patients with POAG. 
Today, there is wide agreement that patients with glaucoma have compromised ocular perfusion. 8 Numerous experiments have shown reduced ocular blood flow in the posterior pole of the eye using a variety of different techniques such as laser Doppler flowmetry, CDI, and others. 5,9 11 Additionally, it has been shown that patients with asymmetric glaucomatous damage have lower retrobulbar blood flow in the more damaged eye, indicating that reduced ocular blood flow may also be associated with the progression of the disease. 12 Furthermore, evidence has been provided that patients with progressive glaucoma have lower hemodynamic parameters than patients with stable visual field test results and that the reduced retrobulbar flow velocities are a risk factor for the progression of the disease. 13 16  
In the present study, patients with glaucoma tended to have lower OPPs than healthy control subjects. Several previous experiments investigating the role of OPP in glaucoma have provided evidence that reduced OPP is a major risk factor for glaucoma. 1,17 In the present study, the lower OPP can be attributed primarily to the slightly increased IOP seen in patients with glaucoma, whereas no significant difference in MAP, SBP, or DBP between the two experimental groups was detected. Considering these data, we cannot entirely exclude that the decrease in retrobulbar hemodynamics is also caused partially by the decreased OPP in glaucoma patients. Although the differences in velocities were small, we deem this as improbable given the small differences in OPP between the two groups. 
In recent years several groups have reported that retinal blood flow—and, to some extent, choroidal blood flow—is well autoregulated. 18 22 Thus, changes in OPP, regardless of whether they are caused by changes in systemic blood pressure or changes in IOP, lead to only small changes in retinal blood flow. This is reflected in the weak association between OPP and retinal blood flow over a wide rage of different OPPs. 18 A stronger correlation between OPP and blood flow would indicate impaired or faulty autoregulation in the measured vascular bed. 4  
We have recently reported an abnormal association between blood pressure and ocular perfusion parameters in patients with POAG, also indicating vascular dysregulation in patients with POAG. 5 In this previous study, 5 pulsatile choroidal blood flow was measured using laser interferometry and scanning laser Doppler flowmetry (Heidelberg Retina Flowmeter [HRF]; Heidelberg Engineering GmbH, Heidelberg, Germany) was used to measure blood flow in the temporal neuroretinal rim and the cup of the optic nerve head. However, these techniques have several limitations. The most important is that laser interferometry measures only the pulsatile component of choroidal blood flow. 23 The HRF, on the other hand, measures blood flow in arbitrary units only, and some doubts regarding the validity of the technique have been raised. 24  
The present study has focused on retrobulbar blood flow using CDI. The main finding of our study is that patients with glaucoma have a more pronounced association between OPP and retrobulbar blood flow than do patients with glaucoma. These data again indicate better autoregulatory response of the ocular vascular system in healthy subjects compared with patients with glaucoma. Our results, therefore, support the concept that vascular dysregulation occurs in glaucoma patients. 
In addition, our data indicate that the main difference in the autoregulatory response between glaucoma subjects and healthy controls occurs in the CRA, whereas the PCA and the OA show smaller differences. Although the reason for this effect is not entirely clear, one could argue that defect autoregulation can be most easily observed in the vascular bed that is best autoregulated. The CRA supplies the retinal circulation, which is known to show strong autoregulatory properties. In contrast, ciliary arteries primarily supply the choroidal circulation, which is less autoregulated. This also holds true for the OA supplying the orbital region with less pronounced autoregulatory capacity. Thus, defect autoregulation is observed primarily in the CRA, the vascular bed, where autoregulation is mostly present. Alternatively, one could hypothesize that the lack of statistical significance in the PCA is caused by worse reproducibility of the CDI measurements in this vascular bed. 
The hypothesis that vascular dysregulation is involved in glaucoma pathogenesis is supported by several recent studies showing that unstable OPP is a risk factor for the development of glaucomatous optic neuropathy. 25,26 In particular, it has been shown that a marked circadian fluctuation of mean OPP is a risk factor for the development of normal tension glaucoma. 25 Moreover, increased 24-hour fluctuation of mean OPP may be a prognostic factor for glaucoma progression. 26 In keeping with the latter results, a larger diurnal fluctuation of ocular blood flow parameters in patients with POAG was found. 27  
Our study has several limitations that have to be mentioned. First, evidence of an abnormal autoregulation in the present study is derived from group correlations only. Thus, additional investigations of the autoregulatory capacity in patients with glaucoma, such as by inducing experimental changes in perfusion pressure, are warranted to further clarify this issue. However, given that glaucoma is a disease of the elderly, such interventional studies are difficult to perform. 
Second, the CDI technique allows only for the measurement of blood flow velocity, not of blood flow per se. Thus, given that no diameter information is available, blood flow velocity as measured with CDI does not necessarily reflect volumetric blood flow. Additionally, a large proportion of the blood supplied by the OA does not go into the eye. Consequently, changes in blood flow measured in the OA do not necessarily reflect ocular blood flow. Considering the RI, it must be stated that there is growing evidence that the RI, at least for ocular vessels, is not a good measure for peripheral vascular resistance. 28  
Furthermore, all patients under study received treatment. Thus, we cannot exclude that either systemic or topical treatment might have, at least partially, influenced our study results. However, given the fact that patients with glaucoma and healthy subjects received comparable systemic medication, we deem this bias rather small. In addition, it is unlikely that topical medication affects the relation between ocular blood flow and OPP because the effects of topical medications on ocular perfusion are generally considered small. 29  
In conclusion, we report that patients with POAG show a stronger correlation between OPP and retrobulbar ocular hemodynamic parameters compared with age- and sex-matched healthy subjects. These data indicate abnormal autoregulation in patients with glaucoma and support the hypothesis that impaired blood flow regulation may contribute to the development of the disease. 
Footnotes
 Disclosure: G. Garhöfer, None; G. Fuchsjäger-Mayrl, None; C. Vass, None; B. Pemp, None; A. Hommer, None; L. Schmetterer, None
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Figure 1.
 
Correlations among OPP and MFV in the CRA, PCA, and OA of healthy controls and POAG patients.
Figure 1.
 
Correlations among OPP and MFV in the CRA, PCA, and OA of healthy controls and POAG patients.
Table 1.
 
Baseline Characteristics of Both Groups
Table 1.
 
Baseline Characteristics of Both Groups
Healthy Control Subjects (n = 198) Glaucoma Subjects (n = 253) P
Sex, M/F 132/120 106/92 0.99
Age, y 68.4 ± 8.5 68.1 ± 8.6 0.87
IOP, mmHg 15.3 ± 2.1 16.2 ± 2.1 <0.001
PSVCRA, cm/s 10.9 ± 1.3 9.9 ± 1.5 <0.001
EDVCRA, cm/s 2.5 ± 0.3 2.2 ± 0.2 <0.001
MFVCRA, cm/s 5.3 ± 0.6 4.6 ± 0.6 <0.001
RICRA 0.77 ± 0.02 0.80 ± 0.03 <0.001
PSVPCA, cm/s 13.4 ± 1.0 12.4 ± 1.6 <0.001
EDVPCA, cm/s 3.6 ± 0.5 3.0 ± 0.5 <0.001
MFVPCA, cm/s 6.9 ± 0.6 6.1 ± 0.8 0.003
RIPCA 0.73 ± 0.03 0.75 ± 0.04 <0.001
PSVOA, cm/s 54.6 ± 6.5 52.5 ± 7.8 <0.001
EDVOA, cm/s 8.8 ± 0.7 7.8 ± 1.1 <0.001
MFVOA, cm/s 24.1 ± 2.3 22.7 ± 3.1 <0.001
RIOA 0.84 ± 0.02 0.85 ± 0.02 <0.001
SBP, mmHg 147 ± 18 145 ± 15 0.15
DBP, mmHg 82 ± 13 80 ± 10 0.09
MAP, mmHg 125 ± 15 123 ± 12 0.11
OPP, mmHg 68 ± 11 66 ± 8 0.009
PR, 1/min 76 ± 10 74 ± 10 0.10
C/D ratio 0.61 ± 0.13
MD, dB −3.8 ± 2.7
Table 2.
 
Topical Treatment of Glaucoma Patients
Table 2.
 
Topical Treatment of Glaucoma Patients
Topical Medication Subjects Treated (n)
β-Receptor antagonists 132
Carboanhydrase inhibitors 37
Prostaglandins 99
α-Receptor agonists 19
Table 3.
 
Correlation Coefficients among OPP, Blood Velocity, and RI as Measured in Central Retinal, Posterior Ciliary, and Ophthalmic Arteries for Both Healthy Control and Glaucoma Subjects
Table 3.
 
Correlation Coefficients among OPP, Blood Velocity, and RI as Measured in Central Retinal, Posterior Ciliary, and Ophthalmic Arteries for Both Healthy Control and Glaucoma Subjects
Healthy Control Subjects (n = 198) Glaucoma Subjects (n = 253) Comparison among Groups (P)
PSVCRA r = 0.16 r = 0.37 0.0123
P = 0.026 P < 0.001
y = 9.56 + 0.019x y = 5.49 + 0.07x
EDVCRA r = 0.18 r = 0.35 0.0549
P = 0.011 P < 0.001
y = 2.21 + 0.004x y = 1.29 + 0.01x
MFVCRA r = 0.18 r = 0.41 0.001
P = 0.012 P < 0.001
y = 4.66 + 0.009x y = 2.69 + 0.03x
RICRA r = 0.01 r = 0.12
P = 0.89 P = 0.067
y = 0.77 + 0.00002x y = 0.77 + 0.0003x
PSVPCA r = 0.24 r = 0.46 0.0083
P = 0.001 P < 0.001
y = 11.81 + 0.023x y = 6.68 + 0.09x
EDVPCA r = 0.24 r = 0.29 0.5755
P = 0.001 P < 0.001
y = 2.9 + 0.012x y = 1.8 + 0.02x
MFVPCA r = 0.28 r = 0.44 0.0536
P < 0.001 P < 0.001
y = 5.84 + 0.015x y = 3.43 + 0.04x
RIPCA r = −0.11 r = 0.06
P = 0.09 P = 0.383
y = 0.75 − 0.0004x y = 0.74 + 0.00025x
PSVOA r = 0.33 r = 0.44 0.177
P < 0.001 P < 0.001
y = 40.88 + 0.2x y = 25.8 + 0.41x
EDVOA r = 0.21 r = 0.42 0.014
P = 0.003 P < 0.001
y = 7.83 + 0.014x y = 4.3 + 0.05x
MFVOA r = 0.35 r = 0.47 0.13
P < 0.001 P < 0.001
y = 18.85 + 0.076x y = 11.45 + 0.17x
RIOA r = 0.19 r = 0.05
P = 0.007 P = 0.411
y = 0.81 + 0.0003x y = 0.84 + 0.00013x
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