Natalia I Kurysheva1, Olga A Parshunina1, Ekaterina O Shatalova1, Tatiana N Kiseleva2, Michael B Lagutin3, Alexey V Fomin4. 1. a Ophthalmological Center of the Federal Medical and Biological Agency of Russia , Moscow , Russia. 2. b Ultrasound Department of Helmholtz Moscow Research Institute of Eye Diseases of the Ministry of Health of Russia , Moscow , Russia. 3. c Faculty of Mechanics and Mathematics, Department of Mathematical Statistics and Random Processes , Lomonosov Moscow State University , Moscow , Russia. 4. d Tradomed Invest , Moscow , Russia.
Abstract
PURPOSE: To compare the diagnostic value of ocular blood flow parameters and choroidal thickness (CT) with standard structural parameters for early glaucoma detection. METHODS: A total of 32 patients with pre-perimetric glaucoma were compared with 30 age-matched normal subjects. The thickness of the ganglion cell complex (GCC), retinal nerve fiber layer (RNFL), and the choroid and foveal loss volume (FLV) were measured by means of optical coherence tomography (OCT). Retrobulbar blood velocities (Color Doppler Imaging), corneal compensated intraocular pressure (IOPcc), and corneal hysteresis (CH) were also evaluated. Mean ocular perfusion pressure (MOPP) was calculated by measuring IOP and mean arterial blood pressure as MOPP = ([2/3 diastolic BP + 1/3 systolic BP] × 2/3-IOP). The value of each diagnostic indicator (z-value) was calculated using the Wilcoxon-Mann-Whitney test and the area under the receiver operating characteristic curve (AUC). RESULTS: The following indicators had the largest AUC and diagnostic value (z-value): mean blood flow velocity in the vortex veins (AUC 1.0; z-value 5.35) and central retinal vein (0.85; 3.74), diastolic blood flow velocity in the central retinal artery (0.73; 2.74) and lateral short posterior ciliary arteries (0.71; 2.53), IOPcc (0.74; -2.9), CH (0.69; 2.24), CT (0.69; -2.28), GCC (0.67; 2.05), and FLV (0.66; -1.86) to discriminate pre-perimetric glaucoma from healthy subjects. CONCLUSIONS: Interestingly, ocular hemodynamic parameters performed better than structural parameters in detecting early glaucoma. This highlights the potential of techniques to measure ocular blood flow in glaucoma diagnostics independently of the question whether perfusion abnormalities are a cause or a consequence of the disease.
PURPOSE: To compare the diagnostic value of ocular blood flow parameters and choroidal thickness (CT) with standard structural parameters for early glaucoma detection. METHODS: A total of 32 patients with pre-perimetric glaucoma were compared with 30 age-matched normal subjects. The thickness of the ganglion cell complex (GCC), retinal nerve fiber layer (RNFL), and the choroid and foveal loss volume (FLV) were measured by means of optical coherence tomography (OCT). Retrobulbar blood velocities (Color Doppler Imaging), corneal compensated intraocular pressure (IOPcc), and corneal hysteresis (CH) were also evaluated. Mean ocular perfusion pressure (MOPP) was calculated by measuring IOP and mean arterial blood pressure as MOPP = ([2/3 diastolic BP + 1/3 systolic BP] × 2/3-IOP). The value of each diagnostic indicator (z-value) was calculated using the Wilcoxon-Mann-Whitney test and the area under the receiver operating characteristic curve (AUC). RESULTS: The following indicators had the largest AUC and diagnostic value (z-value): mean blood flow velocity in the vortex veins (AUC 1.0; z-value 5.35) and central retinal vein (0.85; 3.74), diastolic blood flow velocity in the central retinal artery (0.73; 2.74) and lateral short posterior ciliary arteries (0.71; 2.53), IOPcc (0.74; -2.9), CH (0.69; 2.24), CT (0.69; -2.28), GCC (0.67; 2.05), and FLV (0.66; -1.86) to discriminate pre-perimetric glaucoma from healthy subjects. CONCLUSIONS: Interestingly, ocular hemodynamic parameters performed better than structural parameters in detecting early glaucoma. This highlights the potential of techniques to measure ocular blood flow in glaucoma diagnostics independently of the question whether perfusion abnormalities are a cause or a consequence of the disease.
Authors: Ou Tan; Liang Liu; Qisheng You; Jie Wang; Aiyin Chen; Eliesa Ing; John C Morrison; Yali Jia; David Huang Journal: Transl Vis Sci Technol Date: 2021-05-03 Impact factor: 3.283