Purpose
Alterations in intraocular pressure (IOP), cerebrospinal fluid pressure (CSF-p), arterial blood pressure (BP), and blood flow autoregulation (AR) have been suggested as possible risk factors for glaucoma. The goal of this study is to investigate whether and to what extent these factors influence retinal hemodynamics, as a first step towards a deeper understanding of how they might contribute to glaucoma pathophysiology.
Methods
A mathematical model of the retinal circulation is used as a “virtual lab” to isolate the contributions of IOP, CSF-p, BP and AR on retinal hemodynamics. The model describes the retinal vascular bed as a network of five compartments: central retinal artery (CRA), arterioles, capillaries, venules, and central retinal vein (CRV). The blood flow is driven by systemic pressures, and is regulated by variable resistances accounting for nonlinear effects due to AR, and compression of the lamina cribrosa on CRA and CRV (which depends on IOP, CSF-p, and scleral tension). Velocities, pressures and volumetric flows are computed using the model for (1) IOP=15mmHg (baseline) and 24 mmHg (elevated IOP); (2) CSF-p = 7mmHg (baseline) and 5 mmHg (low CSF-p); (3) BP = 120/80mmHg (normotensive-NT) and 145/85mmHg (hypertensive-HT); and (4) functional AR (fAR) and impaired AR (iAR).
Results
Variations in peak systolic velocity (PSV), end diastolic velocity (EDV), and mean retinal blood flow (Q) in the CRA predicted by the model are reported in Fig1 and Fig2. Variations are computed as percent differences with respect to the values obtained for baseline IOP and CSF-p. When CSF-p is low (Fig1), the variations in PSV and Q remain below 3.3% for both NT and HT, and both fAR and iAR cases, while the variations in EDV reach 4.8% for HT_iAR. When IOP is elevated (Fig2), the variations in PSV and Q remain below 11.2% for both NT and HT, and both fAR and iAR cases, while the variations in EDV reach 16.6% for HT_iAR. In both panels, the case NT_fAR shows variations of smallest magnitudes.
Conclusions
Our mathematical model suggests that retinal blood flow is nonlinearly modulated by IOP, CSF-p, BP, and AR. Specifically, alterations in CRA blood flow are affected by low CSF-p and elevated IOP in different ways and are more pronounced in patients with systemic hypertension.
Keywords: 473 computational modeling •
436 blood supply •
688 retina