April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Effect of intraocular pressure and cerebrospinal fluid pressure on retinal hemodynamics
Author Affiliations & Notes
  • Lucia Carichino
    Mathematics, Indiana Univ Purdue Univ Indianapolis, Indianapolis, IN
  • Giovanna Guidoboni
    Mathematics, Indiana Univ Purdue Univ Indianapolis, Indianapolis, IN
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Brent A Siesky
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Annahita Amireskandari
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Ingrida Januleviciene
    Eye Clinic, Lithuanian University of Health Sciences, Kaunas, Lithuania
  • Alon Harris
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Footnotes
    Commercial Relationships Lucia Carichino, None; Giovanna Guidoboni, None; Brent Siesky, None; Annahita Amireskandari, None; Ingrida Januleviciene, Alcon (C), Allergan (R), MSD (R), Santen (R); Alon Harris, Adom (I), Alcon (R), Biolight (C), MSD (R), Nano Retina (C), ONO Pharmaceuticals (C), Pharmalight (C), Sucampo (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 2946. doi:https://doi.org/
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    • Get Citation

      Lucia Carichino, Giovanna Guidoboni, Brent A Siesky, Annahita Amireskandari, Ingrida Januleviciene, Alon Harris; Effect of intraocular pressure and cerebrospinal fluid pressure on retinal hemodynamics. Invest. Ophthalmol. Vis. Sci. 2014;55(13):2946. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

This study proposes a mathematical model to estimate, quantify and compare the influence of changes in intraocular pressure (IOP) and cerebrospinal fluid pressure (CSFp) on retinal hemodynamics (RH). This investigation is motivated by several studies indicating high IOP and low CSFp as possible risk factors for glaucoma.

 
Methods
 

The central retinal artery and vein (CRA and CRV) are modeled as deformable tubes whose walls deform under the action of an external pressure that varies along the vessel length to include CSFp, IOP and the effect of LC deformation. Retinal arterioles, capillaries and venules are modeled as a network of resistances. The flow rate (Q) and the blood velocity in the CRA and CRV (Va and Vv) are simulated and compared assuming that IOP and CSFp vary independently (case (1a) IOP in the range [15,50]mmHg, CSFp constant; case (1b) CSFp in the range [1,60]mmHg, IOP constant) and assuming that IOP and CSFp may or may not be accompanied by changes in mean arterial blood pressure (MAP) (case (2a) MAP in the range [62.22,108.89]mmHg, CSFp=0.324MAP/7+8.6mmHg (Ren at al 2010) and IOP constant; case(2b) MAP in the range [62.22,108.89]mmHg, CSFp=0.324MAP/7+8.6mmHg (Ren at al 2010) and IOP=0.243MAP/7+11.76mmHg (Deokule et al 2008)). The baseline values are IOP=15mmHg, CSFp=7mmHg and MAP=93.33mmHg.

 
Results
 

The model predicts that for a certain translaminar pressure difference (TLpD), the level of IOP affects RH more than the level of CSFp. For example, for TLpD=33mmHg, when IOP=40mmHg and CSFp=7mmHg (case (1a)) Q, Va and Vv are reduced of 38%, 35% and 39% from baseline, respectively, while for IOP=15mmHg and CSFp=48mmHg (case (1b)) Q, Va and Vv are reduced of 4%, 4% and 7% from baseline, Figure 1. The model also predicts that a decrease in CSFp from 13.64 to 11.48 mmHg due to MAP variations induces a decrease in Q, Va and Vv of 56%, 54% and 56%. The differences between the model predictions corresponding to cases (2a) and (2b) are minimal (less than 2%), Figure 2.

 
Conclusions
 

Changes in IOP have a stronger effect on RH than changes in CSFp, even though these changes lead to the same TLpD. This might be due to the fact that, unlike CSFp, IOP acts directly on the intraocular retinal vessels, thereby altering the vascular resistance of the microcirculation. Our model suggests that the influence of CSFp changes in RH might be mediated by associated changes in arterial blood pressure.

   
Keywords: 473 computational modeling • 436 blood supply • 568 intraocular pressure  
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