June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Effect of Trabeculectomy on Retinal Hemodynamics: Mathematical Modeling of Clinical Data
Author Affiliations & Notes
  • Simone Cassani
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
  • Giovanna Guidoboni
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
  • Ingrida Januleviciene
    Eye Clinic Of Lithuanian University of Health Sciences, Kaunas, Lithuania
  • Lucia Carichino
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
  • Brent Siesky
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Leslie Tobe
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Annahita Amireskandari
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Alon Harris
    Ophthalmology, Indiana University Sch of Medicine, Indianapolis, IN
  • Footnotes
    Commercial Relationships Simone Cassani, None; Giovanna Guidoboni, None; Ingrida Januleviciene, Pharmalight (F), Alcon (C), MSD (C), Santen (C); Lucia Carichino, None; Brent Siesky, None; Leslie Tobe, None; Annahita Amireskandari, None; Alon Harris, MSD (R), Alcon (R), Merck (C), Pharmalight (C), ONO (C), Sucampo (C), Adom (I)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 4462. doi:https://doi.org/
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    • Get Citation

      Simone Cassani, Giovanna Guidoboni, Ingrida Januleviciene, Lucia Carichino, Brent Siesky, Leslie Tobe, Annahita Amireskandari, Alon Harris; Effect of Trabeculectomy on Retinal Hemodynamics: Mathematical Modeling of Clinical Data. Invest. Ophthalmol. Vis. Sci. 2013;54(15):4462. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

To investigate the effect of trabeculectomy (TB) on retinal hemodynamics and to study how this relationship may be influenced by arterial blood pressure and blood flow autoregulation (AR).

 
Methods
 

A mathematical model of the retinal circulation is used to predict alterations in retinal blood flow associated with reductions of intraocular pressure (IOP) following TB. The model describes the retinal vascular bed as a network of five lumped 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 IOP-induced compression of the lamina cribrosa on CRA and CRV. Peak systolic velocity (PSV), end diastolic velocity (EDV), and mean blood volumetric flow (Q) in the CRA are computed with the mathematical model for (i) IOP=28.7 mmHg and 13.1 mmHg, corresponding to IOP levels before and after TB; (ii) systolic and diastolic blood pressures SBP/DBP = 120/80 mmHg and 143/84 mmHg, representing normotensive (NT) and hypertensive (HT) subjects; and functional AR (fAR) or impaired AR (iAR). The model predictions are compared with the clinical data collected in a study involving 49 patients, examining the blood flow pre and post TB.

 
Results
 

The clinical data show that PSV and EDV increase from 7.81 ± 2.51 cm/s and 2.39 ± 0.94 cm/s to 8.90 ± 2.66 cm/s and 2.77 ± 1.14 after TB, while IOP decreases from 28.7 ± 7.9 mmHg to 13.1 ± 5.8 mmHg. The mean values of SBP/DBP are 143/84 mmHg, as for the HT case of the mathematical model. The amounts by which PSV and EDV increase are reported in the Figure and are compared with model predictions. The increases in Q predicted by the model are also reported in the Figure. The case HT_fAR is the closest to the clinical data, and shows that PSV increases more than EDV. On the contrary, in NT_fAR, EDV increases more than PSV, suggesting that the levels of arterial blood pressure strongly influence the results. When AR is impaired, the increases in PSV, EDV and Q are much more pronounced than in the case of functional AR.

 
Conclusions
 

Our mathematical model suggests that CRA blood flow is increased secondarily to TB. Specifically our model reveals the relative impact of alterations in blood pressure and dysfunctional regulation of CRA hemodynamics on changes in retinal blood flow following TB.

  
Keywords: 473 computational modeling • 436 blood supply • 688 retina  
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