September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Ocular perfusion pressure as a surrogate for ocular perfusion: mathematical and statistical methods to interpret clinical data
Author Affiliations & Notes
  • Giovanna Guidoboni
    Mathematical Sciences, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States
  • Alon Harris
    Ophthalmology, Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Alessandra Guglielmi
    Mathematics, Politecnico di Milano, Milano, Italy
  • Simone Cassani
    Mathematical Sciences, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, United States
  • Brent A Siesky
    Ophthalmology, Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Footnotes
    Commercial Relationships   Giovanna Guidoboni, None; Alon Harris, AdOM (C), AdOM (I), Biolight (C), Isama therapeutics (C), Nano Retina (C), Ono (C), Oxymap (I), Science Based Health (C), Stemnion Inc. (C); Alessandra Guglielmi, None; Simone Cassani, None; Brent Siesky, None
  • Footnotes
    Support  This work has been partially supported by the NSF DMS-1224195, NIH 1R21EY022101- 01A1, a grant from Research to Prevent Blindness (RPB, NY, USA), an Indiana University Collaborative Research Grant of the Office of the Vice President for Research, the Chair Gutenberg funds of the Cercle Gutenberg (France) and the Labex IRMIA (University of Strasbourg, France).
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 2990. doi:
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      Giovanna Guidoboni, Alon Harris, Alessandra Guglielmi, Simone Cassani, Brent A Siesky; Ocular perfusion pressure as a surrogate for ocular perfusion: mathematical and statistical methods to interpret clinical data. Invest. Ophthalmol. Vis. Sci. 2016;57(12):2990.

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

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Abstract

Purpose : Ocular perfusion pressure (OPP), defined as the difference between 2/3 of mean arterial pressure (MAP) and intraocular pressure (IOP), has been proposed as a surrogate for ocular perfusion, which is not measurable directly. Even though low OPP is a recognized risk factor for glaucoma, controversies remain as to what extent OPP is indicative of ocular circulation and whether MAP and IOP are combined risk factors. Here, mathematical and statistical methods are used to address these controversies.

Methods : Two approaches are used to study in silico (Part 1) and clinical data (Part 2).
Part 1: A mathematical model of the retinal circulation (Fig1a) is used as a virtual lab to isolate the contributions of IOP and MAP on retinal blood flow (RBF). IOP acts as external pressure in the intraocular region and on the lamina cribrosa, MAP is the input blood pressure, and autoregulation (AR) is embodied in the resistances R2a and R2b.
Part 2: A statistical method (logistic regression) with variance inflation factors is used to account for the multicollinearity of OPP, MAP and IOP as covariates in predicting glaucoma risk and it is applied to a dataset of 157 individuals (73 healthy, 84 with glaucoma) from the Indianapolis Glaucoma Progression Study.

Results : Fig1b shows the model predicted RBF as a function of OPP when: 1) IOP varies in [5, 45]mmHg and MAP=93.3mmHg (solid line); 2) MAP varies in [48.3, 108.3]mmHg and IOP=15mmHg(dashed line); 3) with functional AR (black lines); and 4) impaired AR (blue lines). Results indicate that, in the range [23,50]mmHg, OPP is a good synthetic index for the influence of MAP and/or IOP variations on RBF. Fig2a shows that the probabilities (p) of having glaucoma (p=1) or not (p=0) as predicted by the statistical model (red circles) and observed in the dataset (black circles) are in good agreement. Fig2b reports the predicted p for a new female (F) or male (M) patient joining the study as a function of OPP, for MAP=93.3mmHg and IOP=12, 16 or 20mmHg, indicating the importance of MAP and IOP as combined risk factors.

Conclusions : Our analysis suggest that: 1) OPP is a good indicator of retinal circulation over a wide range of MAP and IOP; and 2) good estimates of glaucoma risk can be obtained when accounting for the multicollinearity of OPP, MAP and IOP in the statistical analysis of clinical data.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Fig1: Mathematical model

Fig1: Mathematical model

 

Fig2: Statistical analysis

Fig2: Statistical analysis

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