June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Physiological variations in intraocular pressure: mathematical modeling outputs versus clinical data from patients with glaucoma and healthy controls
Author Affiliations & Notes
  • Maggie Lin
    Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, United States
  • Alon Harris
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Giovanna Guidoboni
    Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, United States
    Mathematics, University of Missouri, Columbia, Missouri, United States
  • Damien Coll
    Mathematiques Appliquees a Paris 5, Paris, Île-de-France, France
  • Roberto Nunez
    Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, United States
  • Alice Verticchio
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Brent A Siesky
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Marcela Szopos
    Mathematiques Appliquees a Paris 5, Paris, Île-de-France, France
  • Footnotes
    Commercial Relationships   Maggie Lin None; Alon Harris AdOM, Qlaris, Luseed, Cipla, Code C (Consultant/Contractor), AdOM, Luseed, Oxymap, Qlaris, Phileas Pharma, SlitLed, QuLent, Code I (Personal Financial Interest), AdOM, Qlaris, Phileas Pharma, Code S (non-remunerative); Giovanna Guidoboni Foresite Healthcare LLC, Code C (Consultant/Contractor), Gspace LLC, Code I (Personal Financial Interest); Damien Coll None; Roberto Nunez None; Alice Verticchio None; Brent Siesky None; Marcela Szopos None
  • Footnotes
    Support   This work was supported in part by a Challenge Grant award from Research to Prevent Blindness, NY, and by the NSF grants DMS 1853222/2021192 and DMS 2108711/2108665. This work is also supported by the Data Intelligence Institute of Paris (diiP), IdEx Université de Paris, ANR-18-IDEX-0001.
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 448. doi:
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      Maggie Lin, Alon Harris, Giovanna Guidoboni, Damien Coll, Roberto Nunez, Alice Verticchio, Brent A Siesky, Marcela Szopos; Physiological variations in intraocular pressure: mathematical modeling outputs versus clinical data from patients with glaucoma and healthy controls. Invest. Ophthalmol. Vis. Sci. 2022;63(7):448.

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

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Abstract

Purpose : Elevated intraocular pressure (IOP) is a major risk factor for open angle glaucoma (OAG). During each heartbeat, IOP exhibits pulsations described as ocular pulse amplitude (OPA). OPA has been shown to be relevant in glaucoma as IOP and OPA are affected by several factors, including systolic and diastolic blood pressure (SBP, DBP), tissue biomechanics, and aqueous humor (AH) flow. Here, we use a mathematical model to investigate how IOP and OPA are influenced by variations in SBP, DBP, and model parameters characterizing tissue biomechanics and AH flow in comparison with clinical data.

Methods : The model calculates IOP and OPA from the balance between AH inflow and outflow, the pulsatile BP inputs, and the deformability of ocular tissues. Two simulation scenarios are considered (health and disease), with the latter characterized by an outflow facility reduced by 70% with respect to baseline. In both scenarios, physiological variations are included in SBP and DBP as normal distributions [SBP∼ N (124.1, 11.1) and DBP ∼ N (77.5, 7.1)].
Variations in all other model parameters were assumed to follow a uniform distribution [± 15% with respect to published baseline values (Szopos et al 2016, Stefanoni et al 2018)].

Results : The table in Fig.1 compares model predictions for IOP and OPA with clinical data on 90 healthy subjects (Cheng et al, 2017) and 115 glaucoma patients. In both health and disease, model results are within the same range of clinical data. Fig. 2 shows the scatterplots of OPA versus pulse pressure (PP=SBP-DBP) as computed by the model (a) and measured clinically (b) on glaucoma patients. When fitted with a linear regression, both computed and measured data had a positive slope of 0.0265 and 0.0122, respectively.

Conclusions : The model proved capable of simulating IOP and OPA in health and disease. OPA was predicted to increase with PP as evidenced by clinical data on glaucoma patients. These data suggest mathematical modeling approaches may help clinicians confirm and/or discriminate OAG disease.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Comparison between IOP and OPA computed by the model and measured clinically.

Comparison between IOP and OPA computed by the model and measured clinically.

 

Scatterplots of OPA versus PP as computed by the model (a) and measured clinically (b).

Scatterplots of OPA versus PP as computed by the model (a) and measured clinically (b).

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