April 2014
Volume 55, Issue 13
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ARVO Annual Meeting Abstract  |   April 2014
User Friendly Tool Using Kalman Filter Algorithms to Display Glaucoma Progression Indicators and Personalized Time to Next Test
Mariel S. Lavieri; Sam Devaprasad; Manqi Li; Greggory Schell; Pamela Martinez Villarreal; Jonathan E Helm; Mark Van Oyen; David C Musch; Joshua D Stein
Author Affiliations & Notes
  • Mariel S. Lavieri
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
  • Sam Devaprasad
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
  • Manqi Li
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
  • Greggory Schell
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
  • Pamela Martinez Villarreal
    The University of Texas at El Paso, El Paso, TX
  • Jonathan E Helm
    Kelley School of Business, Indiana University, Bloomington, IN
  • Mark Van Oyen
    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
  • David C Musch
    Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor, MI
  • Joshua D Stein
    Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor, MI
  • Footnotes
    Commercial Relationships Mariel Lavieri, Patent pending: 13/668,280 (P); Sam Devaprasad, None; Manqi Li, None; Greggory Schell, Patent pending: 13/668,280 (P); Pamela Martinez Villarreal, None; Jonathan Helm, Patent pending: 13/668,280 (P); Mark Van Oyen, Patent Pending: 13/668,280 (P); David Musch, Patent Pending: 13/668,280 (P); Joshua Stein, Patent Pending: 13/668,280 (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 5611. doi:
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      Mariel S. Lavieri, Sam Devaprasad, Manqi Li, Greggory Schell, Pamela Martinez Villarreal, Jonathan E Helm, Mark Van Oyen, David C Musch, Joshua D Stein; User Friendly Tool Using Kalman Filter Algorithms to Display Glaucoma Progression Indicators and Personalized Time to Next Test. Invest. Ophthalmol. Vis. Sci. 2014;55(13):5611.

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Abstract
 
Purpose
 

To develop a user friendly interface that can visualize measurements obtained from tonometric and perimetric testing of patients with open angle glaucoma (including intraocular pressure (IOP), mean deviation (MD), and pattern standard deviation (PSD)), and to display personalized, dynamically-adjusted forecasts of these indicators and suggestions for the timing of future testing.

 
Methods
 

Kalman filtering algorithms are used to model the IOP, MD and PSD dynamics of patients with Open Angle Glaucoma (OAG) and to update the knowledge about those dynamics as additional readings are obtained. We developed a tool in Excel using Visual Basic for Applications (VBA). It runs MATLAB code that generates forecasted values for IOP, MD, and PSD for each individual patient as well as suggested testing frequencies. The tool also displays data from past visits and changes in visual field readings to enhance clinical decision making. This tool has been calibrated and validated using longitudinal data from patients who were enrolled in Collaborative Initial Glaucoma Treatment Study (CIGTS) and the Advanced Glaucoma Intervention Study (AGIS).

 
Results
 

A sample screenshot from the tool is presented in Figure 1. This tool can calculate adjusted predictions and personalized testing frequencies in seconds, providing clinicians with valuable insight to enable personalized glaucoma management.

 
Conclusions
 

It is possible to integrate a Kalman Filter approach into a user-friendly tool that improves visualization of tonometric and perimetric measurements and how they change over time. This tool generates real-time personalized predictions of the status of each patient’s OAG and when future testing should be performed so as to not miss progression.

 
Figure 1: Sample tool output. Visual field plots (left) and prior, current, and forecasted test results (right).
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Figure 1: Sample tool output. Visual field plots (left) and prior, current, and forecasted test results (right).
 
Figure 1: Sample tool output. Visual field plots (left) and prior, current, and forecasted test results (right).
Figure 1: Sample tool output. Visual field plots (left) and prior, current, and forecasted test results (right).
View OriginalDownload Slide
 
Keywords: 758 visual fields  
© 2014, The Association for Research in Vision and Ophthalmology, Inc., all rights reserved. Permission to republish any abstract or part of an abstract in any form must be obtained in writing from the ARVO Office prior to publication.
Copyright © 2024 Association for Research in Vision and Ophthalmology.
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