Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support

Jonathan E. Helm (), Mariel S. Lavieri (), Mark P. Van Oyen (), Joshua D. Stein () and David C. Musch ()
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Jonathan E. Helm: Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Mariel S. Lavieri: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Mark P. Van Oyen: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Joshua D. Stein: Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105
David C. Musch: Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105

Operations Research, 2015, vol. 63, issue 5, 979-999

Abstract: In managing chronic diseases such as glaucoma, the timing of periodic examinations is crucial, as it may significantly impact patients’ outcomes. We address the question of when to monitor a glaucoma patient by integrating a dynamic, stochastic state space system model of disease evolution with novel optimization approaches to predict the likelihood of progression at any future time. Information about each patient’s disease state is learned sequentially through a series of noisy medical tests. This information is used to determine the best time to next test based on each patient’s individual disease trajectory as well as population information. We develop closed-form solutions and study structural properties of our algorithm. While some have proposed that fixed-interval monitoring can be improved upon, our methodology validates a sophisticated model-based approach to doing so. Based on data from two large-scale, 10+ years clinical trials, we show that our methods significantly outperform fixed-interval schedules and age-based threshold policies by achieving greater accuracy of identifying progression with fewer examinations. Although this work is motivated by our collaboration with glaucoma specialists, the methodology developed is applicable to a variety of chronic diseases.

Keywords: linear Gaussian systems modeling; controlled observations; stochastic control; disease monitoring; medical decision making; glaucoma; visual field (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (17)

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