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

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.