UBC Theses and Dissertations
Optimal treatment planning under consideration of patient heterogeneity and preparation lead-time Skandari, Mohammad Reza
This thesis comprises three chapters with applications of stochastic optimization models to vascular access planning for patients with chronic kidney disease (CKD). Hemodialysis (HD) is the most common treatment for patients with end-stage renal disease, the last stage of CKD. There are two primary types of vascular accesses used for HD, arteriovenous fistula (AVF), and central venous catheter (CVC). An AVF, which is created via a surgical procedure, is often considered the gold standard for delivering HD due to better patient survival and higher quality of life. However, there exists a preparation lead-time for establishing a functional AVF since it takes several months to know whether the surgery was successful, and a majority of AVF surgeries end in failure. In this thesis, we address the question of whether and when to perform AVF surgery on patients with CKD with the aim of finding individualized policies that optimize patient outcomes. In Chapter 2 we focus on vascular access planning for HD dependent patients. Using a continuous-time dynamic programming model and under data-driven assumptions, we establish structural properties of optimal policies that maximize a patient's probability of survival and quality-adjusted life expectancy. We provide further insights for policy makers through our numerical experiments. In Chapter 3 we develop a Monte-Carlo simulation model to address the timing of AVF preparation for progressive CKD patients who have not yet initiated HD. We consider two types of strategies based on approaches suggested in recently published guidelines. We evaluate these strategies over a range of values for each strategy, compare them with respect to different performance metrics (e.g., percentage of patients with an unnecessary AVF creation), and provide policy recommendations. Our simulation results suggest that the timing of AVF referral should be guided by the individual rate of CKD progression. Motivated by our findings in Chapter 3, we develop a dynamic programming model in Chapter 4 that incorporates patient heterogeneity in disease progression when making clinical decisions. We then apply this modeling framework to the case of the AVF preparation timing problem introduced in Chapter 3 and provide recommendations that consider patient heterogeneity in CKD progression.
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