Optimal treatment of chronic kidney disease with uncertainty in obtaining a transplantable kidney: an MDP based approach
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Optimal treatment of chronic kidney disease with uncertainty in obtaining a transplantable kidney: an MDP based approach Wenjuan Fan1,2 · Yang Zong1,2 · Subodha Kumar3 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Chronic kidney disease (CKD) is one of the most serious and prevalent health issues all over the world. The evolution of CKD can last for many years until the death of patients, and the method of treatment mainly includes medication, dialysis, and transplantation with the evolution of the disease. It has been validated by many empirical studies that for severe CKD patients, the optimal treatment is transplantation if a suitable kidney is available, otherwise the patients should initiate dialysis at a suitable time. It has also been validated that the initiation time of dialysis significantly impacts not only the direct treatment results, but also the success of a future possible kidney transplantation. Motivated by this consideration, we investigate the decision-making problem of the optimal treatment approach to maximize the patient’s total reward including pre-transplant reward and post-transplant reward (if applicable), considering the possibility of having a suitable kidney transplantation in the future. A Markov decision process model is established in which the status of the process is described by the patient health status. We present some structural properties of the decision-making problem, which are used to choose the optimal treatment approach in different health status of patients. We collect the clinical data in the simulation experiments to obtain the fitted curves of the evolution process of different CKD patients, and compare the simulation results with the actual clinical data to demonstrate the advantage of our model. Keywords Chronic kidney disease · Markov decision process · Probability of a future transplantation · Healthcare
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Subodha Kumar [email protected]
1
School of Management, Hefei University of Technology, Hefei, China
2
Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei, China
3
Fox School of Business, Temple University, Philadelphia, USA
123
Annals of Operations Research
1 Introduction Chronic kidney disease (CKD) has been one of the most fatal diseases all over the world (Go et al. 2004; Matsushita et al. 2010; Murphy et al. 2016) and the prevalence is estimated to be 8–16% worldwide (Connaughton et al. 2019). The Global Burden of Disease study estimates that there are now nearly 697 million patients worldwide, an increase of 70% since 1990 (Nelson et al. 2019). Ranked fourteenth in the list of leading causes of death, CKD accounted for 12.2 deaths per 100,000 people (Romagnani et al. 2017; Webster et al. 2017a, b). CKD is characterized by reduced glomerular filtration rate, increased urinary albumin excretion, or both, and the complications include increased all-cause and cardiovascular mortality, kidney-disease progression, acute kidney injury, cognitive decline, anemia, mine
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