Optimal Initiation and Management of Dialysis Therapy

Chris P. Lee (), Glenn M. Chertow () and Stefanos A. Zenios ()
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Chris P. Lee: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Glenn M. Chertow: Division of Nephrology, Stanford University School of Medicine, Stanford, California 94305
Stefanos A. Zenios: Graduate School of Business, Stanford University, Stanford, California 94305

Operations Research, 2008, vol. 56, issue 6, 1428-1449

Abstract: Dialysis is the most common therapy for patients afflicted with chronic kidney failure. Currently, little is known about the relationship between the timing of dialysis initiation and the therapy's cost and effectiveness. This paper examines the cost-effective initiation of dialysis and compares standard initiation criteria from the clinical literature to computationally derived strategies. Comparisons make use of a simulation model that integrates submodels of disease progression, hospitalization, transplantation, cost, and quality of life. The simulation model is also used by an approximate dynamic programming (ADP) algorithm to derive approximately optimal strategies that maximize patient welfare. Patient welfare is measured from the society's perspective and is defined as the product of the expected discounted quality-adjusted life years (QALYs) and a “value-of-life” parameter, minus the expected total discounted medical expenditures. Also considered is an alternative formulation in which the goal is to minimize the expected total discounted medical expenditures without affecting patient QALYs relative to current medical practice. Numerical results show that: (i) standard early initiation strategies, where once started on dialysis patients are kept on a fixed weekly program, have a limited potential, and (ii) early dialysis at an incrementally increasing dose customized to each patient can yield a significant cost advantage. These findings demonstrate computationally intensive models of disease progression, and therapy effectiveness can identify novel strategies for managing expensive medical therapies and a more efficient use of scarce health-care resources.

Keywords: dynamic programming; semi-Markov; health care; treatment (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (15)

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