Treatment of mitral valve regurgitation in the elderly: a decision cost-effectiveness analysis model
Proctor, Charles N., IV
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INTRODUCTION: The ever-changing landscape of the US health care system is characterized by innovation and high-level care, yet it remains in a state of crisis. With the system seemingly locked in this dire state of rising costs, it becomes increasingly important to take costs into account when deciding between multiple treatments for a particular disease by undertaking cost-effectiveness analysis (CEA) studies. In the present study, a model for mitral regurgitation (MR)—a cardiac valvular disease for which multiple treatment options exist with varying degrees of effectiveness, making it a suitable candidate for CEA —was developed to determine the cost-effectiveness of the four main treatments of medical therapy (MT), mitral valve repair (MVR), mitral valve replacement with a mechanical valve (MVPm) and mitral valve replacement with a bioprosthetic valve (MVPb). The goal of the present undertaking was to determine the most cost-effective treatment option for a reference patient given patient-specific inputs to test the functionality of the developed model. METHODS: Input values for costs, probabilities of event’s occurrences and quality-adjusted life-year (QALY) estimates for each treatment option were first obtained from databases and relevant literature. These values were then standardized to account for source variability and input into a decision tree (DT) model created specifically for the present analysis that included branches for each of the four potential interventions, from each of which were three potential outcome arms representing the potential endpoints of each treatment: death, alive with complications and alive without complications. The costs, probabilities and QALY –values of each of the four complications of interest in the study—atrial fibrillation (AFib), stroke, congestive heart failure (CHF) and reoperation—were combined and averaged to create a unified endpoint for the alive with complications branches of the DT. Following the development of the model, the relevant cost, probability and utility values were used to run a simulation to test the functionality of the model using values associated with a fictional 65-year-old Medicare-covered patient with chronic MR to act as a representative of a sizable real-life population. The model results were then used to calculate the incremental cost-effectiveness ratio (ICER)—the standard comparison used in CEA—between treatment options to determine the most cost-effective among them. Following this simulation, one-way sensitivity analyses (SA) were conducted to determine the susceptibility of the result to variations in select input values. RESULTS: The probability-weighted costs of MT, MVR, MVPm and MVPb were found to be $40,387, $60,249, $76,293 and $74,320, respectively, with respective probability-weighted QALYs of 4.298, 4.740, 4.428 and 5.119. The calculation of ICERs from these values led to the conclusion that MVPb dominated all other treatments and had an ICER of $41,370/QALY gained over MT, which was treated as the baseline treatment option. The societal willingness-to-pay (WTP) threshold used in the present study ($62,000/QALY gained) was greater than the ICER, indicating that MVPb is a cost-effective solution to society. The results of the SA indicated that variations in mortality rate within the ranges in the relevant literature have significant effect on the cost-effectiveness of the interventions, with roughly a 4.74% increase in mortality for MVPb or a 5.09% decrease in the rate of MT leading MVPb to be considered cost-ineffective. CONCLUSIONS: The simulation study concluded that for the 65-year-old reference case, MVPb was the most cost-effective option and the additional cost to society was deemed less than society’s WTP for the additional health benefit. The successful simulation of the model indicates it may hold real-world potential and be applicable to numerous other situations with varying input values. Further research into more accurate input values for a larger number of variables need to be determined in order to increase the accuracy and maximize the applicability of the present model. In addition, the model will require further complication via the inclusion of an increasing number of variables to allow for a more accurate determination of cost-effectiveness in a wider range of health scenarios. Thus, the current model described here and a further evolved future model hold great potential for use all across health care in order to help contain rising costs plaguing the current health care system in the United States.
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