Cost-effectiveness of Unicompartmental and Total Knee Arthroplasty in Elderly Low-demand Patients. A Markov Decision Analysis

Background: Interest in unicompartmental knee arthroplasty has recently increased in the United States, making a firm understanding of the indications for this procedure important. The purpose of this study was to examine the cost-effectiveness of unicompartmental knee arthroplasty compared with total knee arthroplasty in elderly low-demand patients.

Methods: A Markov decision model was used to evaluate the cost-effectiveness of unicompartmental knee arthroplasty as compared with total knee arthroplasty in the elderly population. Transition probabilities were estimated from the Norwegian Arthroplasty Register and the arthroplasty literature, and costs were based on the average Medicare reimbursement for unicompartmental, tricompartmental, and revision knee arthroplasties. Outcomes were measured in quality-adjusted life-years.

Results: Our model showed unicompartmental knee arthroplasty to be a cost-effective strategy for this population as long as the annual probability of revision is <4%. The cost of unicompartmental knee arthroplasty must be greater than $13,500 or the cost of total knee arthroplasty must be less than $8500 before total knee arthroplasty becomes more cost-effective.

Conclusions: Our model suggests that, on the basis of currently available cost and outcomes data, unicompartmental knee arthroplasty and total knee arthroplasty have similar cost-effectiveness profiles in the elderly low-demand patient population. However, several important parameters that could alter the cost-effectiveness analysis were identified; these included implant survival rates, costs, perioperative mortality and infection rates, and utility values achieved with each procedure. The thresholds identified in this study may help decision-makers to evaluate the cost-effectiveness of each strategy as further research characterizes the variables associate with unicompartmental and total knee arthroplasties and may be helpful for designing future appropriate clinical trials.