Emerging ideas: Shared decision making in patients with osteoarthritis of the hip and knee

Abstract

Background: Despite the widely reported success of total joint arthroplasty (TJA) in reducing pain and improving quality of life and function for patients with hip or knee osteoarthritis, rates of TJA use vary widely throughout the United States, with broad disparities based on geographic, racial, and socioeconomic factors. Shared decision-making approaches, which require an exchange of information between patients and their physicians, can be helpful in improving patient satisfaction with their treatment decision and appropriate use of TJA.

Questions/hypotheses: Expected-value decision analysis models incorporating evidence-based outcome data with individual patient preferences regarding health states and willingness to pay, when used in shared decision-making models, will improve satisfaction among patients with hip or knee osteoarthritis and lead to more appropriate use of TJA.

Proposed program: Patients with hip or knee osteoarthritis will be randomized to usual care or participation in a shared decision-making intervention. Patients in the shared decision-making intervention arm will have their preferences for individual health states related to osteoarthritis and TJA measured using the time trade-off technique, and these values will be incorporated in an expected-value decision analysis model, which also will incorporate the patient's willingness to pay for a particular treatment intervention and evidence-based outcome probabilities. The patient's decision for operative versus nonoperative care and their level of satisfaction with their decision will be compared using chi square and Mann-Whitney rank-sum tests.

Significance: Information regarding patient preferences for particular health states and willingness to pay can be combined with evidence-based outcome data in expected-value decision analysis models, which will help inform shared clinical decision making between surgeons and their patients with hip or knee osteoarthritis.

Fig. 1

A simple decision tree for THA versus nonoperative care is shown. The square indicates a decision node representing a choice between two or more treatments being considered (eg, THA and nonoperative care). The circles are chance nodes, reflecting possible mutually exclusive outcomes with the probability shown below each branch. The triangles are terminal nodes for the final outcomes and to the right of each terminal node is the “reward,” which can be utility, QALYs, cost, or any other quantitative measure of interest associated with each outcome. The expected value of each choice is calculated by multiplying the probability of each branch by its reward in a weighted average in a process known as “folding back.” In the case above, the expected value of THA is 9.6, which is favored over the expected value of nonoperative care of 5.4.

Fig. 2

A graph illustrates the TTO technique for direct preference assessment. This technique derives values for various health states by asking patients how many years in their current state of health they would be willing to give up to live a fixed number of years in excellent health (x) or the time living the rest of their lives in their current state of health (t). i is the direct preference score for the health state. Time, x, varies in values until the respondent is indifferent between the two choices. The higher the value a person places on the state i, the greater the time x would be required for the respondent to be indifferent between the choices being presented [8].

Fig. 3

A flowchart illustrates the standard-gamble technique for direct preference assessment. This technique involves offering the patient two treatment alternatives. In this example, Alternative 1 is a treatment with two possible outcomes: either the patient returns to normal health and lives for an additional finite number of years (probability p) or the patient dies immediately (probability 1 − p). Alternative 2 has the certain outcome of a chronic state of illness or disability (i) for a finite period of time. The probability (p) is varied until the subject is indifferent between the two alternatives, at which point the value for chronic state i is set equal to p [15].