The in vitro effects of four nutraceuticals, catechin hydrate, gallic acid, α-tocopherol and ascorbic acid, on the ability of human osteoarthritic chondrocytes of two female obese groups to form articular cartilage (AC) tissues and to reduce inflammation were investigated. Group 1 represented thirteen females in the 50–69 years old range, an average weight of 100 kg and an average body mass index (BMI) of 34⋅06 kg/m2. Group 2 was constituted of three females in the 70–80 years old range, an average weight of 75 kg and an average BMI of 31⋅43 kg/m2. The efficacy of nutraceuticals was assessed in monolayer cultures using histological, colorimetric and mRNA gene expression analyses. AC engineered tissues of group 1 produced less total collagen and COL2A1 (38-fold), and higher COL10A1 (2⋅7-fold), MMP13 (50-fold) and NOS2 (15-fold) mRNA levels than those of group 2. In comparison, engineered tissues of group 1 had a significant decrease in NO levels from day 1 to day 21 (2⋅6-fold), as well as higher mRNA levels of FOXO1 (2-fold) and TNFAIP6 (16-fold) compared to group 2. Catechin hydrate decreased NO levels significantly in group 1 (1⋅5-fold) while increasing NO levels significantly in group 2 (3⋅8-fold). No differences from the negative control were observed in the presence of other nutraceuticals for either group. In conclusion, engineered tissues of the younger but heavier patients responded better to nutraceuticals than those from the older but leaner study participants. Finally, cells of group 2 formed better AC tissues with less inflammation and better extracellular matrix than cells of group 1.

Objectives: Earlier treatment of publicly funded patients may achieve health gains that justify the additional costs of reducing waiting times. This study reports on the cost-effectiveness of implementing a private contracting model to meet alternative maximum waiting time targets for publicly funded patients undergoing total knee replacement surgery in Australia.

Methods: A linked decision tree and cohort Markov model was developed and populated and validated using secondary data sources to represent the pathways, costs, and quality adjusted life-years (QALYs) gained of non-urgent patients with alternative waiting times for total knee replacement surgery to a maximum age of 100 years.

Results: Assuming public waiting times are reduced through the purchase of private services, additional QALYs are gained at an incremental cost of less than $40,000. Value could be increased if lower private prices could be negotiated. Results are also sensitive to the rate of deterioration in function while waiting for surgery and the impact of functional status at the time of surgery on postsurgery outcomes.

Conclusions: More evidence on the value of expanded capacity or new models of care may inform new funding models to support such investments and reduced prices for new technologies, leading to more efficient and sustainable publicly funded healthcare systems.

Objectives: The aim of this study was to demonstrate how value of information analysis can measure the upper limit on returns to future research and identify the research priorities for computer-assisted total knee replacement (CAS-TKR).

Methods: Using a previous economic analysis of CAS-TKR compared with conventional TKR, the population expected value of perfect information (EVPI) was calculated using Monte Carlo simulation to provide an estimate of the upper limit on returns to future research. The population expected value of partial perfect information (EVPPI) for both individual parameters and groups of parameters was estimated to inform specific future research priorities.

Results: The UK individual EVPI would be £21.4 if the willingness to pay for one QALY (quality-adjusted life-year) were £30,000. The population EVPPI would be £8.3 million, assuming a 10-year time horizon for CAS-TKR. In this instance, the expected value of information is positively related to willingness to pay for one QALY for the range of £0 to £50,000. Although each individual parameter had an EVPPI of £0, groups of utility parameters had positive EVPPI. Population EVPPI was £5.6 million for utility parameters, £20,000 for transition probabilities relating to CAS-TKR, and £5,000 for transition probabilities related to conventional TKR.

Conclusions: The study provides evidence on which parameters further information may be of most value. Focusing research on the utility values associated with health states relating to TKR would be of greatest value.

Objectives: The objective of this study is to apply a Markov model to compare cost-effectiveness of total knee replacement (TKR) using computer-assisted surgery (CAS) with that of TKR using a conventional manual method in the absence of formal clinical trial evidence.

Methods: A structured search was carried out to identify evidence relating to the clinical outcome, cost, and effectiveness of TKR. Nine Markov states were identified based on the progress of the disease after TKR. Effectiveness was expressed by quality-adjusted life years (QALYs). The simulation was carried out initially for 120 cycles of a month each, starting with 1,000 TKRs. A discount rate of 3.5 percent was used for both cost and effectiveness in the incremental cost-effectiveness analysis. Then, a probabilistic sensitivity analysis was carried out using a Monte Carlo approach with 10,000 iterations.

Results: Computer-assisted TKR was a long-term cost-effective technology, but the QALYs gained were small. After the first 2 years, the incremental cost per QALY of computer-assisted TKR was dominant because of cheaper and more QALYs. The incremental cost-effectiveness ratio (ICER) was sensitive to the “effect of CAS,” to the CAS extra cost, and to the utility of the state “Normal health after primary TKR,” but it was not sensitive to utilities of other Markov states. Both probabilistic and deterministic analyses produced similar cumulative serious or minor complication rates and complex or simple revision rates. They also produced similar ICERs.

Conclusions: Compared with conventional TKR, computer-assisted TKR is a cost-saving technology in the long-term and may offer small additional QALYs. The “effect of CAS” is to reduce revision rates and complications through more accurate and precise alignment, and although the conclusions from the model, even when allowing for a full probabilistic analysis of uncertainty, are clear, the “effect of CAS” on the rate of revisions awaits long-term clinical evidence.