Aims. To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. Methods. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. Results. The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). Conclusion. Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (. https://jointcalc.shef.ac.uk. ). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808–820

Aims. Arthrofibrosis is a relatively common complication after joint injuries and surgery, particularly in the knee. The present study used a previously described and validated rabbit model to assess the biomechanical, histopathological, and molecular effects of the mast cell stabilizer ketotifen on surgically induced knee joint contractures in female rabbits. Methods. A group of 12 skeletally mature rabbits were randomly divided into two groups. One group received subcutaneous (SQ) saline, and a second group received SQ ketotifen injections. Biomechanical data were collected at eight, ten, 16, and 24 weeks. At the time of necropsy, posterior capsule tissue was collected for histopathological and gene expression analyses (messenger RNA (mRNA) and protein). Results. At the 24-week timepoint, there was a statistically significant increase in passive extension among rabbits treated with ketotifen compared to those treated with saline (p = 0.03). However, no difference in capsular stiffness was detected. Histopathological data failed to demonstrate a decrease in the density of fibrous tissue or a decrease in α-smooth muscle actin (α-SMA) staining with ketotifen treatment. In contrast, tryptase and α-SMA protein expression in the ketotifen group were decreased when compared to saline controls (p = 0.007 and p = 0.01, respectively). Furthermore, there was a significant decrease in α-SMA (ACTA2) gene expression in the ketotifen group compared to the control group (p < 0.001). Conclusion. Collectively, these data suggest that ketotifen mitigates the severity of contracture formation in a rabbit model of arthrofibrosis. Cite this article: Bone Joint Res 2020;9(6):302–310

Aims

A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.