To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. 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.Aims
Methods
The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man. In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index (C index) and calibration. The dataset contains 327 238 hip replacements for osteoarthritis carried out in England and Wales between 2003 and 2015. The AIC value for the FP model was the lowest. The averages of survival probability estimates were in good agreement with the observed values for the FP model and the RSF algorithm. The integrated Brier score of the FP model and the RSF approach over 10 years were similar: 0.011 (95% confidence interval: 0.011–0.011). The C index of the FP model at 10 years was 59.4% (95% confidence interval: 59.4%–59.4%). This was 56.2% (56.1%–56.3%) for the RSF method. The FP model outperformed other commonly used survival models across chosen validation criteria. However, it does not provide high discriminatory power at the individual level. Models with more comprehensive risk adjustment may provide additional insights for individual risk.
The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man (NJR). In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index and calibration via repeated five-fold cross-validation.Introduction
Methods
Total knee replacement (TKR) is an effective operation for many patients, however approximately 20% of patients experience chronic pain and functional limitations in the months and years following their TKR. If modifiable pre-operative risk factors could be identified, this would allow patients to be targeted with individualised care to optimise these factors prior to surgery and potentially improve outcomes. Psychosocial factors have also been found to be important in predicting outcomes in the first 12 months after TKR, however their impact on long-term outcomes is unknown. This study aimed to identify pre-operative psychosocial predictors of patient-reported and clinician-assessed outcomes at one year and five years after primary TKR. 266 patients listed for a Triathlon TKR because of osteoarthritis were recruited from pre-operative assessment clinics at one orthopaedic centre. Knee pain and function were assessed pre-operatively and at one and five years post-operative using the WOMAC Pain score, WOMAC Function score and American Knee Society Score (AKSS) Knee score. Pre-operative depression, anxiety, catastrophizing, pain self-efficacy and social support were assessed using patient-reported outcome measures. Statistical analyses were conducted using multiple linear regression and mixed effect linear regression, and adjusted for confounding variables.Background
Patients and methods
