Conventional approaches (including Tobit) do not accurately account for ceiling effects in PROMs nor give uncertainty estimates. Here, a classifier neural network was used to estimate postoperative PROMs prior to surgery and compared with conventional methods. The Oxford Knee Score (OKS) and the Oxford Hip Score (OHS) were estimated with separate models. English NJR data from 2009 to 2018 was used, with 278.655 knee and 249.634 hip replacements. For both OKS and OHS estimations, the input variables included age, BMI, surgery date, sex, ASA, thromboprophylaxis, anaesthetic and preoperative PROMs responses. Bearing, fixation, head size and approach were also included for OHS and knee type for OKS estimation. A classifier neural network (NN) was compared with linear or Tobit regression, XGB and regression NN. The performance metrics were the root mean square error (RMSE), maximum absolute error (MAE) and area under curve (AUC). 95% confidence intervals were computed using 5-fold cross-validation.Abstract
Objectives
Methods
Current therapies for osteoporosis are limited to generalised antiresorptive or anabolic interventions, which do not target specific regions to improve skeletal health. Moreover, the adaptive changes of separate and combined pharmacological and biomechanical treatments in the ovariectomised (OVX) mouse tibia has not been studied yet. Therefore, this study combines micro- computed tomography (micro-CT) imaging and computational modelling to evaluate the efficacies of treatments in reducing bone loss. In vivo micro-CT (10.4µm/voxel) images of the right tibiae of N=18 female OVX C57BL/6 mice were acquired at weeks 14, 16, 18, 20 and 22 of age for 3 groups: mechanical loading (ML), parathyroid hormone (PTH) or combined therapies (PTHML). All mice received either injection of PTH (100μg/kg/day, 5days/week) or vehicle from week 18. The right tibiae were mechanically loaded in vivo at week 19 and 21 with a 12N peak load, 40 cycles/day and 3 days/week. Bone adaptation was quantified through spatial changes in bone mineral density (BMD) and strain distribution was obtained from micro-CT-based finite element models.Abstract
Objectives
Methodology
Prediction of bone adaptation in response to mechanical loading is useful in the clinical management of osteoporosis. However, few studies have investigated the effect of repeated mechanical loading in the mouse tibia. Therefore, this study uses a combined experimental and computational approach to evaluate the effect of mechanical loading on bone adaptation in a mouse model of osteoporosis. Six female C57BL/6 mice were ovariectomised (OVX) at week 14 and scanned using in vivo micro computed tomography (10.4µm/voxel) at week 14, 16, 18, 20 and 22. The right tibiae were mechanically loaded in vivo at week 19 and 21 with a 12N peak load, 40 cycles/day, 3 days/week. Linear isotropic homogeneous finite element (microFE) models were created from the tissue mineral density calibrated microCT images. Changes in bone adaptation, densitometric and spatial analyses were measured by comparing the longitudinal images after image registration.Abstract
Objectives
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.
