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.

In conventional DXA (Dual-energy X-ray Absorptiometry) analysis, pixel bone mineral density (BMD) is often averaged at the femoral neck. Neck BMD constitutes the basis for osteoporosis diagnosis and fracture risk assessment. This data averaging, however, limits our understanding of localised spatial BMD patterns that could potentially enhance fracture prediction. DXA region free analysis (RFA) is a validated toolkit for pixel-level BMD analysis. We have previously deployed this toolkit to develop a spatio-temporal atlas of BMD ageing in the femur. This study aims first to introduce bone age to reflect the overall bone structural evolution with ageing, and second to quantify fracture-specific patterns in the femur.

The study dataset comprised 4933 femoral DXA scans from White British women aged 75 years or older. The total number of fractures was 684, of which 178 were reported at the hip within a follow-up period of five years. BMD maps were computed using the RFA toolkit. For each BMD map, bone age was defined as the age for which the L2-norm between the map and the median atlas at that age is minimised. Next, bone maps were normalised for the estimated bone age. A t-test followed by false discovery rate (FDR) analysis was applied to compare between fracture and non-fracture groups.

Excluding the ageing effect revealed subtle localised patterns of loss in BMD oriented in the same direction as principal tensile curves. A new score called f-score was defined by averaging the normalised pixel BMD values over the region with FDR q-value less than 1e–6. The area under the curve (AUC) was 0.731 (95% confidence interval (CI)=0.689–0.761) and 0.736 (95% CI=0.694–0.769) for neck BMD and f-score. Combining bone age and f-score improved the AUC significantly by 3% (AUC=0.761, 95% CI=0.756–0.768) over the neck BMD alone (AUC=0.731, 95% CI=0.726–0.737).

This technique shows promise in characterizing spatially-complex BMD changes, for which the conventional region-based technique is insensitive. DXA RFA shows promise to further improve fracture prediction using spatial BMD distribution.

Ageing is associated with a gradual and progressive bone loss, which predisposes to osteoporosis. Given the close relationship between the involutional bone loss and the underlying mechanism of osteoporosis, improving the understanding of the bone ageing process can lead to enhanced preventive and therapeutic strategies for osteoporosis. To facilitate this understanding, we develop a spatio-temporal atlas of ageing bone in the femur.

We applied our method to a cohort of 11,576 Caucasian women (20–97 years). We amalgamated data from three different studies: 5095 women from the UK Biobank study, 1609 women from the OPUS study, and 5112 women from the MRC-Hip study. The scans are collected using either a Hologic QDR 4500A (Waltham, MA), a Lunar GE iDXA (Madison, WI), or a Lunar GE Prodigy (Madison, WI). Pixel BMD maps were exported using APEX v3.2 and Encore v16 for scans collected on Hologic Inc. and Lunar Corp., respectively. The method utilises a thin plate spline (TPS) registration to warp each scan to a reference mean shape. This image warping, termed Region Free Analysis (RFA), aims to eliminate morphological variation and establish a correspondence between pixel coordinates. At each pixel coordinate, the BMD evolution with ageing was modelled using smooth quantile curves. We deployed the R-package ‘VGAM’ to fit the smooth quantile curves.

Cortical thinning was observed consistently with ageing around the shaft from the 60th onwards. A widespread bone loss was also observed in the trochanteric area. Quantile regression curves demonstrated different rates of bone loss at different anatomic locations. For example, bone loss was observed consistently in the mid-femoral neck, while bone mass was preserved the most in the inferior cortex. The developed atlas provides new insights into the spatial bone loss patterns, for which the conventional DXA analysis is insensitive.

Introduction

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).