Arthroplasty registries are important for the
surveillance of joint replacements and the evaluation of outcome. Independent
validation of registry data ensures high quality. The ability for
orthopaedic implant retrieval centres to validate registry data
is not known. We analysed data from the National Joint Registry
for England, Wales and Northern Ireland (NJR) for primary metal-on-metal
hip arthroplasties performed between 2003 and 2013. Records were
linked to the London Implant Retrieval Centre (RC) for validation.
A total of 67 045 procedures on the NJR and 782 revised pairs of
components from the RC were included. We were able to link 476 procedures
(60.9%) recorded with the RC to the NJR successfully. However, 306
procedures (39.1%) could not be linked. The outcome recorded by the
NJR (as either revised, unrevised or death) for a primary procedure
was incorrect in 79 linked cases (16.6%). The rate of registry-retrieval
linkage and correct assignment of outcome code improved over time.
The rates of error for component reference numbers on the NJR were
as follows: femoral head category number 14/229 (5.0%); femoral head
batch number 13/232 (5.3%); acetabular component category number
2/293 (0.7%) and acetabular component batch number 24/347 (6.5%). Registry-retrieval linkage provided a novel means for the validation
of data, particularly for component fields. This study suggests
that NJR reports may underestimate rates of revision for many types
of metal-on-metal hip replacement. This is topical given the increasing
scope for NJR data. We recommend a system for continuous independent
evaluation of the quality and validity of NJR data. Cite this article:
The Oxford hip score (OHS) is a 12-item questionnaire designed
and developed to assess function and pain from the perspective of
patients who are undergoing total hip replacement (THR). The OHS
has been shown to be consistent, reliable, valid and sensitive to
clinical change following THR. It has been translated into different
languages, but no adequately translated, adapted and validated Danish
language version exists. The OHS was translated and cross-culturally adapted into Danish
from the original English version, using methods based on best-practice
guidelines. The translation was tested for psychometric quality
in patients drawn from a cohort from the Danish Hip Arthroplasty
Register (DHR).Objectives
Methods
This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.Aims
Methods
The National Hip Fracture Database (NHFD) publishes hospital-level risk-adjusted mortality rates following hip fracture surgery in England, Wales and Northern Ireland. The performance of the risk model used by the NHFD was compared with the widely-used Nottingham Hip Fracture Score. Data from 94 hospitals on patients aged 60 to 110 who had hip fracture surgery between May 2013 and July 2013 were analysed. Data were linked to the Office for National Statistics (ONS) death register to calculate the 30-day mortality rate. Risk of death was predicted for each patient using the NHFD and Nottingham models in a development dataset using logistic regression to define the models’ coefficients. This was followed by testing the performance of these refined models in a second validation dataset.Objectives
Methods
