Aims

Knee arthroplasty surgery is a highly effective treatment for arthritis and disorders of the knee. There are a wide variety of implant brands and types of knee arthroplasty available to surgeons. As a result of a number of highly publicized failures, arthroplasty surgery is highly regulated in the UK and many other countries through national registries, introduced to monitor implant performance, surgeons, and hospitals. With time, the options available within many brand portfolios have grown, with alternative tibial or femoral components, tibial insert materials, or shapes and patella resurfacings. In this study we have investigated the effect of the expansion of implant brand portfolios and where there may be a lack of transparency around a brand name. We also aimed to establish the potential numbers of compatible implant construct combinations.

The importance of registries has been brought into focus by recent UK national reports focusing on implant (Cumberlege) and surgeon (Paterson) performance. National arthroplasty registries provide real-time, real-world information about implant, hospital, and surgeon performance and allow case identification in the event of product recall or adverse surgical outcomes. They are a valuable resource for research and service improvement given the volume of data recorded and the longitunidal nature of data collection. This review discusses the current value of registry data as it relates to both clinical practice and research. Cite this article: Bone Joint J 2023;105-B(4):356–360

Data of high quality are critical for the meaningful interpretation of registry information. The National Joint Registry (NJR) was established in 2002 as the result of an unexpectedly high failure rate of a cemented total hip arthroplasty. The NJR began data collection in 2003. In this study we report on the outcomes following the establishment of a formal data quality (DQ) audit process within the NJR, within which each patient episode entry is validated against the hospital unit’s Patient Administration System and vice-versa. This process enables bidirectional validation of every NJR entry and retrospective correction of any errors in the dataset. In 2014/15 baseline average compliance was 92.6% and this increased year-on-year with repeated audit cycles to 96.0% in 2018/19, with 76.4% of units achieving > 95% compliance. Following the closure of the audit cycle, an overall compliance rate of 97.9% was achieved for the 2018/19 period. An automated system was initiated in 2018 to reduce administrative burden and to integrate the DQ process into standard workflows. Our processes and quality improvement results demonstrate that DQ may be implemented successfully at national level, while minimizing the burden on hospitals. Cite this article: Bone Jt Open 2022;3(9):716–725

Aims. The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). Methods. This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC. Results. The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively. Conclusion. When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates. Cite this article: Bone Joint J 2022;104-B(9):1060–1066

‘Big data’ is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Billions of dollars have been spent on attempts to build predictive tools from large sets of poorly controlled healthcare metadata. Companies often sell reports at a physician or facility level based on various flawed data sources, and comparative websites of ‘publicly reported data’ purport to educate the public. Physicians should be aware of concerns and pitfalls seen in such data definitions, data clarity, data relevance, data sources and data cleaning when evaluating analytic reports from metadata in health care. Cite this article: Bone Joint J 2017;99-B:1571–6

Aims. To provide normative data that can assess spinal-related disability and the prevalence of back or leg pain among adults with no spinal conditions in the UK using validated questionnaires. Methods. A total of 1,000 participants with equal sex distribution were included and categorized in five age groups: 20 to 29, 30 to 39, 40 to 49, 50 to 59, and 60 to 69 years. Individuals with spinal pathologies were excluded. Participants completed the Scoliosis Research Society-22 (SRS-22r), visual analogue scale (VAS) for back/leg pain, and the EuroQol five-dimension index (EQ-5D/VAS) questionnaires, and disclosed their age, sex, and occupation. They were also categorized in five professional groups: doctors, nurses, allied health professionals, office workers, and manual workers. Results. The mean age of all participants was 43.8 years (20 to 69). There was no difference in the SRS-22r, EQ-5D, or VAS scores among male and female participants (p > 0.05). There was incremental decrease in SRS-22r total scores as the age increased. The mean EQ-5D index score (0.84) ranged little across the age groups (0.72 to 0.91) but reduced gradually with increasing age. There was difference between the SRS-22r total score (4.51), the individual domain scores, and the EQ-5D score (index: 0.94 and VAS: 89) for the doctors’ group compared to all other occupational categories (p < 0.001). Doctors had a younger mean age of participants, which may explain their improved spinal health. There was no difference in the total or sub-domain SRS-22r and EQ-5D scores between the other four occupational groups. Conclusion. This study provides the first normative data for the SRS-22r, EQ-5D, and VAS for back/leg pain questionnaires among adults in the UK. We recorded an excellent correlation between the three assessment tools with individuals who reported less back and leg pain having better quality of life and greater function. The participants’ age, rather than their sex or profession, appears to be the major determinant for spinal health and quality of life. Cite this article: Bone Jt Open 2022;3(2):130–134

Aims. Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. Methods. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity). Results. The NLP algorithm performed well at extracting variables from unstructured data in our random test dataset (accuracy = 96.3%, sensitivity = 95.2%, and specificity = 97.4%). It performed better at extracting data that were in a structured, templated format such as range of movement (ROM) (accuracy = 98%) and implant brand (accuracy = 98%) than data that were entered with variation depending on the author of the note such as the presence of deep-vein thrombosis (DVT) (accuracy = 90%). Conclusion. The NLP algorithm used in this study was able to identify a subset of variables from randomly selected unstructured notes in arthroplasty with an accuracy above 90%. For some variables, such as objective exam data, the accuracy was very high. Our findings suggest that automated algorithms using NLP can help orthopaedic practices retrospectively collect information for registries and quality improvement (QI) efforts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):99–104

Aims. We present an audit comparing our level I major trauma centre’s data for a cohort of patients with hip fractures in the National Hip Fracture Database (NHFD) with locally held data on these patients. Patients and Methods. A total of 2036 records for episodes between July 2009 and June 2014 were reviewed. . Results. The demographics of nine patients were recorded incorrectly. The rate of incorrect data in operation codes was most significant with overall accuracy of 0.637 (95% CI 0.615 to 0.658). The sensitivity of NHFD coding ranged from 0.250 to 1.000 and the specificity 0.879 to 0.999. The recording of cementation had a sensitivity of 0.932 and specificity of 0.713. The recording of total hip arthroplasty had a sensitivity of 0.739 and specificity of 0.983. The overall accuracy of mortality data was 0.942 (95% CI 0.931 to 0.952), with sensitivity of 0.967 and specificity of 0.419. Conclusion. This paper highlights the need for local audit of the integrity of data uploaded to the NHFD. Cite this article: Bone Joint J 2016;98-B:1406–9

Aims. The aims of this study were to characterize the frequency of missing data in the National Surgical Quality Improvement Program (NSQIP) database and to determine how missing data can influence the results of studies dealing with elderly patients with a fracture of the hip. Patients and Methods. Patients who underwent surgery for a fracture of the hip between 2005 and 2013 were identified from the NSQIP database and the percentage of missing data was noted for demographics, comorbidities and laboratory values. These variables were tested for association with ‘any adverse event’ using multivariate regressions based on common ways of handling missing data. Results. A total of 26 066 patients were identified. The rate of missing data was up to 77.9% for many variables. Multivariate regressions comparing three methods of handling missing data found different risk factors for postoperative adverse events. Only seven of 35 identified risk factors (20%) were common to all three analyses. Conclusion. Missing data is an important issue in national database studies that researchers must consider when evaluating such investigations. Cite this article: Bone Joint J 2018;100-B:226–32

The variation in surgical performance, both between centres and individual surgeons, has recently been of significant political, media and public interest. Within the United Kingdom, a government agenda to increase accountability amongst surgeons has led to the online publication of ‘surgeon-level’ data. Surgeons, journalists and the public need to understand these data if they are to be useful in driving up standards of surgical care. This Editorial describes the use of Funnel Plots, which are the common means by which such data are presented, and discusses how the plots are generated. Cite this article: Bone Joint J 2013;95-B:1156–7

The extent and depth of routine health care data are growing at an ever-increasing rate, forming huge repositories of information. These repositories can answer a vast array of questions. However, an understanding of the purpose of the dataset used and the quality of the data collected are paramount to determine the reliability of the result obtained. . This Editorial describes the importance of adherence to sound methodological principles in the reporting and publication of research using ‘big’ data, with a suggested reporting framework for future Bone & Joint Journal submissions. Cite this article: Bone Joint J 2014;96-B:1575–7

Aims

The aim of this study was to assess medium-term improvements following total hip arthroplasty (THA), and to evaluate what effect different preoperative Oxford Hip Score (OHS) thresholds for treatment may have on patients’ access to THA and outcomes.

Aims

The optimal choice of management for proximal humerus fractures (PHFs) has been increasingly discussed in the literature, and this work aimed to answer the following questions: 1) what are the incidence rates of PHF in the geriatric population in the USA; 2) what is the mortality rate after PHF in the elderly population, specifically for distinct treatment procedures; and 3) what factors influence the mortality rate?

Epidemiological studies enhance clinical practice in a number of ways. However, there are many methodological difficulties that need to be addressed in designing a study aimed at the collection and analysis of data concerning fractures and other injuries. Most can be managed and errors minimised if careful attention is given to the design and implementation of the research. Cite this article: Bone Joint J 2014;96-B:863–7

Objectives. Several genome-wide association studies (GWAS) of bone mineral density (BMD) have successfully identified multiple susceptibility genes, yet isolated susceptibility genes are often difficult to interpret biologically. The aim of this study was to unravel the genetic background of BMD at pathway level, by integrating BMD GWAS data with genome-wide expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (meQTLs) data. Method. We employed the GWAS datasets of BMD from the Genetic Factors for Osteoporosis Consortium (GEFOS), analysing patients’ BMD. The areas studied included 32 735 femoral necks, 28 498 lumbar spines, and 8143 forearms. Genome-wide eQTLs (containing 923 021 eQTLs) and meQTLs (containing 683 152 unique methylation sites with local meQTLs) data sets were collected from recently published studies. Gene scores were first calculated by summary data-based Mendelian randomisation (SMR) software and meQTL-aligned GWAS results. Gene set enrichment analysis (GSEA) was then applied to identify BMD-associated gene sets with a predefined significance level of 0.05. Results. We identified multiple gene sets associated with BMD in one or more regions, including relevant known biological gene sets such as the Reactome Circadian Clock (GSEA p-value = 1.0 × 10. -4. for LS and 2.7 × 10. -2. for femoral necks BMD in eQTLs-based GSEA) and insulin-like growth factor receptor binding (GSEA p-value = 5.0 × 10. -4. for femoral necks and 2.6 × 10. -2. for lumbar spines BMD in meQTLs-based GSEA). Conclusion. Our results provided novel clues for subsequent functional analysis of bone metabolism, and illustrated the benefit of integrating eQTLs and meQTLs data into pathway association analysis for genetic studies of complex human diseases. Cite this article: W. Wang, S. Huang, W. Hou, Y. Liu, Q. Fan, A. He, Y. Wen, J. Hao, X. Guo, F. Zhang. Integrative analysis of GWAS, eQTLs and meQTLs data suggests that multiple gene sets are associated with bone mineral density. Bone Joint Res 2017;6:572–576