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

Aims. The aim of this audit was to assess and improve the completeness and accuracy of the National Joint Registry (NJR) dataset for arthroplasty of the elbow. Methods. It was performed in two phases. In Phase 1, the completeness was assessed by comparing the NJR elbow dataset with the NHS England Hospital Episode Statistics (HES) data between April 2012 and April 2020. In order to assess the accuracy of the data, the components of each arthroplasty recorded in the NJR were compared to the type of arthroplasty which was recorded. In Phase 2, a national collaborative audit was undertaken to evaluate the reasons for unmatched data, add missing arthroplasties, and evaluate the reasons for the recording of inaccurate arthroplasties and correct them. Results. Phase 1 identified 5,539 arthroplasties in HES which did not match an arthroplasty on the NJR, and 448 inaccurate arthroplasties from 254 hospitals. Most mismatched procedures (3,960 procedures; 71%) were radial head arthroplasties (RHAs). In Phase 2, 142 NHS hospitals with 3,640 (66%) mismatched and 314 (69%) inaccurate arthroplasties volunteered to assess their records. A large proportion of the unmatched data (3,000 arthroplasties; 82%) were confirmed as being missing from the NJR. The overall rate of completeness of the NJR elbow dataset improved from 63% to 83% following phase 2, and the completeness of total elbow arthroplasty data improved to 93%. Missing RHAs had the biggest impact on the overall completeness, but through the audit the number of RHAs in the NJR nearly doubled and completeness increased from 35% to 70%. The accuracy of data was 94% and improved to 98% after correcting 212 of the 448 inaccurately recorded arthroplasties. Conclusion. The rate of completeness of the NJR total elbow arthroplasty dataset is currently 93% and the accuracy is 98%. This audit identified challenges of data capture with regard to RHAs. Collaboration with a trauma and orthopaedic trainees through the British Orthopaedic Trainee Association improved the completeness and accuracy of the NJR elbow dataset, which will improve the validity of the reports and of the associated research. Cite this article: Bone Joint J 2024;106-B(12):1461–1468

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. 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. 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. 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

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

This study evaluated the feasibility of using published data from more than one register to define the performance of different hip implants. In order to obtain estimates of performance for specific types of hip system from different register, we analysed data from the annual reports of five national and one Italian regional register. We extracted the number of implants and rates of implant survival at different periods of follow-up. Our aim was to assess whether estimates of cumulative survival rate were comparable with data from registers from different countries, and our conclusion was that such a comparison could only be performed incompletely

We describe a method of audit of a type of total knee replacement, including some details of the organisational difficulties of administering multicentre studies, and draw attention to how this can be done using industrial funding without prejudicing the study. This is a prospective record of 1439 patients who had an Insall-Burstein II (IBII) prosthesis implanted between 1990 and 1994. The data were collected using the American Knee Society scoring system. A method of storing radiographs digitally at low cost is also described. The results emphasise the need for the long-term collection of data on commonly used devices implanted by a cross-section of surgeons. We conclude that for most patients the IBII cemented, posteriorly stabilised, cruciate-substituting prosthesis will relieve pain and give excellent functional results throughout the patients’ remaining years with a very small incidence of revision, except in cases of infection

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: Bone Joint J 2015;97-B:10–18