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. Methods. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. Results. The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add
Aims. To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. Methods. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across
Aims. The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. Methods. We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation. Results. BMI, the duration of stiffness, the preoperative ROM, the preoperative intensity of pain, and grade of post-traumatic osteoarthritis of the elbow were identified as predictors of outcome and incorporated to construct the nomogram. SPESSO displayed good
Objectives. The objective of this study was to develop a test for the rapid (within 25 minutes) intraoperative detection of bacteria from synovial fluid to diagnose periprosthetic joint infection (PJI). Methods. The 16s rDNA test combines a polymerase chain reaction (PCR) for amplification of 16s rDNA with a lateral flow immunoassay in one fully automated system. The synovial fluid of 77 patients undergoing joint aspiration or primary or revision total hip or knee surgery was prospectively collected. The cohort was divided into a proof-of-principle cohort (n = 17) and a validation cohort (n = 60). Using the proof-of-principle cohort, an optimal cut-off for the
Objectives. To evaluate the applicability of MRI for the quantitative assessment
of anterior talofibular ligaments (ATFLs) in symptomatic chronic
ankle instability (CAI). Methods. Between 1997 and 2010, 39 patients with symptomatic CAI underwent
surgical treatment (22 male, 17 female, mean age 25.4 years (15
to 40)). In all patients, the maximum diameters of the ATFLs were
measured on pre-operative T2-weighted MR images in planes parallel
to the path of the ATFL. They were classified into three groups based
on a previously published method with modifications: ‘normal’, diameter
= 1.0 - 3.2 mm; ‘thickened’, diameter >
3.2 mm; ‘thin or absent’,
diameter <
1.0 mm. Stress radiography was performed with the
maximum manual force in inversion under general anaesthesia immediately
prior to surgery. In surgery, ATFLs were macroscopically divided
into two categories: ‘thickened’, an obvious thickened ligament
and ‘thin or absent’. The imaging results were compared with the
macroscopic results that are considered to be of a gold standard. Results. Agreement was reached when comparison was made between groups,
based on MRI and macroscopic findings. ATFLs were abnormal in all
39 cases and classified as ten ‘thickened’ and 29 ‘thin or absent’.
As to talar tilt stress radiography, a clear cut-off angle, which
would allow
Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
Methods
Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article:
The Oxford Shoulder Score (OSS) is a 12-item measure commonly used for the assessment of shoulder surgeries. This study explores whether computerized adaptive testing (CAT) provides a shortened, individually tailored questionnaire while maintaining test accuracy. A total of 16,238 preoperative OSS were available in the National Joint Registry (NJR) for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey dataset (April 2012 to April 2022). Prior to CAT, the foundational item response theory (IRT) assumptions of unidimensionality, monotonicity, and local independence were established. CAT compared sequential item selection with stopping criteria set at standard error (SE) < 0.32 and SE < 0.45 (equivalent to reliability coefficients of 0.90 and 0.80) to full-length patient-reported outcome measure (PROM) precision.Aims
Methods
Our primary aim was to establish the proportion of female orthopaedic consultants who perform arthroplasty via cases submitted to the National Joint Registry (NJR), which covers England, Wales, Northern Ireland, the Isle of Man, and Guernsey. Secondary aims included comparing time since specialist registration, private practice participation, and number of hospitals worked in between male and female surgeons. Publicly available data from the NJR was extracted on the types of arthroplasty performed by each surgeon, and the number of procedures of each type undertaken. Each surgeon was cross-referenced with the General Medical Council (GMC) website, using GMC number to extract surgeon demographic data. These included sex, region of practice, and dates of full and specialist registration.Aims
Methods
Objectives. 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. Methods. 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. Results. The 30-day mortality rate was 5.36% in the validation dataset (n = 3861), slightly lower than the 6.40% in the development dataset (n = 4044). The NHFD and Nottingham models showed a slightly lower
To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale.Aims
Methods
The Forgotten Joint Score-12 (FJS-12) is a validated patient-reported outcome measure (PROM) tool designed to assess artificial prosthesis awareness during daily activities following total hip arthroplasty (THA). The patient-acceptable symptom state (PASS) is the minimum cut-off value that corresponds to a patient’s satisfactory state-of-health. Despite the validity and reliability of the FJS-12 having been previously demonstrated, the PASS has yet to be clearly defined. This study aims to define the PASS of the FJS-12 following primary THA. We retrospectively reviewed all patients who underwent primary elective THA from 2019 to 2020, and answered both the FJS-12 and the Hip Disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS, JR) questionnaires one-year postoperatively. HOOS, JR score was used as the anchor to estimate the PASS of FJS-12. Two statistical methods were employed: the receiver operating characteristic (ROC) curve point, which maximized the Youden index; and 75th percentile of the cumulative percentage curve of patients who had the HOOS, JR score difference larger than the cut-off value.Aims
Methods
The COVID-19 pandemic has caused unprecedented disruption to elective orthopaedic services. The primary objective of this study was to examine changes in functional scores in patients awaiting total hip arthroplasty (THA), total knee arthroplasty (TKA), and unicompartmental knee arthroplasty (UKA). Secondary objectives were to investigate differences between these groups and identify those in a health state ‘worse than death’ (WTD). In this prospective cohort study, preoperative Oxford hip and knee scores (OHS/OKS) were recorded for patients added to a waiting list for THA, TKA, or UKA, during the initial eight months of the COVID-19 pandemic, and repeated at 14 months into the pandemic (mean interval nine months (SD 2.84)). EuroQoL five-dimension five-level health questionnaire (EQ-5D-5L) index scores were also calculated at this point in time, with a negative score representing a state WTD. OHS/OKS were analyzed over time and in relation to the EQ-5D-5L.Aims
Methods
This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.Aims
Methods
To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
Several previously identified patient-, injury-, and treatment-related factors are associated with the development of nonunion in distal femur fractures. However, the predictive value of these factors is not well defined. We aimed to assess the predictive ability of previously identified risk factors in the development of nonunion leading to secondary surgery in distal femur fractures. We conducted a retrospective cohort study of adult patients with traumatic distal femur fracture treated with lateral locking plate between 2009 and 2018. The patients who underwent secondary surgery due to fracture healing problem or plate failure were considered having nonunion. Background knowledge of risk factors of distal femur fracture nonunion based on previous literature was used to form an initial set of variables. A logistic regression model was used with previously identified patient- and injury-related variables (age, sex, BMI, diabetes, smoking, periprosthetic fracture, open fracture, trauma energy, fracture zone length, fracture comminution, medial side comminution) in the first analysis and with treatment-related variables (different surgeon-controlled factors, e.g. plate length, screw placement, and proximal fixation) in the second analysis to predict the nonunion leading to secondary surgery in distal femur fractures.Aims
Methods
This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated. A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss’ kappa and Cohen’s kappa were calculated for interobserver and intraobserver reliability, respectively.Aims
Methods
The overall aim of this study was to determine the impact of deprivation with regard to quality of life, demographics, joint-specific function, attendances for unscheduled care, opioid and antidepressant use, having surgery elsewhere, and waiting times for surgery on patients awaiting total hip arthroplasty (THA) and total knee arthroplasty (TKA). Postal surveys were sent to 1,001 patients on the waiting list for THA or TKA in a single Northern Ireland NHS Trust, which consisted of the EuroQol five-dimension five-level questionnaire (EQ-5D-5L), visual analogue scores (EQ-VAS), and Oxford Hip and Knee Scores. Electronic records determined prescriptions since addition to the waiting list and out-of-hour GP and emergency department attendances. Deprivation quintiles were determined by the Northern Ireland Multiple Deprivation Measure 2017 using postcodes of home addresses.Aims
Methods
Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp).Aims
Methods
To investigate the optimal thresholds and diagnostic efficacy of commonly used serological and synovial fluid detection indexes for diagnosing periprosthetic joint infection (PJI) in patients who have rheumatoid arthritis (RA). The data from 348 patients who had RA or osteoarthritis (OA) and had previously undergone a total knee (TKA) and/or a total hip arthroplasty (THA) (including RA-PJI: 60 cases, RA-non-PJI: 80 cases; OA-PJI: 104 cases, OA-non-PJI: 104 cases) were retrospectively analyzed. A receiver operating characteristic curve was used to determine the optimal thresholds of the CRP, ESR, synovial fluid white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) for diagnosing RA-PJI and OA-PJI. The diagnostic efficacy was evaluated by comparing the area under the curve (AUC) of each index and applying the results of the combined index diagnostic test.Aims
Methods