To achieve expert clinical consensus in the delivery of hydrodilatation for the treatment of primary frozen shoulder to inform clinical practice and the design of an intervention for evaluation. We conducted a two-stage, electronic questionnaire-based, modified Delphi survey of shoulder experts in the UK NHS. Round one required positive, negative, or neutral ratings about hydrodilatation. In round two, each participant was reminded of their round one responses and the modal (or ‘group’) response from all participants. This allowed participants to modify their responses in round two. We proposed respectively mandating or encouraging elements of hydrodilatation with 100% and 90% positive consensus, and respectively disallowing or discouraging with 90% and 80% negative consensus. Other elements would be optional.Aims
Methods
Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation.Aims
Methods
Aims. The aim of this study was to present data on 11 459 patients
who underwent total hip (THA), total knee (TKA) or unicompartmental
knee arthroplasty (UKA) between November 2002 and April 2014 with
aspirin as the primary agent for pharmacological thromboprophylaxis. . Patients and Methods. We analysed the incidence of deep vein thrombosis (DVT) and pulmonary
embolism (PE) then compared the 90-day all-cause mortality with
the corresponding data in the National Joint Registry for England
and Wales (NJR). . Results. The incidence of PE was 0.6% after THA, 1.47% after TKA and 1.2%
after UKA. The 90-day mortality was 0.39% after THA and 0.44% after
TKA. No deaths occurred after UKA. The main causes of death were
ischaemic heart disease and respiratory failure. PE was responsible
for only 18% of deaths. There was a decline in 90-day mortality,
from 0.64% between 2002 and 2007, to 0.21% between 2008 and 2013
after THA, and from 0.47% to 0.39% after TKA for the corresponding
period. The standardised mortality ratio (SMR) declined from 86.5
(confidence interval (CI) 63.0 to 137.7) to 39.7 (CI 31.2 to 54.3)
p = 0.024. The incidence of proximal DVT was 0.3%. . Take home message: With individualised
The aim of this study was to explore current use of the Global Fragility Fracture Network (FFN) Minimum Common Dataset (MCD) within established national hip fracture registries, and to propose a revised MCD to enable international benchmarking for hip fracture care. We compared all ten established national hip fracture registries: England, Wales, and Northern Ireland; Scotland; Australia and New Zealand; Republic of Ireland; Germany; the Netherlands; Sweden; Norway; Denmark; and Spain. We tabulated all questions included in each registry, and cross-referenced them against the 32 questions of the MCD dataset. Having identified those questions consistently used in the majority of national audits, and which additional fields were used less commonly, we then used consensus methods to establish a revised MCD.Aims
Methods
Periprosthetic joint infections (PJIs) and fracture-related infections (FRIs) are associated with a significant risk of adverse events. However, there is a paucity of data on cardiac complications following revision surgery for PJI and FRI and how they impact overall mortality. Therefore, this study aimed to investigate the risk of perioperative myocardial injury (PMI) and mortality in this patient cohort. We prospectively included consecutive patients at high cardiovascular risk (defined as age ≥ 45 years with pre-existing coronary, peripheral, or cerebrovascular artery disease, or any patient aged ≥ 65 years, plus a postoperative hospital stay of > 24 hours) undergoing septic or aseptic major orthopaedic surgery between July 2014 and October 2016. All patients received a systematic screening to reliably detect PMI, using serial measurements of high-sensitivity cardiac troponin T. All-cause mortality was assessed at one year. Multivariable logistic regression models were applied to compare incidence of PMI and mortality between patients undergoing septic revision surgery for PJI or FRI, and patients receiving aseptic major bone and joint surgery.Aims
Methods
This study aimed to describe the use of revision knee arthroplasty in Australia and examine changes in lifetime risk over a decade. De-identified individual-level data on all revision knee arthroplasties performed in Australia from 2007 to 2017 were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. Population data and life tables were obtained from the Australian Bureau of Statistics. The lifetime risk of revision surgery was calculated for each year using a standardized formula. Separate calculations were undertaken for males and females.Aims
Methods
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
The long-term effects of metal-on-metal arthroplasty are currently under scrutiny because of the potential biological effects of metal wear debris. This review summarises data describing the release, dissemination, uptake, biological activity, and potential toxicity of metal wear debris released from alloys currently used in modern orthopaedics. The introduction of
Type 2 diabetes mellitus (T2DM) impairs bone strength and is a significant risk factor for hip fracture, yet currently there is no reliable tool to assess this risk. Most risk stratification methods rely on bone mineral density, which is not impaired by diabetes, rendering current tests ineffective. CT-based finite element analysis (CTFEA) calculates the mechanical response of bone to load and uses the yield strain, which is reduced in T2DM patients, to measure bone strength. The purpose of this feasibility study was to examine whether CTFEA could be used to assess the hip fracture risk for T2DM patients. A retrospective cohort study was undertaken using autonomous CTFEA performed on existing abdominal or pelvic CT data comparing two groups of T2DM patients: a study group of 27 patients who had sustained a hip fracture within the year following the CT scan and a control group of 24 patients who did not have a hip fracture within one year. The main outcome of the CTFEA is a novel measure of hip bone strength termed the Hip Strength Score (HSS).Aims
Methods
The incidence of atypical femoral fractures (AFFs) continues to increase. However, there are currently few long-term studies on the complications of AFFs and factors affecting them. Therefore, we attempted to investigate the outcomes, complications, and risk factors for complication through mid-term follow-up of more than three years. From January 2003 to January 2016, 305 patients who underwent surgery for AFFs at six hospitals were enrolled. After exclusion, a total of 147 patients were included with a mean age of 71.6 years (48 to 89) and 146 of whom were female. We retrospectively evaluated medical records, and reviewed radiographs to investigate the fracture site, femur bowing angle, presence of delayed union or nonunion, contralateral AFFs, and peri-implant fracture. A statistical analysis was performed to identify the significance of associated factors.Aims
Methods
We compared a group of 46 somatised patients with a control group of 41 non-somatised patients who had undergone elective surgery to the lumbar spine in an attempt to identify pre-operative factors which could predict the outcome. In a prospective single-centre study, the Distress and
This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
Methods
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
Fungal periprosthetic joint infections (fPJIs) are rare complications, constituting only 1% of all PJIs. Neither a uniform definition for fPJI has been established, nor a standardized treatment regimen. Compared to bacterial PJI, there is little evidence for fPJI in the literature with divergent results. Hence, we implemented a novel treatment algorithm based on three-stage revision arthroplasty, with local and systemic antifungal therapy to optimize treatment for fPJI. From 2015 to 2018, a total of 18 patients with fPJI were included in a prospective, single-centre study (DKRS-ID 00020409). The diagnosis of PJI is based on the European Bone and Joint Infection Society definition of periprosthetic joint infections. The baseline parameters (age, sex, and BMI) and additional data (previous surgeries, pathogen spectrum, and Charlson Comorbidity Index) were recorded. A therapy protocol with three-stage revision, including a scheduled spacer exchange, was implemented. Systemic antifungal medication was administered throughout the entire treatment period and continued for six months after reimplantation. A minimum follow-up of 24 months was defined.Aims
Methods
It is imperative to understand the risks of operating on urgent cases during the COVID-19 (SARS-Cov-2 virus) pandemic for clinical decision-making and medical resource planning. The primary aim was to determine the mortality risk and associated variables when operating on urgent cases during the COVID-19 pandemic. The secondary objective was to assess differences in the outcome of patients treated between sites treating COVID-19 and a separate surgical site. The primary outcome measure was 30-day mortality. Secondary measures included complications of surgery, COVID-19 infection, and length of stay. Multiple variables were assessed for their contribution to the 30-day mortality. In total, 433 patients were included with a mean age of 65 years; 45% were male, and 90% were Caucasian.Aims
Methods
To compare the functionality of adults with displaced mid-shaft clavicular fractures treated either operatively or nonoperatively and to compare the relative risk of nonunion and reoperation between the two groups. Based on specific eligibility criteria, 120 adults (median age 37.5 years (interquartile range (18 to 61)) and 84% males (n = 101)) diagnosed with an acute displaced mid-shaft fracture were recruited, and randomized to either the operative (n = 60) or nonoperative (n = 60) treatment group. This randomized controlled, partially blinded trial followed patients for 12 months following initial treatment. Functionality was assessed by the Constant score (CS) (assessor blinded to treatment) and Disability of the Arm, Shoulder and Hand (DASH) score. Clinical and radiological evaluation, and review of patient files for complications and reoperations, were added as secondary outcomes.Aims
Methods
Restarting planned surgery during the COVID-19 pandemic is a clinical and societal priority, but it is unknown whether it can be done safely and include high-risk or complex cases. We developed a Surgical Prioritization and Allocation Guide (SPAG). Here, we validate its effectiveness and safety in COVID-free sites. A multidisciplinary surgical prioritization committee developed the SPAG, incorporating procedural urgency, shared decision-making, patient safety, and biopsychosocial factors; and applied it to 1,142 adult patients awaiting orthopaedic surgery. Patients were stratified into four priority groups and underwent surgery at three COVID-free sites, including one with access to a high dependency unit (HDU) or intensive care unit (ICU) and specialist resources. Safety was assessed by the number of patients requiring inpatient postoperative HDU/ICU admission, contracting COVID-19 within 14 days postoperatively, and mortality within 30 days postoperatively.Aims
Methods
Low-energy fractures of the proximal humerus indicate osteoporosis and it is important to direct treatment to this group of patients who are at high risk of further fracture. Data were prospectively collected from 79 patients (11 men, 68 women) with a mean age of 69 years (55 to 86) with fractures of the proximal humerus in order to determine if current guidelines on the measurement of the bone mineral density at the hip and lumbar spine were adequate to stratify the risk and to guide the treatment of osteoporosis. Bone mineral density measurements were made by dual-energy x-ray absorptiometry at the proximal femur, lumbar spine (L2-4) and contralateral distal radius, and the T-scores were generated for comparison. Data were also collected on the use of steroids, smoking, the use of alcohol, hand dominance and comorbidity. The mean T-score for the distal radius was −2.97 (. sd. 1.56) compared with −1.61 (. sd. 1.62) for the lumbar spine and −1.78 (. sd. 1.33) for the femur. There was a significant difference between the mean lumbar and radial T scores (1.36 (1.03 to 1.68); p <
0.001) and between the mean femoral and radial T-scores (1.18 (0.92 to 1.44); p <
0.001). The inclusion of all three sites in the determination of the T-score increased the sensitivity to 66% compared with that of 46% when only the proximal femur and lumbar spine were used. This difference between measurements in the upper limb compared with the axial skeleton and lower limb suggests that basing
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
