Radiological residual acetabular dysplasia (RAD) has been reported in up to 30% of children who had successful brace treatment of infant developmental dysplasia of the hip (DDH). Predicting those who will resolve and those who may need corrective surgery is important to optimize follow-up protocols. In this study we have aimed to identify the prevalence and predictors of RAD at two years and five years post-bracing. This was a single-centre, prospective longitudinal cohort study of infants with DDH managed using a published, standardized Pavlik harness protocol between January 2012 and December 2016. RAD was measured at two years’ mean follow-up using acetabular index-lateral edge (AI-L) and acetabular index-sourcil (AI-S), and at five years using AI-L, AI-S, centre-edge angle (CEA), and acetabular depth ratio (ADR). Each hip was classified based on published normative values for normal, borderline (1 to 2 standard deviations (SDs)), or dysplastic (> 2 SDs) based on sex, age, and laterality.Aims
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Surgical site infection (SSI) after soft-tissue sarcoma (STS) resection is a serious complication. The purpose of this retrospective study was to investigate the risk factors for SSI after STS resection, and to develop a nomogram that allows patient-specific risk assessment. A total of 547 patients with STS who underwent tumour resection between 2005 and 2021 were divided into a development cohort and a validation cohort. In the development cohort of 402 patients, the least absolute shrinkage and selection operator (LASSO) regression model was used to screen possible risk factors of SSI. To select risk factors and construct the prediction nomogram, multivariate logistic regression was used. The predictive power of the nomogram was evaluated by receiver operating curve (ROC) analysis in the validation cohort of 145 patients.Aims
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Frailty greatly increases the risk of adverse outcome of trauma in older people. Frailty detection tools appear to be unsuitable for use in traumatically injured older patients. We therefore aimed to develop a method for detecting frailty in older people sustaining trauma using routinely collected clinical data. We analyzed prospectively collected registry data from 2,108 patients aged ≥ 65 years who were admitted to a single major trauma centre over five years (1 October 2015 to 31 July 2020). We divided the sample equally into two, creating derivation and validation samples. In the derivation sample, we performed univariate analyses followed by multivariate regression, starting with 27 clinical variables in the registry to predict Clinical Frailty Scale (CFS; range 1 to 9) scores. Bland-Altman analyses were performed in the validation cohort to evaluate any biases between the Nottingham Trauma Frailty Index (NTFI) and the CFS.Aims
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Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
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:
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
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The Uppföljningsprogram för cerebral pares (CPUP) Hip Score distinguishes between children with cerebral palsy (CP) at different levels of risk for displacement of the hip. The score was constructed using data from Swedish children with CP, but has not been confirmed in any other population. The aim of this study was to determine the calibration and discriminatory accuracy of this score in children with CP in Scotland. This was a total population-based study of children registered with the Cerebral Palsy Integrated Pathway Scotland. Displacement of the hip was defined as a migration percentage (MP) of > 40%. Inclusion criteria were children in Gross Motor Function Classification System (GMFCS) levels III to V. The calibration slope was estimated and Kaplan-Meier curves produced for five strata of CPUP scores to compare the observed with the predicted risk of displacement of the hip at five years. For discriminatory accuracy, the time-dependent area under the receiver operating characteristic curve (AUC) was estimated. In order to analyze differences in the performance of the score between cohorts, score weights, and subsequently the AUC, were re-estimated using the variables of the original score: the child’s age at the first examination, GMFCS level, head shaft angle, and MP of the worst hip in a logistic regression with imputation of outcomes for those with incomplete follow-up.Aims
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There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
Rotating-hinge knee prostheses are commonly used to reconstruct the distal femur after resection of a tumour, despite the projected long-term burden of reoperation due to complications. Few studies have examined the factors that influence their failure and none, to our knowledge, have used competing risk models to do so. The purpose of this study was to determine the risk factors for failure of a rotating-hinge knee distal femoral arthroplasty using the Fine-Gray competing risk model. We retrospectively reviewed 209 consecutive patients who, between 1991 and 2016, had undergone resection of the distal femur for tumour and reconstruction using a rotating-hinge knee prosthesis. The study endpoint was failure of the prosthesis, defined as removal of the femoral component, the tibial component, or the bone-implant fixation; major revision (exchange of the femoral component, tibial component, or the bone-implant fixation); or amputation.Aims
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To estimate the measurement properties for the Oxford Knee Score (OKS) in patients undergoing revision knee arthroplasty (responsiveness, minimal detectable change (MDC-90), minimal important change (MIC), minimal important difference (MID), internal consistency, construct validity, and interpretability). Secondary data analysis was performed for 10,727 patients undergoing revision knee arthroplasty between 2013 to 2019 using a UK national patient-reported outcome measure (PROM) dataset. Outcome data were collected before revision and at six months postoperatively, using the OKS and EuroQol five-dimension score (EQ-5D). Measurement properties were assessed according to COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) guidelines.Aims
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A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the CPAK classification. CPAK comprises nine phenotypes based on the arithmetic HKA (aHKA) that estimates constitutional limb alignment and joint line obliquity (JLO). Intraoperative balance was compared within each phenotype in a cohort of 138 computer-assisted TKAs randomized to KA or MA. Primary outcomes included descriptive analyses of healthy and OA groups per CPAK type, and comparison of balance at 10° of flexion within each type. Secondary outcomes assessed balance at 45° and 90° and bone recuts required to achieve final knee balance within each CPAK type.Aims
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Our aim was to develop and validate nomograms that would predict the cumulative incidence of sarcoma-specific death (CISSD) and disease progression (CIDP) in patients with localized high-grade primary central and dedifferentiated chondrosarcoma. The study population consisted of 391 patients from two international sarcoma centres (development cohort) who had undergone definitive surgery for a localized high-grade (histological grade II or III) conventional primary central chondrosarcoma or dedifferentiated chondrosarcoma. Disease progression captured the first event of either metastasis or local recurrence. An independent cohort of 221 patients from three additional hospitals was used for external validation. Two nomograms were internally and externally validated for discrimination (c-index) and calibration plot.Aims
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This study evaluates the quality of patient-reported outcome measures (PROMs) reported in childhood fracture trials and recommends outcome measures to assess and report physical function, functional capacity, and quality of life using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) standards. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic review of OVID Medline, Embase, and Cochrane CENTRAL was performed to identify all PROMs reported in trials. A search of OVID Medline, Embase, and PsycINFO was performed to identify all PROMs with validation studies in childhood fractures. Development studies were identified through hand-searching. Data extraction was undertaken by two reviewers. Study quality and risk of bias was evaluated by COSMIN guidelines and recorded on standardized checklists.Aims
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The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots.Aims
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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
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The health-related quality of life (HRQoL) of paediatric patients with orthopaedic conditions and spinal deformity is important, but existing generic tools have their shortcomings. We aim to evaluate the use of Paediatric Quality of Life Inventory (PedsQL) 4.0 generic core scales in the paediatric population with specific comparisons between those with spinal and limb pathologies, and to explore the feasibility of using PedsQL for studying scoliosis patients’ HRQoL. Paediatric patients attending a speciality outpatient clinic were recruited through consecutive sampling. Two groups of patients were included: idiopathic scoliosis, and paediatric orthopaedic upper and lower limb condition without scoliosis. Patients were asked to complete PedsQL 4.0 generic core scales, Youth version of 5-level EuroQol-5-dimension questionnaire, and Refined Scoliosis Research Society 22-item (SRS-22r) questionnaire. Statistical analyses included scores comparison between scoliosis and limb pathology patients using independent-samples Aims
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The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
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To validate the Sydney Hamstring Origin Rupture Evaluation (SHORE), a hamstring-specific clinical assessment tool to evaluate patient outcomes following surgical treatment. A prospective study of 70 unilateral hamstring surgical repairs, with a mean age of 47.3 years (15 to 73). Patients completed the SHORE preoperatively and at six months post-surgery, and then completed both the SHORE and Perth Hamstring Assessment Tool (PHAT) at three years post-surgery. The SHORE questionnaire was validated through the evaluation of its psychometric properties, including; internal consistency, reproducibility, reliability, sensitivity to change, and ceiling effect. Construct validity was assessed using Pearson’s correlation analysis to examine the strength of association between the SHORE and the PHAT.Aims
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Aims. The aim of this study was to assess whether supine flexibility predicts the likelihood of curve progression in patients with adolescent idiopathic scoliosis (AIS) undergoing brace treatment. Methods. This was a retrospective analysis of patients with AIS prescribed with an underarm brace between September 2008 to April 2013 and followed up until 18 years of age or required surgery. Patients with structural proximal curves that preclude underarm bracing, those who were lost to follow-up, and those who had poor compliance to bracing (<16 hours a day) were excluded. The major curve Cobb angle, curve type, and location were measured on the pre-brace standing posteroanterior (PA) radiograph, supine whole spine radiograph, initial in-brace standing PA radiograph, and the post-brace weaning standing PA radiograph.
The early mortality in patients with hip fractures from bony metastases is unknown. The objectives of this study were to quantify 30- and 90-day mortality in patients with proximal femoral metastases, and to create a mortality prediction tool based on biomarkers associated with early death. This was a retrospective cohort study of consecutive patients referred to the orthopaedic department at a UK trauma centre with a proximal femoral metastasis (PFM) over a seven-year period (2010 to 2016). The study group were compared to a matched control group of non-metastatic hip fractures. Minimum follow-up was one year.Aims
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