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 discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). Conclusion. Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (. https://jointcalc.shef.ac.uk. ). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808–820

Background and purpose. Investigating inter-vertebral biomechanics in vivo using end-of-range imaging is difficult due to high intra subject variation, measurement errors and insufficient data. Quantitative fluoroscopy (QF) can reliably measure continuous motion but may suffer from contamination from uncontrolled loading and muscle contraction which compromises comparisons between studies and limits interpretation of results. This study presents the methods used to overcome these limitations. Methods and results. Forty chronic, non-specific low back pain (CNSLPB) patients and 40 matched controls underwent QF using a passive recumbent protocol which standardised the rate and range of trunk rotation, thus reducing intra-subject variation and excluding loading and muscle contraction factors. Left, right, flexion and extension were recorded from L2-5 and vertebral motion registered using image processing algorithms, Resultant continuous inter-vertebral rotation data were normalised to produce proportional contributions of each segment throughout the trunk bend. The expected continuous proportional contributions at each level and direction were determined by calculating reference intervals (mean +/− 2SD) from controls. Prevalence of patients exceeding these ranges was determined and the association with CNSLBP calculated using Chi-squared analysis. Additionally the variance of the normalised data throughout the continuous motion for each direction was determined and summed to produce an combined number. This was used to measure the difference between patients and controls and entered into ROC curve analysis to investigate discrimination between patients and controls. Conclusion. A methodology for assessment of the differences between the continuous in vivo spine kinematics of CNSLBP patients and healthy controls has been developed and will be presented

Introduction: It is now well recognised that the patient’s perception of the medical problem and the treatment for the medical problem are not always the same as the facts of the diagnosis and treatment process. The study being reported was conducted to determine the validity of the SRS-22 patient questionnaire for the discrimination of scoliosis patients based on curve pattern and curve size. Materials: Three study groups were developed. The first or control group consisted of patients who had been referred for evaluation of suspected scoliosis but documented by X-ray not to have structural scoliosis of 10° or more. The second group, a non surgical group (NS) consisted of patients with documented idiopathic scoliosis who were either being evaluated and discharged, observed either short or long term, or who had been or would be braced. The third or surgical group (S) were being seen prior to primary idiopathic scoliosis surgery. Patients with comorbidities were excluded. Methods: Deformity pattern and Cobb measurement were determined from standing frontal and sagittal plane radiographs. Each patient completed a SRS-22 outcomes questionnaire leaving off the satisfaction with management domain. Thus there were four domains: pain; self image; function; and mental health, five questions per domain. Scoring is 5 best and 1 lowest. Case series: Patients were gathered between October 1999 and September 2000. The control group consisted of 17 patients average age 13 years. Non surgical group included 72 patients of average age 16 years and average scoliosis of 33°. The surgical group consisted of 33 patients of average age 16 years with an average curve size of 64°. Statistical analysis: The effect of curve pattern was studied with ANOVA and the effect of curve size by the Pearson correlation coefficient. Results: There were 69 patients with single, 33 with double and three with triple curves. There was no difference in SRS domain or total scores based on curve pattern. There was a very significant correlation between curve size and SRS-22 score, p> 0.001 for pain; self image, function; and a total of these domains. For mental health there was also a significant relationship at p=0.0124. Conclusion: The SRS-22 questionnaire successfully discriminates among persons with no scoliosis, moderate scoliosis, and large scoliosis by curve size. It does not discriminate among patients with single, double or triple curves

Object: Smoking is a negative prognostic factor in the outcome of some fractures. We evaluated whether smoking is associated with primary care quality and referral to orthopedic surgeons for an isolated injury.

Materials and Methods: We enrolled all new ambulatory cases with an isolated injury to an extremity referred to an orthopedic trauma clinic. Data were analyzed concerning: type of trauma, prior medical consultations, quality of initial management, patient characteristics and smoking status.

Results: Among 166 consecutive patients referred, 45 were smokers. Family income was under $30 000 for 44% of smokers compared to 27% for non-smokers (p< 0,05). Smokers were younger (43 y.o. vs 50 y.o.; p< 0,05) and used illegal drugs more often (16% vs 5%; p< 0,05). Smokers were more likely to have been injured at work while non -smokers reported their injury as a sport accident. Injury severity, type of injury and ethnic characteristics were not different. Smokers were twice as likely to receive an unacceptable immobilization for their injury than non-smokers (52% vs 25%; p< 0,05) and received inadequate walking aids (26% vs 9%;0< 0,05). Delay from first primary care consultation to orthopedic appointment was almost 2 times longer for smokers (93hrs vs 58hrs; p< 0,05).

Discussion and Conclusion: Injured smokers received a lower standard of care and had longer delays for orthopedic consultations. Primary care quality and efficiency were associated with smoking status, possibly due to medical bias or incorrect use of health service by patients. Relevance: Smoking is a risk factor for complications in orthopedic surgery. Our results suggest that biology may not be the only explanation.

Areal BMD (aBMD) is relatively poor at discriminating those patients at risk of hip fracture. This study tested the hypothesis that a measure of bending resistance, cross section moment of inertia (CSMI) and section modulus, derived from 3D peripheral quantitative computed tomography (pQCT) images made ex-vivo, would discriminate cases of hip fracture from controls better than areal bone mineral density.

The biopsies were from (n = 20, F) subjects that had suffered an intracapsular hip fracture. The control material (n = 23, F) was from post-mortem subjects. Serial pQCT 1mm thick cross-sectional images using the Densiscan 1000 pQCT clinical forearm densitometer were obtained, and matched for location along the neck. The image voxels were converted to units of bone mass, which were then used to derive the mass weighted CSMI (MWCSMI), section modulus and areal bone mineral density, (see Table).

The aBMD results showed that the difference between the means of the fracture cases compared to the controls was 9.9% (−0.061g/cm²; +0.0055g/cm², −0.127g/cm²; 95% confidence interval). However, the MWCSMI was 29.5% (−5966mm⁴; −8868mm⁴,−3066mm⁴; 95% confidence interval) lower in the fracture cases compared to the controls, while section modulus was 32.5% (−242mm³; −133mm³, −352mm³ 95% confidence interval) lower. When presented as Z scores the fracture cases had considerably lower section modulus Z scores (mean −1.27 SD, p=0.0001) than aBMD – Z scores (mean −0.5 SD, p=0.07). To simulate the forces experienced during a sideways fall, the model’s neutral axis was rotated by 210°. The results were similar for section modulus to those at 0°.

This study suggests that biomechanical analysis of the distribution of bone within the femoral neck may offer a marked improvement in the ability to discriminate patients with an increased risk of intracapsular fracture. Progress towards implementing this form of analysis in clinical densitometry should improve its diagnostic value.

Introduction

Cell-based therapies become more and more prominent for the treatment of intervertebral disc (IVD) injuries. Different strategies are under current development and address the restoration of either annulus fibrosus (AF) or nucleus pulposus (NP). Application of such Advanced Therapy Medicinal Products (ATMPs) is strictly regulated. One requirement is to show the identity of the cells, to make sure the cells are indeed AF or NP cells and retained their IVD cell character during manufacturing process before injection to the site of injury. Therefore, we recently identified novel marker genes that discriminate AF and NP cells on tissue level. However, expression of these AF and NP tissue markers has not been investigated in cultured cells, yet. The aim of this study was to proof the tissue marker”s specificity to discriminate cultured AF and NP cells. Furthermore, we evaluated the tissue markers robustness to different cell culture conditions.

Background and Purpose of Study:

Differences in regional lumbar angles in sitting have been observed between subgroups of NSCLBP patients exhibiting motor control impairments (MCI) (O'Sullivan, 2005; Dankaerts et al, 2006). However, differences in standing posture and other spinal regions are unknown. This study aimed to compare regional spinal angles in healthy and MCI subgroups in sitting and standing.

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 discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results. The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Cite this article: Bone Jt Open 2023;4(3):168–181

With the rising rates, and associated costs, of total knee arthroplasty (TKA), enhanced clarity regarding patient appropriateness for TKA is warranted. Towards addressing this gap, we elucidated in qualitative research that surgeons and osteoarthritis (OA) patients considered TKA need, readiness/willingness, health status, and expectations of TKA most important in determining patient appropriateness for TKA. The current study evaluated the predictive validity of pre-TKA measures of these appropriateness domains for attainment of a good TKA outcome. This prospective cohort study recruited knee OA patients aged 30+ years referred for TKA at two hip/knee surgery centers in Alberta, Canada. Those receiving primary, unilateral TKA completed questionnaires pre-TKA assessing TKA need (WOMAC-pain, ICOAP-pain, NRS-pain, KOOS-physical function, Perceived Arthritis Coping Efficacy, prior OA treatment), TKA readiness/willingness (Patient Acceptable Symptom State (PASS), willingness to undergo TKA), health status (PHQ-8, BMI, MSK and non-MSK comorbidities), TKA expectations (HSS KR Expectations survey items) and contextual factors (e.g., age, gender, employment status). One-year post-TKA, we assessed for a ‘good outcome’ (yes/no), defined as improved knee symptoms (OARSI-OMERACT responder criteria) AND overall satisfaction with TKA results. Multiple logistic regression, stepwise variable selection, and best possible subsets regression was used to identify the model with the smallest number of independent variables and greatest discriminant validity for our outcome. Receiver Operating Characteristic (ROC) curves were generated to compare the discriminative ability of each appropriateness domain based on the ‘area under the ROC curve’ (AUC). Multivariable robust Poisson regression was used to assess the relationship of the variables to achievement of a good outcome. f 1,275 TKA recipients, 1,053 (82.6%) had complete data for analyses (mean age 66.9 years [SD 8.8]; 58.6% female). Mean WOMAC pain and KOOS-PS scores were 11.5/20 (SD 3.5) and 52.8/100 (SD 17.1), respectively. 78.1% (95% CI 75.4–80.5%) achieved a good outcome. Stepwise variable selection identified optimal discrimination was achieved with 13 variables. The three best 13-variable models included measures of TKA need (WOMAC pain, KOOS-PS), readiness/willingness (PASS, TKA willingness), health status (PHQ-8, troublesome hips, contralateral knee, low back), TKA expectations (the importance of improved psychological well-being, ability to go up stairs, kneel, and participate in recreational activities as TKA outcomes), and patient age. Model discrimination was fair for TKA need (AUC 0.68, 95% CI 0.63-0.72), TKA readiness/willingness (AUC 0.61, 95% CI 0.57-0.65), health status (AUC 0.59, 95% CI 0.54-0.63) and TKA expectations (AUC 0.58, 95% CI 0.54-0.62), but the model with all appropriateness variables had good discrimination (AUC 0.72, 95% CI 0.685-0.76). The likelihood of achieving a good outcome was significantly higher for those with greater knee pain, disability, unacceptable knee symptoms, definite willingness to undergo TKA, less depression who considered improved ability to perform recreational activities or climb stairs ‘very important’ TKA outcomes, and lower in those who considered it important that TKA improve psychological wellbeing or ability to kneel. Beyond surgical need (OA symptoms) and health status, assessment of patients’ readiness and willingness to undergo, and their expectations for, TKA, should be incorporated into assessment of patient appropriateness for surgery

Aims. Though most humeral shaft fractures heal nonoperatively, up to one-third may lead to nonunion with inferior outcomes. The Radiographic Union Score for HUmeral Fractures (RUSHU) was created to identify high-risk patients for nonunion. Our study evaluated the RUSHU’s prognostic performance at six and 12 weeks in discriminating nonunion within a significantly larger cohort than before. Methods. Our study included 226 nonoperatively treated humeral shaft fractures. We evaluated the interobserver reliability and intraobserver reproducibility of RUSHU scoring using intraclass correlation coefficients (ICCs). Additionally, we determined the optimal cut-off thresholds for predicting nonunion using the receiver operating characteristic (ROC) method. Results. The RUSHU demonstrated good interobserver reliability with an ICC of 0.78 (95% CI 0.72 to 0.83) at six weeks and 0.77 (95% CI 0.71 to 0.82) at 12 weeks. Intraobserver reproducibility was good or excellent for all analyses. Area under the curve in the ROC analysis was 0.83 (95% CI 0.77 to 0.88) at six weeks and 0.89 (95% CI 0.84 to 0.93) at 12 weeks, indicating excellent discrimination. The optimal cut-off values for predicting nonunion were ≤ eight points at six weeks and ≤ nine points at 12 weeks, providing the best specificity-sensitivity trade-off. Conclusion. The RUSHU proves to be a reliable and reproducible radiological scoring system that aids in identifying patients at risk of nonunion at both six and 12 weeks post-injury during non-surgical treatment of humeral shaft fractures. The statistically optimal cut-off values for predicting nonunion are ≤ eight at six weeks and ≤ nine points at 12 weeks post-injury

Approximately 20% of patients feel unsatisfied 12 months after primary total knee arthroplasty (TKA). Current predictive tools for TKA focus on the clinician as the intended user rather than the patient. The aim of this study is to develop a tool that can be used by patients without clinician assistance, to predict health-related quality of life (HRQoL) outcomes 12 months after total knee arthroplasty (TKA). All patients with primary TKAs for osteoarthritis between 2012 and 2019 at a tertiary institutional registry were analysed. The predictive outcome was improvement in Veterans-RAND 12 utility score at 12 months after surgery. Potential predictors included patient demographics, co-morbidities, and patient reported outcome scores at baseline. Logistic regression and three machine learning algorithms were used. Models were evaluated using both discrimination and calibration metrics. Predictive outcomes were categorised into deciles from 1 being the least likely to improve to 10 being the most likely to improve. 3703 eligible patients were included in the analysis. The logistic regression model performed the best in out-of-sample evaluation for both discrimination (AUC = 0.712) and calibration (gradient = 1.176, intercept = -0.116, Brier score = 0.201) metrics. Machine learning algorithms were not superior to logistic regression in any performance metric. Patients in the lowest decile (1) had a 29% probability for improvement and patients in the highest decile (10) had an 86% probability for improvement. Logistic regression outperformed machine learning algorithms in this study. The final model performed well enough with calibration metrics to accurately predict improvement after TKA using deciles. An ongoing randomised controlled trial (ACTRN12622000072718) is evaluating the effect of this tool on patient willingness for surgery. Full results of this trial are expected to be available by April 2023. A free-to-use online version of the tool is available at . smartchoice.org.au.

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 discrimination with a C-index of 0.73 (95% confidence interval 0.64 to 0.81). A high C-index value of 0.70 could still be reached in the interval validation. The calibration graph showed good agreement between the nomogram prediction and the outcome. Conclusion. The newly developed SPESSO is a valid and convenient model which can be used to predict the outcome of open arthrolysis of the elbow. It could assist clinicians in counselling patients regarding the choice and expectations of treatment. Cite this article: Bone Joint J 2022;104-B(4):486–494

This qualitative study aims to explore and highlight the experiences of trainees in the Orthopaedic Surgical Education Training (SET) program in New Zealand, with a focus on identifying gender-specific biases which may impact professional development. Orthopaedic SET trainees in New Zealand were invited to complete a qualitative, semi-structured questionnaire exploring their experiences in the Orthopaedic SET program. A broad range of topics were covered, addressing culture, belonging, learning styles and role modelling. Recurrent themes were identified using inductive methods. Analysis of questionnaire responses identified several key themes for women in the Orthopaedic SET program, compared to their male counterparts, including (1) role incredulity, (2) confidence vs. competence, (3) adaptation, (4) interdisciplinary relationships and (5) role modelling. Female participants described experiencing gender bias or discrimination by both patients and interdisciplinary colleagues at a higher rate than their male counterparts. The majority of female participants described feeling as competent as their male counterparts at the same SET level, however, identified that they do not typically exhibit the same confidence in their surgical abilities. Whilst similar numbers of female and male participants described experiencing barriers to career progression, female participants described having to adapt both physically and socially to overcome additional gender-specific barriers. Positive influences on training experience included role modelling and supportive relationships amongst trainee groups. This study highlighted gender-specific biases experienced by trainees in the Orthopaedic SET program in New Zealand. Further investigation is warranted to determine how these experiences affect professional development, and how they may be addressed to foster increased gender equity in the surgical profession. This will likely require system-level interventions to create meaningful and sustainable culture change

Aim. This study aimed to externally validate promising preoperative PJI prediction models in a recent, multinational European cohort. Method. Three preoperative PJI prediction models (by Tan et al., Del Toro et al., and Bülow et al.) which previously demonstrated high levels of accuracy were selected for validation. A multicenter retrospective observational analysis was performed of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) between January 2020 and December 2021 and treated at centers in the Netherlands, Portugal, and Spain. Patient characteristics were compared between our cohort and those used to develop the prediction models. Model performance was assessed through discrimination and calibration. Results. A total of 2684 patients were included of whom 60 developed a PJI (2.2%). Our patient cohort differed from the models’ original cohorts in terms of demographic variables, procedural variables, and the prevalence of comorbidities. The c-statistics for the Tan, Del Toro, and Bülow models were 0.72, 0.69, and 0.72 respectively. Calibration was reasonable, but precise percentage estimates for PJI risk were most accurate for predicted risks up to 3-4%; the Tan model overestimated risks above 4%, while the Del Toro model underestimated risks above 3%. Conclusions. In this multinational cohort study, the Tan, Del Toro, and Bülow PJI prediction models were found to be externally valid for classifying high risk patients for developing a PJI. These models hold promise for clinical application to enhance preoperative patient counseling and targeted prevention strategies. Keywords. Periprosthetic Joint Infection (PJI), High Risk Groups, Prediction Models, Validation, Infection Prevention

Several different algorithms attempt to estimate life expectancy for patients with metastatic spine disease. The Skeletal Oncology Research Group (SORG) has recently developed a nomogram to estimate survival of patients with metastatic spine disease. Whilst the use of the SORG nomogram has been validated in the international context, there has been no study to date that validates the use of the SORG nomogram in New Zealand. This study aimed to validate the use of the SORG nomogram in Aotearoa New Zealand. We collected data on 100 patients who presented to Waikato Hospital with a diagnosis of spinal metastatic disease. The SORG nomogram gave survival probabilities for each patient at each time point. Receiver Operating Characteristic (ROC) Area Under Curve (AUC) analysis was performed to assess the predictive accuracy of the SORG score. A calibration curve was also performed, and Brier scores calculated. A multivariate Cox regression analysis was performed. The SORG score was correlated with 30 day (AUC = 0.72) and 90-day mortality (AUC = 0.71). The correlation between the SORG score and 90-day mortality was weaker (AUC = 0.69). Using this method, the nomogram was correct for 79 (79%) patients at 30-days, 59 patients (59%) at 90-days, and 42 patients (42%) at 365-days. Calibration curves demonstrated poor forecasting of the SORG nomogram at 30 (Brier score = 0.65) and 365 days (Brier score = 0.33). The calibration curve demonstrated borderline forecasting of the SORG nomogram at 90 days (Brier score = 0.28). Several components of the SORG nomogram were not found to be correlated with mortality. In this New Zealand cohort the SORG nomogram demonstrated only acceptable discrimination at best in predicting life 30-, 90- or 356-day mortality in patients with metastatic spinal disease

Introduction. The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Methods. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)). Results. Clustering partitioned this cohort (training data set) into a high bone density subgroup consisting of 96 patients and a low bone density subgroup consisting of 146 patients. The optimal number of clusters (n = 2) was determined based on optimization metrics. Discrimination of the cross validated classification model showed comparable performance for the training (accuracy=91.2%; AUC=0.967) and testing data (accuracy=90.5 %; AUC=0.958) while outperforming the conventional statistical model (Logistic Regression (LR)). Local interpretable model-agnostic explanations (LIME) were created for each patient to explain how the predicted output was achieved. Conclusion. The trained and tested model provides preoperative information for surgeons treating patients with potentially poor bone quality. The use of machine learning and patient-specific calibration showed that multiple 3D bone density scores improved accuracy for objective preoperative bone quality assessment

Quantitative ultrasound (QUS) is a promising tool to estimate bone structure characteristics and predict fragile fracture. The aim of this pilot cross-sectional study was to evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragile fractures retrospectively in postmenopausal women. Methods. RF signal and speed of sound (SOS) were obtained using an axial transmission QUS at one‐third distal radius for 246 postmenopausal women. Based on the involved RF signal, we conducted a MResNet, which combines multi-channel training with original ResNet, to classify the high risk of fragility fractures patients from all subjects. The bone mineral density (BMD) at lumber, hip and femoral neck acquired with DXA was recorded on the same day. The fracture history of all subjects in adulthood were collected. To assess the ability of the different methods in the discrimination of fragile fracture, the odds ratios (OR) calculated using binomial logistic regression analysis and the area under the receiver operator characteristic curves (AUC) were analyzed. Results. Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was discriminant for all fragile fractures (OR = 2.64; AUC = 0.74), for Vertebral fracture (OR = 3.02; AUC = 0.77), for non-vertebral fracture (OR = 2.01; AUC = 0.69). MResNet showed comparable performance to that of BMD of hip and lumbar with all types of fractures, and significantly better performance than SOS all types of fractures. Conclusions. the MResNet model based on the ultrasonic RF signal can significantly improve the ability of QUS device to recognize previous fragile fractures. Moreover, the performance of the proposed model modified by age, weight, and height is further optimized. These results open perspectives to evaluate the risk of fragile fracture applying a deep learning model to analyze ultrasonic RF signal

Aims. Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. Methods. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis. Results. Overall, 49 patients underwent extra-articular excision. The area under the curve (AUC) ranged from 0.65 to 0.76 for direct signs of joint invasion, and was 0.83 for all three combined. In all, 26 patients had only one to two direct signs of invasion, representing an equivocal result. In these patients, the AUC was 0.63 for joint effusion and 0.85 for synovial thickening. When direct signs and synovial thickening were combined, the AUC was 0.89. Conclusion. MRI provides excellent discrimination for determining intra-articular tumour extension when multiple direct signs of invasion are present. When MRI results are equivocal, assessment of synovial thickening increases MRI’s discriminatory ability to predict intra-articular joint extension. These results should be interpreted in the context of the study’s limitations. The inclusion of only extra-articular excisions enriched the sample for true positive cases. Direct signs likely varied with tumour histology and location. A larger, prospective study of periarticular bone sarcomas with spatial correlation of histological and radiological findings is needed to validate these results before their adoption in clinical practice. Cite this article: Bone Jt Open 2024;5(10):944–952

Introduction. The Comprehensive Care for Joint Replacement (CJR) model for total hip arthroplasty (THA) involves a target reimbursement set by the Center for Medicare and Medicaid Services (CMS). Many patients exceed these targets, but predicting risk for incurring these excess costs remains challenging, and we hypothesized that select patient characteristics would adequately predict CJR cost overruns. Methods. Demographic factors and comorbidities were retrospectively reviewed in 863 primary unilateral CJR THAs performed between 2013 and 2017 at a single institution. A predictive model was built from 31 validated comorbidities and a base set of 5 patient factors (age, gender, BMI, ASA, marital status). A multivariable logistic regression model was refined to include only parameters predictive of exceeding the target reimbursement level. These were then assigned weights relative to the weakest parameter in the model. Results. The overall cost of care for 225 patients (26.1%) exceeded the target price, and a comprehensive model containing all 36 parameters demonstrated adequate discrimination (AUC: 0.748). This model was narrowed to 12 parameters retained for their statistical value in predicting excess cost, without substantial loss of predictive ability (AUC: 0.735). A single score formed from the sum of each patient's weighted parameters also showed adequate discrimination (AUC: .732), with predicted risk for exceeding CJR targets ranging from 10% for a patient score of 10 to 80% for a score of 30. Average scores for patients exceeding the target price were significantly higher than those who did not (19.5 vs 15.0, p < 0.0001). Conclusions. A model composed of weighted comorbidities and base demographics provides adequate discrimination in predicting whether THA costs will exceed CJR targets. This not only helps identify patients who may benefit from further pre-operative optimization, but also allows health systems to predict the likely minimum incurred costs for select patients scheduled for surgery

The postoperative course of median nerve decompression in the carpal tunnel syndrome may sometimes be complicated by postoperative pain, paresthesias, and other unpleasant symptoms, or be characterized by a slow recovery of nerve function due to prolonged preoperative injury causing extensive nerve damage. The aim of this study is to explore any possible effects of alpha lipoic acid (ALA) in the postoperative period after surgical decompression of the median nerve at the wrist. Patients were enrolled with proven carpal tunnel syndrome and randomly assigned into one of two groups: Group A: surgical decompression of the median nerve followed by ALA for 40 days. Group P: surgical decompression followed by placebo. The primary endpoint of the study was nerve conduction velocity at 3 months post surgery, Other endpoints were static 2 point discrimination, the Boston score for hand function, pillar pain and use of pain killers beyond the second postoperative day. ALA did not show to significantly improve nerve conduction velocity or Boston score. However, a statistically significant reduction in the postoperative incidence of pillar pain was noted in Group A. In addition, static 2 point discrimination showed to be significantly improved by ALA. Administration of ALA following decompression of the median nerve for carpal tunnel release is effective on nerve recovery, although this is not detectable through nerve conduction studies but in terms of accelerated and improved static two-point discrimination. The use of ALA as a supplementation for nerve recovery after surgical decompression may be extended to all types of compression syndromes or conditions where a nerve is freed from a mechanical insult. Furthermore, ALA limits post-decompression pain, including late pericicatricial pain at the base of the palm, the so called pillar pain, which seems to be associated with a reversible damage to the superfical sensitive small nerve fibers. In conclusion postoperative administration of ALA for 40 days post-median nerve decompression was positively associated with nerve recovery, induced a lower incidence of postoperative pillar pain and was associated with a more rapid improvement of static two-point discrimination. This treatment is well tolerated and associated with high levels of satisfaction and compliance, supporting its value as a standard postoperative supplementation after carpal tunnel decompression