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

Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation studies have shown mixed results. We present the development, validation, and comparative evaluation of novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery. Data was gathered retrospectively from medical records of patients who underwent spine surgery at Waikato Hospital between January 2019 and December 2020 (n = 488). Variables were selected a priori based on previous evidence and clinical judgement. Postoperative adverse events were classified objectively using the Comprehensive Complication Index. Models were constructed for the occurrence of any complication and significant complications (based on CCI >26). Performance and clinical utility of the novel model was compared against SpineSage (. https://depts.washington.edu/spinersk/. ), an extant online tool which we have shown in unpublished work to be valid in our local population. Overall complication rate was 34%. In the multivariate model, higher age, increased surgical invasiveness and the presence of preoperative anemia were most strongly predictive of any postoperative complication (OR = 1.03, 1.09, 2.1 respectively, p <0.001), whereas the occurrence of a major postoperative complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, p <0.001). Internal validation using the bootstrapped models showed the model was robust, with an AUC of 0.73. Using sensitivity analysis, 80% of the model's predictions were correct. By comparison SpineSage had an AUC of 0.71, and in decision curve analysis the novel model showed greater expected benefit at all thresholds of risk. NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their prognosis. Use of an objective tool may help to provide uniformity between DHBs when completing the “clinician assessment of risk” section of the national prioritization tool

Over 300,000 total hip arthroplasties (THA) are performed annually in the USA. Surgical Site Infections (SSI) are one of the most common complications and are associated with increased morbidity, mortality and cost. Risk factors for SSI include obesity, diabetes and smoking, but few studies have reported on the predictive value of pre-operative blood markers for SSI. The purpose of this study was to create a clinical prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of THA based on commonly ordered pre-operative lab markers and using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral THA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was used to determine coefficients, with manual stepwise reduction. Receiver Operating Characteristic (ROC) curves were also graphed. The SSI prediction model included the following covariates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphate, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. Since the data met logistic assumption requirements, bootstrap estimation was used to measure internal validity. The area under the ROC curve for final derivations along with McFadden's R-squared were utilized to compare prediction models. A total of 130,619 patients were included with the median age of patients at time of THA was 67 years (mean=66.6+11.6 years) with 44.8% (n=58,757) being male. A total of 1,561 (1.20%) patients had a superficial or deep SSI (overall SSI). Of all SSI, 45.1% (n=704) had a deep SSI and 55.4% (n=865) had a superficial SSI. The incidence of SSI occurring annually decreased from 1.44% in 2011 to 1.16% in 2016. Area under the ROC curve for the SSI prediction model was 0.79 and 0.78 for deep and superficial SSI, respectively and 0.71 for overall SSI. CHF had the largest effect size (Odds Ratio(OR)=2.88, 95% Confidence Interval (95%CI): 1.56 – 5.32) for overall SSI risk. Albumin (OR=0.44, 95% CI: 0.37 – 0.52, OR=0.31, 95% CI: 0.25 – 0.39, OR=0.48, 95% CI: 0.41 – 0.58) and sodium (OR=0.95, 95% CI: 0.93 – 0.97, OR=0.94, 95% CI: 0.91 – 0.97, OR=0.95, 95% CI: 0.93 – 0.98) levels were consistently significant in all clinical prediction models for superficial, deep and overall SSI, respectively. In terms of pre-operative blood markers, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. In this large NSQIP database study, we were able to create an SSI prediction model and identify risk factors for predicting acute superficial, deep and overall SSI after THA. To our knowledge, this is the first clinical model whereby pre-operative hyponatremia (in addition to hypoalbuminemia) levels have been predictive of SSI after THA. Although the model remains without external validation, it is a vital starting point for developing a risk prediction model for SSI and can help physicians mitigate risk factors for acute SSI post THA

Abstract. Introduction. Minimising postoperative complications and mortality in COVID-19 patients who were undergoing trauma and orthopaedic surgeries is an international priority. Aim was to develop a predictive nomogram for 30-day morbidity/mortality of COVID-19 infection in patients who underwent orthopaedic and trauma surgery during the coronavirus pandemic in the UK in 2020 compared to a similar period in 2019. Secondary objective was to compare between patients with positive PCR test and those with negative test. Methods. Retrospective multi-center study including 50 hospitals. Patients with suspicion of SARS-CoV-2 infection who had underwent orthopaedic or trauma surgery for any indication during the 2020 pandemic were enrolled in the study (2525 patients). We analysed cases performed on orthopaedic and trauma operative lists in 2019 for comparison (4417). Multivariable Logistic Regression analysis was performed to assess the possible predictors of a fatal outcome. A nomogram was developed with the possible predictors and total point were calculated. Results. Of the 2525 patients admitted for suspicion of COVID-19, 658 patients had negative preoperative test, 151 with positive test and 1716 with unknown preoperative COVID-19 status. Preoperative COVID-19 status, sex, ASA grade, urgency and indication of surgery, use of torniquet, grade of operating surgeon and some comorbidities were independent risk factors associated with 30-day complications/mortality. The 2020 nomogram model exhibited moderate prediction ability. In contrast, the prediction ability of total points of 2019 nomogram model was excellent. Conclusions. Nomograms can be used by orthopaedic and trauma surgeons as a practical and effective tool in postoperative complications and mortality risk estimation

Total knee arthroplasty (TKA) is the most commonly performed elective orthopaedic procedure. With an increasingly aging population, the number of TKAs performed is expected to be ∼2,900 per 100,000 by 2050. Surgical Site Infections (SSI) after TKA can have significant morbidity and mortality. The purpose of this study was to construct a risk prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of a TKA based on commonly ordered pre-operative blood markers and using audited administrative data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral TKA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was conducted to estimate coefficients, with manual stepwise reduction to construct models. Bootstrap estimation was administered to measure internal validity. The SSI prediction model included the following co-variates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphatase, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. To compare clinical models, areas under the receiver operating characteristic (ROC) curves and McFadden's R-squared values were reported. The total number of patients undergoing TKA were 210,524 with a median age of 67 years (mean age of 66.6 + 9.6 years) and the majority being females (61.9%, N=130,314). A total of 1,674 patients (0.8%) had a SSI within 30 days of the index TKA, of which N=546 patients (33.2%) had a deep SSI and N=1,128 patients (67.4%) had a superficial SSI. The annual incidence rate of overall SSI decreased from 1.60% in 2011 to 0.68% in 2016. The final risk prediction model for SSI contained, smoking (OR=1.69, 95% CI: 1.31 – 2.18), previous/current steroid use (OR=1.66, 95% CI: 1.23 – 2.23), as well as the pre-operative lab markers, albumin (OR=0.46, 95% CI: 0.37 – 0.56), blood urea nitrogen (BUN, OR=1.01, 95% CI: 1 – 1.02), international normalized ratio (INR, OR=1.22, 95% CI:1.05 – 1.41), and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98;). Area under the ROC curve for the final model of overall SSI was 0.64. Models for deep and superficial SSI had ROC areas of 0.68 and 0.63, respectively. Albumin (OR=0.46, 95% CI: 0.37 – 0.56, OR=0.33, 95% CI: 0.27 – 0.40, OR=0.75, 95% CI: 0.59 – 0.95) and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98, OR=0.96, 95% CI: 0.93 – 0.99, OR=0.97, 95% CI: 0.96 – 0.99) levels were consistently significant in all prediction models for superficial, deep and overall SSI, respectively. Overall, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. To our knowledge, this is the first prediction model for acute SSI post TKA whereby hyponatremia (and hypoalbuminemia) are predictive of SSI. This prediction model can help fill an important gap for predicting risk factors for SSI after TKA and can help physicians better optimize patients prior to TKA

Introduction. Diaphyseal tibial fractures account for approximately 1.9% of adult fractures. Several studies demonstrate a high proportion of diaphyseal tibial fractures have ipsilateral occult posterior malleolus fractures, this ranges from 22–92.3%. Materials and Methods. A retrospective review of a prospectively collected database was performed at Liverpool University Hospitals NHS Foundation Trust between 1/1/2013 and 9/11/2020. The inclusion criteria were patients over 16, with a diaphyseal tibial fracture and who underwent a CT. The articular fracture extension was categorised into either posterior malleolar (PM) or other fracture. Results. 764 fractures were analysed, 300 had a CT. There were 127 intra-articular fractures. 83 (65.4%) cases were PM and 44 were other fractures. On univariate analysis for PM fractures, fibular spiral (p=.016) fractures, no fibular fracture(p=.003), lateral direction of the tibial fracture (p=.04), female gender (p=.002), AO 42B1 (p=.033) and an increasing angle of tibial fracture. On multivariate regression analysis a high angle of tibia fracture was significant. Other fracture extensions were associated with no fibular fracture (p=.002), medial direction of tibia fracture (p=.004), female gender (p=.000), and AO 42A1 (p=.004), 42A2 (p=.029), 42B3 (p=.035) and 42C2 (p=.032). On multivariate analysis, the lateral direction of tibia fracture, and AO classification 42A1 and 42A2 were significant. Conclusions. Articular extension happened in 42.3%. A number of factors were associated with the extension, however multivariate analysis did not create a suitable prediction model. Nevertheless, rotational tibia fractures with a high angle of fracture should have further investigation with a CT

Background. Stability of total knee arthroplasty (TKA) is dependent on correct and precise rotation of the femoral component. Multiple differing surgical techniques are currently utilized to perform total knee arthroplasty. Accurate implant position have been cited as the most important factors of successful TKA. There are two techniques of achieving soft gap balancing in TKA; a measured resection technique and a balanced gap technique. Debate still exists on the choice of surgical technique to achieve the optimal soft tissue balance with opinions divided between the measured resection technique and the gap balance technique. In the measured resection technique, the bone resection depends on size of the prosthesis and is referenced to fixed anatomical landmarks. This technique however may have accompanying problems in imbalanced patients. Prediction of gap balancing technique, tries to overcome these fallacies. Our aim in this study was twofold: 1) To describe our methodology of ROBOTIC TKA using prediction of gap balancing technique. 2) To analyze the clinico-radiological outcome our technique comparison of meseaured resection ROBOTIC TKA after 1year. Methods. Patients that underwent primary TKA using a robotic system were included for this study. Only patients with a diagnosis of primary degenerative osteoarthritis with varus deformity and flexion deformity of were included in this study. Patients with valgus deformity, secondary arthritis, inflammatory arthritis, and severe varus/flexion deformity were excluded. Three hundred ten patients (319 knees) who underwent ROBOTIC TKA using measured resection technique from 2004 – 2009. Two hundred twenty (212 knees) who underwent ROBOTIC TKA using prediction of gap balancing technique from 2010 – 2012. Clinical outcomes including KS and WOMAC scores, and ranges of motion and radiological outcomes including mechanical axis, prosthesis alignments, flexion varus/valgus stabilities were compared after 1year. Results. Leg mechanical axes were significantly different at follow-up 1year versus preoperative values, the mean axes in the Robotic-TKA with measured resection technique and Robotic-TKA with prediction of gap balancing technique improved from 9.6±5.0° of varus to 0.5±1.9° of varus, and from 10.6±5.5° to 0.4±1.3° of varus (p<0.001), respectively. However, no significant intergroup differences were found between mechanical axis or coronal alignments of femoral or tibial prostheses (pï¼ï¿½0.05). Mean varus laxities at 90° of knee flexion in measured resection and gap prediction technique group were 6.4° and 5.3°, respectively, and valgus laxities were 6.2 and 5.2 degrees, respectively, with statistical significance (p=0.045 and 0.032, respectively). KS knee and function scores and WOMAC scores were significantly improved at follow-up 1year (pï¼ï¿½0.05). However, no significant difference was found between the Robotic-TKA with measured resection technique and Robotic-TKA with prediction of gap balancing technique for any clinical outcome parameter at follow-up 1year (pï¼ï¿½0.05). Conclusions. Robotic assisted TKA using measured resection or gap prediction technique provide adequate and practically identical levels of flexion stability at 90° of knee flexion with accurate leg and prosthesis alignment. But, Robotic TKA using measured resection technique have less than flexion stability compared with gap prediction technique with statistical significance after follow-up 1year

Background. Total knee arthroplasty (TKA) is a proven and cost-effective treatment for osteoarthritis. Despite the good to excellent long-term results, some patients remain dissatisfied. Our study aimed at establishing a predictive model to aid patient selection and decision-making in TKA. Methods. Using data from our prospective arthroplasty outcome database, 113 patients were included. Pre- and postoperatively, the patients completed 107 questions in 5 questionnaires: KOOS, OKS, PCS, EQ-5D and KSS. First, outcome parameters were compared between the satisfied and dissatisfied group. Secondly, we developed a new prediction tool using regression analysis. Each outcome score was analysed with simple regression. Subsequently, the predictive weight of individual questions was evaluated applying multiple linear regression. Finally, 10 questions were retained to construct a new prediction tool. Results. Overall satisfaction rate in this study was found to be 88%. We identified a significant difference between the satisfied and dissatisfied group when looking at the preoperative questionnaires. Dissatisfied patients had more preoperative symptoms (such as stiffness), less pain and a lower QOL. They were more likely to ruminate and had a lower preoperative KSS satisfaction score. The developed prediction tool consists of 10 simple, but robust questions. Sensitivity was 97% with a positive predictive value of 93%. Conclusions. Based upon preoperative parameters, we were able to partially predict satisfaction and dissatisfaction after TKA. After further validation this new prediction tool for patient satisfaction following TKA may allow surgeons and patients to evaluate the risks and benefits of surgery on an individual basis and help in patient selection

Introduction. Up to 60% of total hip arthroplasties (THA) in Asian populations arise from avascular necrosis (AVN), a bone disease that can lead to femoral head collapse. Current diagnostic methods to classify AVN have poor reproducibility and are not reliable in assessing the fracture risk. Femoral heads with an immediate fracture risk should be treated with a THA, conservative treatments are only successful in some cases and cause unnecessary patient suffering if used inappropriately. There is potential to improve the assessment of the fracture risk by using a combination of density-calibrated computed tomographic (QCT) imaging and engineering beam theory. The aim of this study was to validate the novel fracture prediction method against in-vitro compression tests on a series of six human femur specimens. Methods. Six femoral heads from six subjects were tested, a subset (n=3) included a hole drilled into the subchondral area of the femoral head via the femoral neck (University of Leeds, ethical approval MEEC13-002). The simulated lesions provided a method to validate the fracture prediction model with respect of AVN. The femoral heads were then modelled by a beam loaded with a single joint contact load. Material properties were assigned to the beam model from QCT-scans by using a density-modulus relationship. The maximum joint loading at which each bone cross-section was likely to fracture was calculated using a strain based failure criterion. Based on the predicted fracture loads, all six femoral heads (validation set) were classified into two groups, high fracture risk and low fracture risk (Figure 1). Beam theory did not allow for an accurate fracture load to be found because of the geometry of the femoral head. Therefore the predicted fracture loads of each of the six femoral heads was compared to the mean fracture load from twelve previously analysed human femoral heads (reference set) without lesions. The six cemented femurs were compression tested until failure. The subjects with a higher fracture risk were identified using both the experimental and beam tool outputs. Results. The computational tool correctly identified all femoral head samples which fractured at a significantly low load in-vitro (Figure 2). Both samples with a low experimental fracture load had an induced lesion in the subchondral area (Figure 3). Discussion. This study confirmed findings of a previous verification study on a disease models made from porcine femoral heads (Preutenborbeck et al. I-CORS2016). It demonstrated that fracture prediction based on beam theory is a viable tool to predict fracture. The tests confirmed that samples with a lesion in the weight bearing area were more likely to fracture at a low load however not all samples with a lesion fractured with a low load experimentally, indicating that a lesion alone is not a sufficient factor to predict fracture. The developed tool takes both structural and material properties into account when predicting the fracture risk. Therefore it might be superior to current diagnostic methods in this respect and it has the added advantage of being largely automated and therefore removing the majority of user bias. For any figures or tables, please contact the authors directly

In conventional DXA (Dual-energy X-ray Absorptiometry) analysis, pixel bone mineral density (BMD) is often averaged at the femoral neck. Neck BMD constitutes the basis for osteoporosis diagnosis and fracture risk assessment. This data averaging, however, limits our understanding of localised spatial BMD patterns that could potentially enhance fracture prediction. DXA region free analysis (RFA) is a validated toolkit for pixel-level BMD analysis. We have previously deployed this toolkit to develop a spatio-temporal atlas of BMD ageing in the femur. This study aims first to introduce bone age to reflect the overall bone structural evolution with ageing, and second to quantify fracture-specific patterns in the femur. The study dataset comprised 4933 femoral DXA scans from White British women aged 75 years or older. The total number of fractures was 684, of which 178 were reported at the hip within a follow-up period of five years. BMD maps were computed using the RFA toolkit. For each BMD map, bone age was defined as the age for which the L2-norm between the map and the median atlas at that age is minimised. Next, bone maps were normalised for the estimated bone age. A t-test followed by false discovery rate (FDR) analysis was applied to compare between fracture and non-fracture groups. Excluding the ageing effect revealed subtle localised patterns of loss in BMD oriented in the same direction as principal tensile curves. A new score called f-score was defined by averaging the normalised pixel BMD values over the region with FDR q-value less than 1e–6. The area under the curve (AUC) was 0.731 (95% confidence interval (CI)=0.689–0.761) and 0.736 (95% CI=0.694–0.769) for neck BMD and f-score. Combining bone age and f-score improved the AUC significantly by 3% (AUC=0.761, 95% CI=0.756–0.768) over the neck BMD alone (AUC=0.731, 95% CI=0.726–0.737). This technique shows promise in characterizing spatially-complex BMD changes, for which the conventional region-based technique is insensitive. DXA RFA shows promise to further improve fracture prediction using spatial BMD distribution

Fractures through the physis account for 18–30% of all paediatric fractures, leading to growth arrest in 5.5% of cases. We have limited knowledge to predict which physeal fractures result in growth arrest and subsequent deformity or limb length discrepancy. The purpose of this study is to identify factors associated with physeal growth arrest to improve patient outcomes. This prospective cohort study was designed to develop a clinical prediction model for growth arrest after physeal injury. Patients < 1 8 years old presenting within four weeks of injury were enrolled if they had open physes and sustained a physeal fracture of the humerus, radius, ulna, femur, tibia or fibula. Patients with prior history of same-site fracture or a condition known to alter bone growth or healing were excluded. Demographic data, potential prognostic indicators and radiographic data were collected at baseline, one and two years post-injury. A total of 167 patients had at least one year of follow-up. Average age at injury was 10.4 years, 95% CI [9.8,10.94]. Reduction was required in 51% of cases. Right-sided (52.5%) and distal (90.1%) fractures were most common. After initial reduction 52.5% of fractures had some form of residual angulation and/or displacement (38.5% had both). At one year follow-up, 34 patients (21.1%) had evidence of a bony bridge on plain radiograph, 10 (6.2%) had residual angulation (average 12.6°) and three had residual displacement. Initial angulation (average 22.4°) and displacement (average 5.8mm) were seen in 16/34 patients with bony bridge (48.5%), with 10 (30.3%) both angulated and displaced. Salter-Harris type II fractures were most common across all patients (70.4%) and in those with bony bridges (57.6%). At one year, 44 (27.3%) patients had evidence of closing/closed physes. At one year follow-up, there was evidence of a bony bridge across the physis in 21.1% of patients on plain film, and residual angulation and/or displacement in 8.1%. Initial angulation and/or displacement was present in 64.7% of patients showing possible evidence of growth arrest. The incidence of growth arrest in this patient population appears higher than past literature reports. However, plain film is an unreliable modality for assessing physeal bars and the true incidence may be lower. A number of patients were approaching skeletal maturity at time of injury and any growth arrest is likely to have less clinical significance in these cases. Further prospective long-term follow-up is required to determine the true incidence and impact of growth arrest

Introduction. Integrating additively manufactured structures, such as porous lattices into implants has numerous potential benefits, such as custom mechanical properties, porosity for osseointegration/fluid flow as well as improved fixation features. Component anisotropic stiffness can be controlled through varying density and lattice orientation. This is useful due to the influence of load on bone remodelling. Matching implant and bone anisotropy/stiffness may help reduce problems such as stress shielding and prevent implant loosening. It is therefore beneficial to be able to design AM parts with a desired anisotropic stiffness. In this study we present a method that predicts the anisotropic stiffness of an additively manufactured lattice structure from its CAD data, and validate this model with experimental testing. The model predicts anisotropic stiffness in terms of density (ρ), fabric (M) and fabric eigen values (m) and is matched to stiffness data of the structure in 3 principal directions, based on an orthotropic assumption. This model was described in terms of 10 constants and had the form shown in Equation 1. Eq.1. S. =. ∑. i. ,. j. =. 1.  .  .  .  . i. ,. j. =. 3. λ. (. i. ,. j. ). ρ. k. m. (. i. ). 1. (. i. ). m. (. j. ). 1. (. i. ). |. M. i. M. j. '. |. 2. Methods. A stochastic line structure was formed in CAD by joining pseudo-random points generated using the Poisson-disk method Lines at an angle lower than 30° to the x-y plane removed to allow for AM manufacturing. Lines were converted to struts with 330 µm diameter. Second order fabric tensors were determined from CAD files of the AM specimens using the mean intercept length (MIL), the gold standard for determining a measure of the ‘average orientation’ of material within trabecular bone structures. 10 × 10 × 12 mm specimens of the CAD model were manufactured on a Renishaw AM250 powder bed fusion machine. The structure was built in 10 different orientations to enable stiffness measurement in 10 different directions (n=5 for each direction). Compression testing in a servohydraulic materials testing machine was performed according to ISO13314 with LVDTs used to measure displacement to remove compliance effects. Stress-strain curves were obtained and elastic moduli were estimated from a hysteresis loop in the load application, from 70% to 20% of the plateau stress. Specimen density and fabric data were fit to the observed stiffnesses using least squares linear regression. Experimental stiffnesses of the structure in 10 directions were compared to the model to evaluate the accuracy of model predictions. Results & Discussion. The model predicted the stiffness of the structure across all 10 orientations to within 13% absolute error compared to the observed stiffness data, with an R. 2. value of 0.969. The three dimensional stiffness plot formed by the model was similar to the experimental data, displaying an hourglass shape. Our model is the first to predict the anisotropic stiffness of stochastic structures and will be highly useful in predicting stiffness of lattice structures and could also be applied to bone to measure anisotropic stiffness. For any figures or tables, please contact authors directly

Introduction:. Primary stability is crucial for long-term fixation of cementless tibial trays. Micromotion less than 50 μm is associated with stable bone ingrowth and greater than 150 μm causes the formation of fibrous tissue around the implant [1, 2]. Finite element (FE) analysis of complete activities of daily living (ADL's) have been used to assess primary stability, but these are computationally expensive. There is an increasing need to account for both patient and surgical variability when assessing the performance of total joint replacement. As a consequence, an implant should be evaluated over a spectrum of load cases. An alternative approach to running multiple FE models, is to perform a series of analyses and train a surrogate model which can then be used to predict micromotion in a fraction of the time. Surrogate models have been used to predict single metrics, such as peak micromotion. The aim of this work is to train a surrogate model capable of predicting micromotion over the entire bone-implant interface. Methods:. A FE model of an implanted proximal tibia was analysed [3] (Fig. 1). A statistical model of knee kinetics, incorporating subject-specific variability in all 6-DOF joint loads [4], was used to randomly generate loading profiles for 50 gait cycles. A Latin Hypercube (LH) sampling method was applied to sample 6-DOF loads of the new population throughout the gait cycle. Kinetic data was sampled at 10, 50 and 100 instances and FE predictions of micromotion were calculated and used to train a surrogate model capable of describing micromotion over the entire bone-implant interface. The surrogate model was tested for an unseen gait cycle and the resulting micromotions were compared with FE predictions. Results and discussion:. Accuracy of the surrogate model increased with increasing sample size in the training set; with a LH sample of 10, 50 and 100 trials, the surrogate model predicted micromotion at the bone-implant interface during gait with RMS accuracy of 61, 44 and 33 μm, respectively (Fig. 2). Similar range in micromotion was measured in FE and surrogate models; although the surrogate model tended to over-predict micromotion early in the gait cycle (Fig. 2). There was good agreement in location and magnitude of micromotion at the interface surface through out the gait cycle (Fig. 3). Although encouraging, further work is required to optimize the number and distribution of the training samples to minimize the error in the surrogate model. Analysis time for the FE model was 15 hours, compared to 30 seconds for the surrogate model. The results suggest that surrogate models have significant potential to rapidly predict micromotion over the entire bone-implant interface, allowing for a greater range in loading conditions to be explored than would be possible through conventional methods

Introduction. From 2004 to 2015, elective lumbar fusions increased by 62% in the US. The largest increases were for among age 65 or older (139% in volume) and scoliosis (187%) [1]. Age is a well known factor of osteoporosis. The load-sharing may exceed the pedicular screws constructs in aging spine and lead to non-union and re-do. Surgical options may increase the screw purchase (e.g.: augmentation, extensions) at supplementary risks. Pedicular screw are known to cause vascular, nerve root or cord injuries. Facing these pitfalls, the surgeon's experience and rule of thumbs are the most deciding factors for the surgical planning. The aim of this study is to assess the accuracy of a patient specific tool, designed to plan a safe pedicular trajectory and to provide an intraoperative screw pullout strength estimate. Materials and Methods. Clinical QCT were taken for nine cadaveric spines (82 y. [61; 87], 6 females, 3 males). The experimental maximum axial pullout resistance (FMax) of twenty-seven pedicular screws inserted (nine T12, nine L4 and nine L5) was obtained as described in a previous study [2]. A custom 3D-WYSIWYG software simulated a medio-lateral surgical insertion technique in the QCTs coordinates reference, respecting the cortical walls. Repeatable density, morphometric and hardware parameters were recorded for each vertebrae. A statistical model was built to match predictive and experimental data. Preliminary results. Experimental FMax(N) were [104;953] (359 ±223). A further displacement of 1,81mm ±0,35 halved the experimental FMax. Predictive FMax(N) were [142;862] (359 ±220). A high positive correlation between experimental and predictive FMax was revealed (Pearson, ρ = 0.93, R2 = 0.87, p < .001, figure 1). Absolute differences ranged between 3N and 177N. Discussion. A high screw purchase in primary fixation is paramount to achieve spine surgical procedures (e.g.: kyphosis, scoliosis) and postoperative stability for vertebrae fusion. High losses of screw purchase by bone plastic deformation, begin with tiny pullouts. Theses unwanted intraoperative millimetric over-displacements are hard to avoid when monitoring at the same time tens of screws surrounded by bleedings. This advocates for including predictive FMax for each implantable pedicular screw in the surgical planning decision making process to prevent failures and assess risks. For the first time, this study presents an experimentally validated statistical model for FMax prediction with a safe trajectory definition tool, including patients’ vertebrae and hardware properties and referring to the patient's clinical 3D quantitative imagery. The model was able to differentiate between bone quality and vertebrae variations. More extensive model validation is currently ongoing to interface with robotics & navigation systems and to produce meshes for 3D printing of sterilizable insertion guides

Introduction. Preoperative templating of femoral and tibial components can assist in choosing the appropriate implant size prior to TKA. While weight bearing long limb roentograms have been shown to provide benefit to the surgeon in assessing alignment, disease state, and previous pathology or trauma, their accuracy in size prediction is continually debated due to scaling factors and rotated views. Further, they represent a static time point, accounting for boney anatomy only. A perceived benefit of robotic-assisted surgery is the ability to pre-operatively select component sizes with greater accuracy based on 3D information, however, to allow for flexibility in refining based on additional data only available at the time of surgery. Methods. The purpose of this study was to determine the difference of pre-operative plans in size prediction of the tibia, femur, and polyethylene insert. Eighty four cases were enrolled at three centers as part of an Investigational Device Exemption to evaluate a robotic-assisted TKA. All patients had a CT scan as part of a pre-operative planning protocol. Scans were segmented and implant sizes predicted based on the patients boney morphology and an estimated 2mm cartilage presence. Additional information such as actual cartilage presence and soft tissue effects on balance and kinematics were recorded intra-operatively. Utilizing this additional information, surgical plans were fine tuned if necessary to achieve minimal insert thickness and balance. Data from the Preoperative CT plan sizing and final size were compared to determine the percentage of size and within one size accuracy. Results. The pre-operative plan was able to determine the femoral and tibial components within one size for 100% of cases. Intra-operatively, surgeon upsized femoral 15 out of 85 (18%), downsized femoral 1 out of 85 (1%), baseplate 13 out of 85 (15%), and downsized baseplate 4 out of 85 (5%). Polyethylene exact size could be planned 93% of the time. Discussion/Conclusion. Robotic-assisted pre-operative CT based planning was accurate over 70% of the time for the femur and tibial components, and over 90% with respect to the insert thickness Additionally, intraoperative information allowed for adjustments to provide patients with ideal coverage of articular surfaces and for joint balancing providing optimal individualized component placement. Further research is needed to determine the potential cost savings in hospital and OR inventory management

The primary objectives of this study were to: 1) identify risk factors for subsequent surgery following initial treatment of proximal humerus fractures, stratified by initial treatment type; 2) generate risk prediction tools to predict subsequent shoulder surgery following initial treatment; and 3) internally validate the discriminative ability of each tool. We identified patients ≥ 50 years with a diagnosis of proximal humerus fracture from 2004 to 2015 using linkable health datasets in Ontario, Canada. We used procedural and fee codes within 30 days of the index fracture to classify patients into treatment groups: 1) surgical fixation; 2) shoulder replacement; and 3) conservative. We used intervention and diagnosis codes to identify all instances of complication-related subsequent shoulder surgery following initial treatment within two years post fracture. We developed logistic regression models for randomly selected two thirds of each treatment group to evaluate the association of patient, fracture, surgical, and hospital variables on the odds of subsequent shoulder surgery following initial treatment. We used regression coefficients to compute points associated with each of the variables within each category, and calculated the risk associated with each point total using the regression equation. We used the final third of each cohort to evaluate the discriminative ability of the developed risk tools (via the continuous point total and a dichotomous point cut-off value for “higher” vs. “lower” risk determined by Receiver Operating Curves) using c-statistics. We identified 20,897 patients with proximal humerus fractures that fit our inclusion criteria for analysis, 2,414 treated with fixation, 1,065 treated with replacement, and 17,418 treated conservatively. The proportions of patients who underwent subsequent shoulder surgery within two years were 13.8%, 5.1%, and 1.3%, for fixation, replacement, and conservative groups, respectively. Predictors of reoperation following fixation included the use of a bone graft, and fixation with a nail or wire vs. a plate. The only significant predictor of reoperation following replacement was poor bone quality. The only predictor of subsequent shoulder surgery following conservative treatment was more comorbidities while patients aged 70+, and those discharged home following initial presentation (vs. admitted or transferred to another facility) had lower odds of subsequent shoulder surgery. The risk tools developed were able to discriminate between patients who did or did not undergo subsequent shoulder surgery in the derivation cohorts with c-statistics of 0.75–0.88 (continuous point total), and 0.82–0.88 (dichotomous cut-off), and 0.53–0.78 (continuous point total) and 0.51–0.79 (dichotomous cut-off) in the validation cohorts. Our results present potential factors associated with subsequent shoulder surgery following initial treatment of proximal humerus fractures, stratified by treatment type. Our developed risk tools showed good to strong discriminative ability in both the derivation and validation cohorts for patients treated with fixation, and conservatively. This indicates that the tools may be useful for clinicians and researchers. Future research is required to develop risk tools that incorporate clinical variables such as functional demands

Introduction. Despite generally excellent patient outcomes for Total Knee Arthroplasty (TKA), there remains a contingent of patients, up to 20%, who are not satisfied with the outcome of their procedure. (Beswick, 2012) There has been a large amount of research into identifying the factors driving these poor patient outcomes, with increasing recognition of the role of non-surgical factors in predicting achieved outcomes. However, most of this research has been based on single database or registry sources and so has inherited the limitations of its source data. The aim of this work is to develop a predictive model that uses expert knowledge modelling in conjunction with data sources to build a predictive model of TKR patient outcomes. Method. The preliminary Bayesian Belief Network (BBN) developed and presented here uses data from the Osteoarthritis Initiative, a National Institute of Health funded observational study targeting improved diagnosis and monitoring of osteoarthritis. From this data set, a pared down subset of patient outcome relevant preoperative questionnaire sets has been extracted. The BBN structure provides a flexible platform that handles missing data and varying data collection preferences between surgeons, in addition to temporally updating its predictions as the patient progresses through pre and postoperative milestones in their recovery. In addition, data collected using wearable activity monitoring devices has been integrated. An expert knowledge modelling process relying on the experience of the practicing surgical authors has been used to handle missing cross-correlation observations between the two sources of data. Results. The model presented here has been internally cross validated and has some interesting facets, including the strongest single predictive question of bad outcome for the patient being the presence of lower back pain. Clinical implementation and long term predictive accuracy result collection is ongoing. Discussion. Unsatisfied patients represent a significant minority of TKR recipients, with multiple, multifaceted causal factors both in surgery and out implicated. Historically, focus has been on the role of management and improvement of the surgical factors, which is linked to the fact that surgical factors can often lead to far more disastrous consequences for the patient and the basic principle that “you only improve what you measure.” Growing collection of Patient Reported Outcome Measures by registries around the world has exposed the fact that management of patient factors has lagged behind. (Judge, 2012) Increasingly, the pivotal role of unmet expectations in determining patient satisfaction (Noble, 2006) and the “expectation gap” (Ghomrawi, 2012) between surgeons and patients has been exposed as an opportunity to improve patient outcomes. By developing a model that uses existing surgical expert knowledge to integrate research identified preoperative factors that can be accurately and practically gathered in a clinical setting, a workflow that manages patient expectations in order to optimize outcomes could reduce dissatisfaction rates in TKR recipients. Future work should focus on improving clinical integration and, in the absence of sufficiently wide, deep and complete patient response and predictor datasets, ways of harnessing existing expert knowledge into an evolving predictive tool of patient outcomes

This is quite an innovative study that should lead to a multicentre validation trial. We have developed an FDG-PET/MRI texture-based model for the prediction of lung metastases (LM) in newly diagnosed patients with soft-tissue sarcomas (STSs) using retrospective analysis. In this work, we assess the model performance using a new prospective STS cohort. We also investigate whether incorporating hypoxia and perfusion biomarkers derived from FMISO-PET and DCE-MRI scans can further enhance the predictive power of the model. A total of 66 patients with histologically confirmed STSs were used in this study and divided into two groups: a retrospective cohort of 51 patients (19 LM) used for training the model, and a prospective cohort of 15 patients (two patients with LM, one patient with bone metastases and suspicious lung nodules) for testing the model. In the training phase, a model of four texture features characterising tumour sub-region size and intensity heterogeneities was developed for LM prediction from pre-treatment FDG-PET and MRI scans (T1-weighted, T2-weighted with fat saturation) of the retrospective cohort, using imbalance-adjusted bootstrap statistical resampling and logistic regression multivariable modeling. In the testing phase, this multivariable model was applied to predict the distant metastasis status of the prospective cohort. The predictive power of the obtained model response was assessed using the area under the receiver-operating characteristic curve (AUC). In the exploratory phase of the study, we extracted two heterogeneity metrics from the prospective cohort: the area under the intensity-volume histogram of pre-treatment DCE-MRI volume transfer constant parametric maps and FMISO-PET hypoxia maps (AU-IVH-Ktrans, AU-IVH-FMISO). The impact of the addition of these two individual metrics to the texture-based model response obtained in the testing phase was first investigated using Spearman's correlation (rs), and lastly using logistic regression and leave-one-out cross-validation (LOO-CV) to account for overfitting bias. First, the texture-based model reached an AUC of 0.94, a sensitivity of 1, a specificity of 0.83 and an accuracy of 0.87 when tested in the prospective cohort. In the exploratory phase, the addition of AU-IVH-FMISO did not improve predictive power, yielding a correlation of rs = −0.42 (p = 0.12) with lung metastases, and a relative change in validation AUC of 0% in comparison with the texture-based model response alone in LOO-CV experiments. In contrast, the addition of AU-IVH-Ktrans improved predictive power, yielding a correlation of rs = −0.54 (p = 0.04) with lung metastases, and a change in validation AUC of +10%. Our results demonstrate that texture-based models extracted from pre-treatment FDG-PET and MRI anatomical scans could be successfully used to predict distant metastases in STS cancer. Our results also suggest that the addition of perfusion heterogeneity metrics may contribute to improving model prediction performance

Bone remodeling effects is a significant issue in predicting long term stability of hip arthroplasty. It has been frequently observed around the femoral components especially with the implantation of prosthesis stem. Presence of the stiffer materials into the femur has altering the stress distribution and induces changes in the architecture of the bone. Phenomenon of bone resorption and bone thickening are the common reaction in total hip arthroplasty (THA) which leading to stem loosening and instability. The objectives of this study are (i) to develop inhomogeneous model of lower limbs with hip osteoarthritis and THA and (ii) to predict the bone resorption behavior of lower limbs for both cases. Biomechanical evaluations of lower limbs are established using the finite element method in predicting bone remodeling process. Lower limbs CT-based data of 79 years old female with hip osteoarthritis (OA) are used in constructing three dimensional inhomogenous models. The FE model of lower limbs was consisted of sacrum, left and right ilium and both femur shaft. Bond between cartilage, acetabulum and femoral head, sacrum and ilium were assumed to be rigidly connected. The inhomogeneous material properties of the bone are determined from the Hounsfield unit of the CT image using commercial biomedical software. A load case of 60kg body weight was considered and fixed at the distal cut of femoral shaft. For THA lower limbs model, the left femur which suffering for hip OA was cut off and implanted with prosthesis stem. THA implant is designed to be Titanium alloy and Alumina for stem and femoral ball, respectively. Distribution of young modulus of cross-sectional inhomogeneous model is presented in Fig. 2 while model of THA lower limbs also shown in Fig. 2. Higher values of young modulus at the outer part indicate hard or cortical bone. Prediction of bone resorption is discussed with the respect of bone mineral density (BMD). Changes in BMD at initial age to 5 years projection were simulated for hip OA and THA lower limbs models. The results show different pattern of stress distribution and bone mineral density between hip OA lower limbs and THA lower limbs. Stress is defined to be dominant at prosthesis stem while femur experienced less stress and leading to bone resorption. Projection for 5 years follow up shows that the density around the greater tronchanter appears to decrease significantly

Background. Establishing the diagnosis in a child presenting with an atraumatic limp can be difficult. Clinical prediction algorithms have been devised to distinguish septic arthritis (SA) from transient synovitis (TS). Within Europe measurement of the Erythrocyte Sedimentation Rate (ESR) has largely been replaced with assessment of C-Reactive Protein (CRP) as an acute phase protein. We produce a prediction algorithm to determine the significance of CRP in distinguishing between TS and SA. Method. All children with a presentation of ‘atraumatic limp’ and a proven effusion on hip ultrasound between 2004 and 2009 were included. Patient demographics, details of the clinical presentation and laboratory investigations were documented to identify a response to each of the four variables (Weight bearing status, WCC >12,000 cells/m3, CRP >20mg/L and Temperature >38.5°C). SA was defined based upon culture and microscopy of the operative findings. Results. 311 hips were included within the study. Of these 282 were considered to have transient synovitis. 29 patients met criteria to be classified as SA based upon laboratory assessment of the synovial fluid. The introduction of CRP eliminated the need for a four variable model as the use of two variables (CRP and weight bearing status) had similar efficacy. Treating individuals who were non-weight-bearing and a CRP >20mg/L as SA correctly classified 94.8% individuals, with a sensitivity of 75.9%, specificity of 96.8%, positive predictive value of 71.0%, and negative predictive value of 97.5%. CRP was a significant independent predictor of septic arthritis. Conclusions. CRP was a strong independent risk factor of septic arthritis, and its inclusion within a regression model simplifies the diagnostic algorithm. Nevertheless, this and other models are generally more reliable in excluding SA, than confirming SA, and therefore a clinician's acumen remains important in identifying SA in those individuals with a single abnormal variable