Introduction

IM (Intra Medullary) nail fixation is the standard treatment for diaphyseal femur fractures and also for certain types of proximal and distal femur fractures. Despite the advances in the tribology for the same, cases of failed IM nail fixation continue to be encountered routinely in clinical practice. Common causes are poor alignment or reduction, insufficient fixation and eventual implant fatigue and failure. This study was devised to study such patients presenting to our practice and develop a predictive model for eventual failure.

Aim

Kingella kingae seems to be the most common cause of osteoarticular infections (OAI) in children under 48 months of age (1). Recent studies had shown that K. kingae is poorly susceptible to anti-staphylococcal penicillin and some isolates produce beta-lactamase (2). This led to the need for new treatment guidelines for OAI in populations in which K. kingae is frequent. Our study aimed to design a model which could predict K. kingae OAI in order to initiate appropriate empirical treatment on hospital admission.

Technology within medicine has great potential to bring about more accessible, efficient, and a higher quality delivery of care. Paediatric supracondylar fractures are the most common elbow fracture in children and at our institution often have high rates of unnecessary long term clinical follow-up, leading to an inefficient use of healthcare and patient resources. This study aims to evaluate patient and clinical factors that significantly predict necessity for further clinical visits following closed reduction and percutaneous pinning.

A total of 246 children who underwent closed reduction and percutaneous pinning following supracondylar humerus fractures were prospectively enrolled over a two year period. Patient demographics, perioperative course, goniometric measurements, functional outcome measures, clinical assessment and decision making for further follow up were assessed. Categorical and continuous variables were analyzed and screened for significance via bivariate regression. Significant covariates were used to develop a predictive model through multivariate logistical regression. A probability cut-off was determined on the Receiver Operator Characteristic (ROC) curve using the Youden index to maximize sensitivity and specificity. The regression model performance was then prospectively tested against 22 patients in a blind comparison to evaluate accuracy.

246 paediatrics patients were collected, with 29 cases requiring further follow up past the three month visit. Significant predictive factors for follow up were residual nerve palsy (p < 0 .001) and maximum active flexion angle of injured elbow (p < 0 .001). Insignificant factors included other goniometric measures, subjective evaluations, and functional outcomes scores. The probability of requiring further clinical follow up at the 3 month post-op point can be estimated with the equation: logit(follow-up) = 11.319 + 5.518(nerve palsy) − 0.108(maximum active flexion). Goodness of fit of the model was verified with Nagelkerke R2 = 0.574 and Hosmer & Lemeshow chi-square (p = 0.739). Area Under Curve of the ROC curve was C = 0.919 (SE = 0.035, 95% CI 0.850 – 0.988). Using Youden's Index, a cut-off for probability of follow up was set at 0.094 with the overall sensitivity and specificity maximized to 86.2% and 88% respectively. Using this model and cohort, 194 three month clinic visits would have been deemed medically unnecessary. Preliminary blind prospective testing against the 22 patient cohort demonstrates a model sensitivity and specificity at 100% and 75% respectively, correctly deeming 15 visits unnecessary.

Virtual clinics and automated clinical decision making can improve healthcare inefficiencies, unclog clinic wait times, and ultimately enhance quality of care delivery. Our regression model is highly accurate in determining medical necessity for physician examination at the three month visit following supracondylar fracture closed reduction and percutaneous pinning. When applied correctly, there is potential for significant reductions in health care expenditures and in the economic burden on patient families by removing unnecessary visits. In light of positive patient and family receptiveness toward technology, our promising findings and predictive model may pave the way for remote health care delivery, virtual clinics, and automated clinical decision making.

Aim: To investigate, quantify and model the influence of three biomechanical factors on the severity of mechanically induced nuclear disruption in healthy bovine, caudal, intervertebral discs.

Method: A preliminary study was conducted with a fully divided annular wall to investigate the cohesive nature of the isolated nucleus and its tendency to form clefts when loaded. A second more clinically relevant model using whole bovine discs was then conducted to investigate whether significant clefts could be induced in healthy discs by controlling flexion, hydration and rate of compressive loading.. A finite element model of the bovine caudal disc was constructed to predict the complex stress conditions that exist within the disc.

Results: We found that high degrees of flexion and hydration were significant risk factors in nuclear disruption (P < 0.005), while the rate of loading showed no significant effect (P = 0.37). The intact disc study also showed that flexion and hydration are significant risk factors (P < 0.002). In contrast with the preliminary study, the rate of loading was also shown to be mildly significant (P < 0.1). The finite element model predicted relatively high concentrations of stress and strain energy density within the nucleus. This is consistent with the experimental observations of cleft formation.

Conclusions: While it is well established that dehydration of the nucleus is a symptom of degeneration this study suggested that the healthy nucleus, when maximally hydrated, is more susceptible to nuclear disruption when loaded. This supports the hypothesis that the histologically abnormal and degenerate nuclear material removed at surgery, may well have attained this state as a result of biomechanical and biochemical changes occurring in the disc following rather than preceding a prolapse. This study further defined the rôle of trauma in disc injury and prolapse of the normal disc.

Purpose: There is very little evidence to guide treatment of patients with spinal surgical site infection (SSI) who require irrigation and debridement (I& D) with respect to need for single or multiple I& D’s. The purpose of this study is to build a predictive model which stratifies patients with spinal SSI to determine which patients will go on to need single versus multiple I& D.

Method: A consecutive series of 128 patients from a tertiary spine center (collected from 1999–2005) who required I& D for spinal SSI, were studied based on data from a prospectively collected outcomes database. Over 30 variables were identified by extensive literature review as possible risk factors for SSI, and tested as possible predictors of risk for multiple I& D. Logistic regression was conducted to assess each variable’s predictability by a “bootstrap” statistical method. Logistic regression was applied using outcome of I& D – single or multiple as the “response”.

Results: 24/128 patients required multiple I& D. Primary spine diagnosis was approximately represented by ¼ trauma, ¼ deformity, ¼ degenerative and ¼ oncology/inflammatory/other. Six predictors: spine location, medical comorbidities, microbiology of the SSI, presence of distant site infection (ie. UTI or bacteremia), presence of instrumentation and bone graft type, proved to be the most reliable predictors of need for multiple I& D. Internal validation of the predictive model yielded area under the curve (AUC) of .84

Conclusion: Infection factors played an important role in need for multiple I& D. Patients with +MRSA culture or those with distant site infection such as bacteremia with or without UTI or pneumonia, were strong predictors of need for multiple I& D. Presence of instrumentation, location of surgery in the posterior lumbar spine and use of non-autograft bone predicted multiple I& D. Diabetes also proved to be the most significant medical comorbidity for multiple I& D.

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

While clinically important improvements in Oxford Shoulder Scores have been defined for patients with general shoulder problems or those undergoing subacromial decompression, no threshold has been reported for classifying improvement after shoulder replacement surgery. This study aimed to establish the minimal clinically important change (MCIC) for the Oxford Shoulder Score in patients undergoing primary total shoulder replacement (TSR). Patient-reported outcomes data were sourced from the Australian Orthopaedic Association National Joint Replacement Registry Patient-Reported Outcome Measures Program. These included pre- and 6-month post-operative Oxford Shoulder Scores and a rating of patient-perceived change after surgery (5-point scale ranging from ‘much worse’ to ‘much better’). Two anchor-based methods (using patient-perceived improvement as the anchor) were used to calculate the MCIC: 1) mean change method; and 2) predictive modelling, with and without adjustment for the proportion of improved patients. The analysis included 612 patients undergoing primary TSR who provided pre- and post-operative data (58% female; mean (SD) age 70 (8) years). Most patients (93%) reported improvement after surgery. The MCIC derived from the mean change method was 6.8 points (95%CI 4.7 to 8.9). Predictive modelling produced an MCIC estimate of 11.6 points (95%CI 8.9 to 15.6), which reduced to 8.7 points (95%CI 6.0 to 12.7) after adjustment for the proportion of improved patients. For patient-reported outcome measures to provide valuable information that can support clinical care, we need to understand the magnitude of change that matters to patients. Using contemporary psychometric methods, this analysis has generated MCIC estimates for the Oxford Shoulder Score. These estimates can be used by clinicians and researchers to interpret important changes in pain and function after TSR from the patient's perspective. We conclude that an increase in Oxford Shoulder Scores of at least 9 points can be considered a meaningful improvement in shoulder-related pain and function after TSR

While clinically important improvements in Oxford Shoulder Scores have been defined for patients with general shoulder problems or those undergoing subacromial decompression, no threshold has been reported for classifying improvement after shoulder replacement surgery. This study aimed to establish the minimal clinically important change (MCIC) for the Oxford Shoulder Score in patients undergoing primary total shoulder replacement (TSR). Patient-reported outcomes data were sourced from the Australian Orthopaedic Association National Joint Replacement Registry Patient-Reported Outcome Measures Program. These included pre- and 6-month post-operative Oxford Shoulder Scores and a rating of patient-perceived change after surgery (5-point scale ranging from ‘much worse’ to ‘much better’). Two anchor-based methods (using patient-perceived improvement as the anchor) were used to calculate the MCIC: 1) mean change method; and 2) predictive modelling, with and without adjustment for the proportion of improved patients. The analysis included 612 patients undergoing primary TSR who provided pre- and post-operative data (58% female; mean (SD) age 70 (8) years). Most patients (93%) reported improvement after surgery. The MCIC derived from the mean change method was 6.8 points (95%CI 4.7 to 8.9). Predictive modelling produced an MCIC estimate of 11.6 points (95%CI 8.9 to 15.6), which reduced to 8.7 points (95%CI 6.0 to 12.7) after adjustment for the proportion of improved patients. For patient-reported outcome measures to provide valuable information that can support clinical care, we need to understand the magnitude of change that matters to patients. Using contemporary psychometric methods, this analysis has generated MCIC estimates for the Oxford Shoulder Score. These estimates can be used by clinicians and researchers to interpret important changes in pain and function after TSR from the patient's perspective. We conclude that an increase in Oxford Shoulder Scores of at least 9 points can be considered a meaningful improvement in shoulder-related pain and function after TSR

Abstract. 1.0 Objectives. Predictive structural models resulting in a trabecular bone topology closely resembling real bone would be a step toward 3D printing of sympathetic prosthetics. This study modifies an established trabecular bone structural adaptation approach, with the objective of achieving an improved adapted topology, specifically connectivity, compared to CT imaging studies; whilst retaining continuum level mechanical properties consistent with those reported in experimental studies. Strain driven structural adaptation models successfully identify trabecular trajectories, although tend to overpredict connectivity and skew trabecular radii distribution towards the smallest radius included in the adaptation. Radius adaptation of each trabecula is driven by a mechanostat approach with a target strain (1250 µɛ) below which radius is decreased (resorption), and above which radius is increased (apposition). Simulations include a lazy zone, in which neither resorption nor apposition takes place (1000 to 1500 µɛ); and a dead zone (<250 µɛ) in which complete resorption of trabeculae with the smallest included radius takes place. This study assesses the impact of increasing the dead zone threshold from <250 µɛ to <1000 µɛ, the lower limit of the lazy zone. 2.0 Methods. In-silico structural models with an initial connectivity (number of trabeculae connecting at each joint) of 14 were generated using a nearest neighbour approach applied to a random cloud of points. Trabeculae were modelled using circular beams whose radii were adapted in response to normal strains caused by the axial force and bending moments due to a vertical pressure of 1 MPa applied to the top of the lattice, with the bottom of the lattice fixed in the vertical direction. Lattices in which nodes are either able (rigid jointed) or unable (pin jointed) to transmit bending moments were considered. Five virtual samples of each lattice type were used, and each simulation repeated twice: with a dead zone of either <250 µɛ or <1000 µɛ. 3.0 Results. In pin jointed lattices the increase in dead zone threshold resulted in reduction of predicted Young's Modulus from 580 MPa (95% CI [577 MPa, 583 MPa]) to 408 MPa (95% CI [397 MPa, 419 MPa]) whilst in rigid jointed lattices it increased form 839MPa (95% CI [832 MPa, 846 MPa]) to 933 MPa (95% CI [931 MPa, 936 MPa]). Mean connectivity decreased from 10.2 to 5.8 in pin jointed simulations and from 9.6 to 3.8 in fixed joined simulations. Topological studies of trabecular bone CT images report a mean connectivity of around 3.4. Pin jointed lattice mean radius increased from 33mm to 45mm, and rigid jointed lattice mean radius increased from 33mm to 64mm. Prevalence of smallest included radius beams decreased in both. 4.0 Conclusion. Improved in-silico representations of trabecular bone can be achieved in structural adaptions by increasing the dead zone threshold and adopting a bending dominated (rigid jointed) lattice structure. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

Considerable efforts have been invested into identifying risk factors for periprosthetic joint infection (PJI) after total joint arthroplasty (TJA). Preoperative identification of risk factors for developing PJI is imperative for medical optimization and targeted prophylaxis. The purpose of this study was to create a preoperative risk calculator for PJI by assessing a patient's individual risks for developing PJI with resistant organisms and S.aureus. A retrospective review of 27117 patients (43253 TJAs) from 1999 to 2014, including 1035 PJIs, was performed. A total of 41 risk factors including demographics, comorbidities (using the Elixhauser and Charlson Index), and the number of previous TJAs, were evaluated. Multivariate analysis was performed; coefficients of the models were scaled to produce useful integer scoring. Predictive model strength was assessed employing area under the curve (AUC) analysis. Among the 41 assessed variables, the following were significant risk factors in descending order of significance: prior surgeries (p<0.0001), drug abuse (p=0.0003), revision surgery (p<0.0001), human immunodeficiency virus (p=0.0004), coagulopathy (p<0.0001), renal disease (p<0.0001), congestive heart-failure (p<0.0001), psychoses (p=0.0024), rheumatological disease (p<0.0001), knee involvement (p<0.0001), diabetes (p<0.0001), anemia (p<0.0001), males (p<0.0001), liver disease (p=0.0093), smoking (p=0.0268), and high BMI (p<0.0001). Furthermore, presence of heart-valve disease (p=0.0409), metastatic disease (p=0.0006), and pulmonary disease (p=0.0042) increased the resistant organism PJIs. Patients with metastatic disease were also more likely to be infected with S. aureus (p=0.0002). AUCs were 0.83 for any PJI, 0.86 for resistant PJI, and 0.84 for S.aureus PJI models. This large-scale single-institutional study has determined various risk factors for PJI. Some factors are modifiable and need to be addressed before elective arthroplasty. It is imperative that surgeons are aware of these risk factors and implement all possible preventative measures, including targeted prophylaxis, in patients with high-risk of PJI. Continued efforts are needed to find novel and effective solutions to minimize the burden PJI

Introduction: The aims of this study were to. determine predictors of pain, function and activity level 1–2 years after revision hip arthroplasty and. define quality of life outcomes after revision total hip replacement. Methods: A prospective cohort of 222 patients who underwent revision hip arthroplasty were evaluated. Predictive models were developed and proportional odds regression analyses were performed to identify factors that predict quality of life outcomes at 1 and 2 years post surgery. The dependent outcome variables were WOMAC function, pain and UCLA activity. The independent variables included patient demographic, surgery specific and objective parameters including baseline Western Ontario McMaster Universities (WOMAC) osteoarthritis index, and the Short Form-12 mental component. The Loess method was used to plot the change of WOMAC and SF-12 scores over time. Results: There was a significant improvement (p< 0.001) in all patient quality of life scores from baseline with results plateauing at 1 year. UCLA activity remained static between 1 and 2 years. In the predictive model, higher baseline WOMAC function (p < 0.001), age between 60–70 (p< 0.037), male gender (0.017), lower Charnley class (p < 0.001) and diagnosis of aseptic loosening (p < 0.003) were significant predictors of improved function. When considering WOMAC pain as an outcome variable, factors predictive of improving category outcome included baseline WOMAC function (p= 0.001), age between 60–70 (p< 0.004), male gender (p= 0.005), lower Charnley class (p< 0.001) and no previous revisions (p < 0.023). Baseline WOMAC pain did not predict final pain outcome. Baseline WOMAC function (p=0.001), the indication for the operation (p=0.007), and the operating surgeon were significant predictors of UCLA activity at follow up. Peri or post-operative complications were not an adverse predictor of physical function, pain or activity. Conclusions: Predictors of quality of life outcomes after revision hip replacement-showed that although some patient specific and surgical specific variables were important, age, gender, Charnley class and baseline WOMAC function had the most robust associations with outcomes

Aims

No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model.

Aims

The primary objective of this registry-based study was to compare patient-reported outcomes of cementless and cemented medial unicompartmental knee arthroplasty (UKA) during the first postoperative year. The secondary objective was to assess one- and three-year implant survival of both fixation techniques.

Aims

The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA).

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.

The April 2013 Spine Roundup³⁶⁰ looks at: smuggling spinal implants; local bone graft and PLIF; predicting disability with slipped discs; mortality and spinal surgery; spondyloarthropathy; brachytherapy; and fibrin mesh and BMP.