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.

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

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

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.