Risk factors for musculoskeletal injuries in elite junior tennis players: a systematic review

Item does not contain fulltextThe objective was to systematically review the literature on risk factors and prevention programs for musculoskeletal injuries among tennis players. PubmedMedline, Embase, CINAHL, Cochrane, SportDiscus were searched up to February 2017. Experts in clinical and epidemiological medicine were contacted to obtain additional studies. For risk factors, prospective cohort studies (n > 20) with a statistical analysis for injured and non-injured players were included and studies with a RCT design for prevention programs. Downs&Black checklist was assessed for risk of bias for risk factors. From a total of 4067 articles, five articles met our inclusion criteria for risk factors. No studies on effectiveness of prevention programs were identified. Quality of studies included varied from fair to excellent. Best evidence synthesis revealed moderate evidence for previous injury regardless of body location in general and fewer years of tennis experience for the occurrence of upper extremity injuries. Moderate evidence was found for lower back injuries, a previous back injury, playing >6hours/week and low lateral flexion of the neck for risk factors. Limited evidence was found for male gender as a risk factor. The risk factors identified can assist clinicians in developing prevention-strategies. Further studies should focus on risk factor evaluation in recreational adult tennis players

Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data

Objective When correcting for the "class imbalance" problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Materials and Methods Multiple classification algorithms were trained on 7 resampled datasets: no correction, random undersampling, 4 ratios of Synthetic Minority Oversampling Technique (SMOTE), and random oversampling with the Adaptive Synthetic algorithm (ADASYN). Performance was evaluated in Area Under the Curve (AUC), precision, recall, Brier score, and calibration metrics. A case study on prediction modeling for 30-day unplanned readmissions in previously admitted Urology patients was presented. Results For most algorithms, using resampled data showed a significant increase in AUC and precision, ranging from 0.74 (CI: 0.69-0.79) to 0.93 (CI: 0.92-0.94), and 0.35 (CI: 0.12-0.58) to 0.86 (CI: 0.81-0.92) respectively. All classification algorithms showed significant increases in recall, and significant decreases in Brier score with distorted calibration overestimating positives. Discussion Imbalance correction resulted in an overall improved performance, yet poorly calibrated models. There can still be clinical utility due to a strong discriminating performance, specifically when predicting only low and high risk cases is clinically more relevant. Conclusion Resampling data resulted in increased performances in classification algorithms, yet produced an overestimation of positive predictions. Based on the findings from our case study, a thoughtful predefinition of the clinical prediction task may guide the use of resampling techniques in future studies aiming to improve clinical decision support tools.Orthopaedics, Trauma Surgery and Rehabilitatio

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

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 Hemi-arthroplasty (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, in-ternally 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.</p

Clockwise torque results in higher reoperation rates in left-sided femur fractures

Purpose: Effects of clockwise torque rotation onto proximal femoral fracture fixation have been subject of ongoing debate: fixated right-sided trochanteric fractures seem more rotationally stable than left-sided fractures in the biomechanical setting, but this theoretical advantage has not been demonstrated in the clinical setting to date. The purpose of this study was to identify a difference in early reoperation rate between patients undergoing surgery for left-versus right-sided proximal femur fractures using cephalomedullary nailing (CMN). Materials and methods: The American College of Surgeons National Surgical Quality Improvement Program was queried from 2016-2019 to identify patients aged 50 years and older undergoing CMN for a proximal femoral fracture. The primary outcome was any unplanned reoperation within 30 days following surgery. The difference was calculated using a Chi-square test, and observed power calculated using post-hoc power analysis. Results: In total, of 20,122 patients undergoing CMN for proximal femoral fracture management, 1.8% (n=371) had to undergo an unplanned reoperation within 30 days after surgery. Overall, 208 (2.0%) were left-sided and 163 (1.7%) right-sided fractures (p=0.052, risk ratio [RR] 1.22, 95% confidence interval [CI] 1.00-1.50), odds ratio [OR] 1.23 (95%CI 1.00-1.51), power 49.2% (& alpha;=0.05). Conclusion: This study shows a higher risk of reoperation for left-sided compared to right-sided proximal femur fractures after CMN in a large sample size. Although results may be underpowered and statistically insignificant, this finding might substantiate the hypothesis that clockwise rotation during implant insertion and (post-operative) weightbearing may lead to higher reoperation rates. Level of evidence: Therapeutic level II.Orthopaedics, Trauma Surgery and Rehabilitatio

Artificial intelligence in orthopaedic trauma surgery: The hype & the hips

The use of artificial intelligence (AI) methods has made the healthcare sector curious about improving decision support tools to aid decision-making using data-driven methods, in all subsequent steps of patient care. The hype around AI triggers hope, with the rise of available electronic health record data to build large datasets, and the increase in computational power. However, many AI applications have not yet made it to clinical practice. Therefore, questions arise: ‘Why do so many promising applications have yet to be adopted in patients’ clinical care or doctors’ workflow?’ and ‘Maybe AI methods do not always lead to better outcomes than traditional methods?’. The thesis will start with an introduction to AI in healthcare, and the rise of AI applications in Orthopaedic surgery. Subsequently, the aim of this thesis was to evaluate the hype of machine learning (ML) applications in Orthopaedic surgery, develop and (internally and externally) validate decision support tools specific to the hip fracture population (mortality prediction and postoperative delirium prediction), and propose a way forward for enabling integration of these tools into the clinical workflow

Artificial intelligence in orthopaedic trauma surgery: The hype & the hips

The use of artificial intelligence (AI) methods has made the healthcare sector curious about improving decision support tools to aid decision-making using data-driven methods, in all subsequent steps of patient care. The hype around AI triggers hope, with the rise of available electronic health record data to build large datasets, and the increase in computational power. However, many AI applications have not yet made it to clinical practice. Therefore, questions arise: ‘Why do so many promising applications have yet to be adopted in patients’ clinical care or doctors’ workflow?’ and ‘Maybe AI methods do not always lead to better outcomes than traditional methods?’. The thesis will start with an introduction to AI in healthcare, and the rise of AI applications in Orthopaedic surgery. Subsequently, the aim of this thesis was to evaluate the hype of machine learning (ML) applications in Orthopaedic surgery, develop and (internally and externally) validate decision support tools specific to the hip fracture population (mortality prediction and postoperative delirium prediction), and propose a way forward for enabling integration of these tools into the clinical workflow

Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data

Objective: When correcting for the “class imbalance” problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Materials and Methods: Multiple classification algorithms were trained on 7 resampled datasets: no correction, random undersampling, 4 ratios of Synthetic Minority Oversampling Technique (SMOTE), and random oversampling with the Adaptive Synthetic algorithm (ADASYN). Performance was evaluated in Area Under the Curve (AUC), precision, recall, Brier score, and calibration metrics. A case study on prediction modeling for 30-day unplanned readmissions in previously admitted Urology patients was presented. Results: For most algorithms, using resampled data showed a significant increase in AUC and precision, ranging from 0.74 (CI: 0.69–0.79) to 0.93 (CI: 0.92–0.94), and 0.35 (CI: 0.12–0.58) to 0.86 (CI: 0.81–0.92) respectively. All classification algorithms showed significant increases in recall, and significant decreases in Brier score with distorted calibration overestimating positives. Discussion: Imbalance correction resulted in an overall improved performance, yet poorly calibrated models. There can still be clinical utility due to a strong discriminating performance, specifically when predicting only low and high risk cases is clinically more relevant. Conclusion: Resampling data resulted in increased performances in classification algorithms, yet produced an overestimation of positive predictions. Based on the findings from our case study, a thoughtful predefinition of the clinical prediction task may guide the use of resampling techniques in future studies aiming to improve clinical decision support tools.Information and Communication Technolog