Similar Medium-Term Revision Rates Following Acute Total Hip Arthroplasty Versus Open Reduction and Internal Fixation for Acetabular Fractures in the Elderly.

BACKGROUND: The management of elderly acetabular fractures is complex, with high rates of conversion total hip arthroplasty (THA) after open reduction and internal fixation (ORIF), but potentially higher rates of complications after acute THA. METHODS: The California Office of Statewide Health Planning and Development database was queried between 2010 and 2017 for all patients aged 60 years or older who sustained a closed, isolated acetabular fracture and underwent ORIF, THA, or a combination. Chi-square tests and Student t tests were used to identify demographic differences between groups. Multivariate regression was used to evaluate predictors of 30-day readmission and 90-day complications. Kaplan-Meier (KM) survival analysis and Cox proportional hazards model were used to estimate the revision surgery-free survival (revision-free survival [RFS]), with revision surgery defined as conversion THA, revision ORIF, or revision THA. RESULTS: A total of 2,184 surgically managed acetabular fractures in elderly patients were identified, with 1,637 (75.0%) undergoing ORIF and 547 (25.0%) undergoing THA with or without ORIF. Median follow-up was 295 days (interquartile range, 13 to 1720 days). 99.4% of revisions following ORIF were for conversion arthroplasty. Unadjusted KM analysis showed no difference in RFS between ORIF and THA (log-rank test P = 0.27). RFS for ORIF patients was 95.1%, 85.8%, 78.3%, and 71.4% at 6, 12, 24 and 60 months, respectively. RFS for THA patients was 91.6%, 88.9%, 87.2%, and 78.8% at 6, 12, 24 and 60 months, respectively. Roughly 50% of revisions occurred within the first year postoperatively (49% for ORIF, 52% for THA). In propensity score-matched analysis, there was no difference between RFS on KM analysis (P = 0.22). CONCLUSIONS: No difference was observed in medium-term RFS between acute THA and ORIF for elderly acetabular fractures in California. Revision surgeries for either conversion or revision THA were relatively common in both groups, with roughly half of all revisions occurring within the first year postoperatively. LEVEL OF EVIDENCE: III.

Compliant Intramedullary Stems for Joint Reconstruction.

The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.

A predictive algorithm for perioperative complications and readmission after ankle arthrodesis.

PURPOSE: Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis. We aim to develop a predictive model for major perioperative complication or readmission after ankle arthrodesis. METHODS: This is a retrospective cohort study of adult patients who underwent ankle arthrodesis at any non-federal California hospital between 2015 and 2017. The primary outcome is readmission within 30 days or major perioperative complication. We build logistic regression and ML models spanning different classes of modeling approaches, assessing discrimination and calibration. We also rank the contribution of the included variables to model performance for prediction of adverse outcomes. RESULTS: A total of 1084 patients met inclusion criteria for this study. There were 131 patients with major complication or readmission (12.1%). The XGBoost algorithm demonstrates the highest discrimination with an area under the receiver operating characteristic curve of 0.707 and is well-calibrated. The features most important for prediction of adverse outcomes for the XGBoost model include: diabetes, peripheral vascular disease, teaching hospital status, morbid obesity, history of musculoskeletal infection, history of hip fracture, renal failure, implant complication, history of major fracture. CONCLUSION: We report a well-calibrated algorithm for prediction of major perioperative complications and 30-day readmission after ankle arthrodesis. This tool may help accurately risk-stratify patients and decrease likelihood of major complications.

Short-Term Outcomes and Long-Term Implant Survival After Inpatient Surgical Management of Geriatric Proximal Humerus Fractures.

Introduction: The most common surgical options for geriatric proximal humerus fractures are open reduction and internal fixation (ORIF), hemiarthroplasty (HA), and reverse total shoulder arthroplasty. We used a longitudinal inpatient discharge database to determine the cumulative incidence of conversion to arthroplasty after ORIF of geriatric proximal humerus fractures. The rates of short-term complications and all-cause reoperation were also compared. Patients and Methods: All patients 65 or older who sustained a proximal humerus fracture and underwent either ORIF, HA, or shoulder arthroplasty (SA) as an inpatient from 2000 through 2017 were identified. Survival analysis was performed with ORIF conversion to arthroplasty and all-cause reoperation as the endpoints of interest. Rates of 30-day readmission and short-term complications were compared. Trends in procedure choice and outcomes over the study period were analyzed. Results: A total of 27 102 geriatric patients that underwent inpatient surgical management of proximal humerus fractures were identified. Among geriatric patients undergoing ORIF, the cumulative incidence of conversion to arthroplasty within 10 years was 8.2%. The 10-year cumulative incidence of all-cause reoperation was 12.1% for ORIF patients and less than 4% for both HA and SA patients. Female sex was associated with increased risk of ORIF conversion and younger age was associated with higher all-cause reoperation. ORIF was associated with higher 30-day readmission and short-term complication rates. Over the study period, the proportion of patients treated with ORIF or SA increased while the proportion of patients treated with HA decreased. Short-term complication rates were similar between arthroplasty and ORIF patients in the later cohort (2015-2017). Conclusion: The 10-year cumulative incidence of conversion to arthroplasty for geriatric patients undergoing proximal humerus ORIF as an inpatient was found to be 8.2%. All-cause reoperations, short-term complications, and 30-day readmissions were all significantly lower among patients undergoing arthroplasty, but the difference in complication rate between arthroplasty and ORIF was attenuated in more recent years. Younger age was a risk factor for reoperation and female sex was associated with increased risk of requiring conversion to arthroplasty after ORIF.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: A retrospective, case-control study. OBJECTIVE: We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. METHODS: This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. RESULTS: A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. CONCLUSION: We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Geriatric Distal Femur Fractures: Equivalent Long-Term Reoperation Rates Between Fixation and Primary Arthroplasty.

OBJECTIVE: Our primary objectives were to (1) determine the rate of requiring conversion to arthroplasty after open reduction internal fixation (ORIF) of geriatric distal femur fractures and (2) compare 10-year reoperation rates after ORIF versus primary arthroplasty for geriatric distal femur fractures. DESIGN: Propensity-matched retrospective cohort study. SETTING: All centers participating in the California Office of Statewide Health Planning and Development (OSHPD) database. PATIENTS/PARTICIPANTS: All patients 65 years of age or older who underwent operative management of a distal femur fracture between 2000 and 2017. INTERVENTION: ORIF, total knee arthroplasty (TKA), or distal femur replacement (DFR). MAIN OUTCOME MEASUREMENTS: Reoperation. RESULTS: A total of 16,784 patients with geriatric distal femur fracture were identified, of which 16,343 (97.4%) underwent ORIF. The cumulative incidence of conversion to arthroplasty within 10 years of ORIF was found to be 3.5%, with young age and female sex identified as risk factors for conversion. There was no significant difference in 10-year reoperation-free survival rate between propensity-matched patients undergoing ORIF versus primary arthroplasty (94.5% vs. 96.2%, P = 0.659). There were no differences in short-term complication or readmission rates between matched treatment cohorts, but arthroplasty was associated with a higher rate of wound infection within 90 days (2.0% vs. 0.2%, P = 0.011). CONCLUSIONS: The 10-year cumulative incidence of conversion to arthroplasty after ORIF was found to be low. There was no significant difference in long-term reoperation-free survival rates between patients undergoing ORIF versus primary arthroplasty. Primary arthroplasty was associated with significantly higher rates of acute wound or joint infection. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

A Risk Calculator for the Prediction of C5 Nerve Root Palsy After Instrumented Cervical Fusion.

BACKGROUND: C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy. METHODS: We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model. RESULTS: A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age. CONCLUSIONS: We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.

Background: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods: A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. Results: There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. Conclusion: We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.

Total joint replacement surgeon choice: A qualitative analysis in a medicare population.

INTRODUCTION: Previous research has shown that patients from historically marginalized groups in the United States tend to have poorer outcomes after joint replacement surgery and that they are less likely to receive joint replacement surgery at high-volume hospitals. However, little is known regarding how this group of patients chooses their joint replacement surgeon. The purpose of this study was to understand the factors influencing the choice of joint replacement surgeon amongst a diverse group of patients. METHODS: Semi-structured interviews were conducted with Medicare patients who underwent a hip or knee replacement within the last 24 months (N = 38) at an academic and community hospital. Interviews were audio recorded, transcribed and verified for accuracy. Transcripts were reviewed using iterative content analysis to extract key themes related to how respondents chose their joint replacement surgeon. RESULTS AND DISCUSSION: MD referral/recommendation appears to be the strongest factor influencing joint replacement surgeon choice. Other key considerations are hospital reputation and surgeon attributes-including operative experience, communication skills, and participation in shared decision-making. Gender/ethnicity of a surgeon, industry payments to surgeons, number of publications and cost did not play a large role in surgeon choice. CONCLUSION AND CLINICAL RELEVANCE: The process of choosing a joint replacement surgeon is a complex decision-making process with several factors at play. Despite growing availability of information regarding surgeons, patients largely relied on referrals for choosing their joint replacement surgeon regardless of ethnicity. Referring physicians need to ensure that patients are able to access hospital and surgeon outcomes, operative volume, and industry-payment information to learn more about their orthopedic surgeons in order to make an informed choice.

Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion.

PURPOSE: Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance. METHODS: This is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance with standard ML models and logistic regression (LR), ranking contribution of included variables to model performance. RESULTS: Of the included 6822 patients, 18.8% suffered a major complication or readmission. The ensemble model demonstrated slightly superior predictive performance compared to LR and standard ML models. The most important features to performance include sex, malignancy, pneumonia, stroke, and teaching hospital status. Seven of the ten most important features for the ensemble model were markedly less important for LR. CONCLUSION: We report an ensemble ML model for prediction of major complications and readmission after posterior cervical fusion with a modest risk prediction advantage compared to LR and benchmark ML models. Notably, the features most important to the ensemble are markedly different from those for LR, suggesting that advanced ML methods may identify novel prognostic factors for adverse outcomes after posterior cervical fusion.

A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.

BACKGROUND: There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA. METHODS: Thirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic regression (LR), various other ML methods (XGBoost, Gradient Boosting, AdaBoost, and Random Forest), and AP were used for model building to determine discriminative power (area under receiver operating curve), calibration (Brier score), and feature importance. RESULTS: Between 2015 and 2017, there were a total of 156,750 TKAs with 1109 (0.7%) total major complications. AP had the highest discriminative performance with area under receiver operating curve 0.679 compared with LR, XGBoost, Gradient Boosting, AdaBoost, and Random Forest (0.617, 0.601, 0.662, 0.657, and 0.545, respectively). AP (Brier score 0.007) had similar calibration as the other ML methods (0.006, 0.006, 0.022, 0.007, and 0.008, respectively). The variables that are most important for AP differ from those that are most important for LR. CONCLUSION: Compared to conventional ML algorithms, AP has superior discriminative ability with similar calibration and suggests nonlinear relationships between variables in outcomes of TKA.

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model. METHODS: We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively. RESULTS: There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers' compensation insurance. Teaching hospital status and concussion history were not found to be important for regression. CONCLUSIONS: We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.

Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.

BACKGROUND: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods. METHODS: This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration. RESULTS: There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease). CONCLUSION: We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty.

Background: Reverse total shoulder arthroplasty (rTSA) offers tremendous promise for the treatment of complex pathologies beyond the scope of anatomic total shoulder arthroplasty but is associated with a higher rate of major postoperative complications. We aimed to design and validate a machine learning (ML) model to predict major postoperative complications or readmission following rTSA. Methods: We retrospectively reviewed California's Office of Statewide Health Planning and Development database for patients who underwent rTSA between 2015 and 2017. We implemented logistic regression (LR), extreme gradient boosting (XGBoost), gradient boosting machines, adaptive boosting, and random forest classifiers in Python and trained these models using 64 binary, continuous, and discrete variables to predict the occurrence of at least one major postoperative complication or readmission following primary rTSA. Models were validated using the standard metrics of area under the receiver operating characteristic (AUROC) curve, area under the precision-recall curve (AUPRC), and Brier scores. The key factors for the top-performing model were determined. Results: Of 2799 rTSAs performed during the study period, 152 patients (5%) had at least 1 major postoperative complication or 30-day readmission. XGBoost had the highest AUROC and AUPRC of 0.681 and 0.129, respectively. The key predictive features in this model were patients with a history of implant complications, protein-calorie malnutrition, and a higher number of comorbidities. Conclusion: Our study reports an ML model for the prediction of major complications or 30-day readmission following rTSA. XGBoost outperformed traditional LR models and also identified key predictive features of complications and readmission.

Risk Adjustment in the California Joint Replacement Registry: Is Patient Complexity Accurately Assessed in Academic Versus Nonacademic Hospitals?

BACKGROUND: We sought to report on the differences in observed versus expected arthroplasty outcomes between academic and nonacademic hospitals in a large joint registry. We utilized the California Joint Replacement Registry's data and risk adjustment model. METHODS: Observed versus expected hip and knee arthroplasty complications were utilized to assess hospital and surgeon risk-adjusted complication rates (RACRs). Based on a hospital and surgeon RACR, each was assigned a performance rating ("worse," "expected," "better"). Associations between academic status and performance ratings, rates of individual complications, prevalence of risk factors associated with increased complication rates, and differences in complication rates were calculated. RESULTS: A higher percentage of academic providers had "worse" than expected ratings, whereas a higher percentage of nonacademic providers had "expected" and "better" than expected ratings (P = .011) based on the observed versus expected complication rates. There was a higher incidence of patients with congestive heart failure and an elevated American Society of Anesthesiologists classification in academic institutions (P = .0001). The complication rate was higher in academic institutions for all total knee arthroplasties (P < .0016). CONCLUSIONS: We identified disparities in RACRs between nonacademic and academic institutions. This may reflect the difficulty of fully adjusting for medical risk and surgical complexity in a large arthroplasty database.

Randomized Trial of a Virtual Reality Tool to Teach Surgical Technique for Tibial Shaft Fracture Intramedullary Nailing.

INTRODUCTION: Active learning methods have accumulated popularity due to improved results in knowledge acquisition as opposed to passive learning methods. For surgical resident physicians with limited training opportunities outside of the operating room due to time constraints, virtual reality (VR) is a relatively inexpensive and time-efficient active training method for procurement of surgical skills. We conducted a simulated intramedullary nailing (IMN) of a tibia to demonstrate VR training programs as a more effective modality of learning orthopedic surgical techniques compared to passive learning tools such as a standard guide (SG) through trained novice medical students performing a SawBones simulation of intramedullary nail fixation. MATERIALS AND METHODS: First and second-year medical students without prior experience of procedure were recruited and randomized to SG or VR training. Participants were observed performing simulated tibia IMN procedure immediately after training and evaluated by a blinded attending surgeon using procedure-specific checklist and 5-point global assessment scale. Participants returned after 2-weeks for repeat training and evaluation. RESULTS: 20 participants were recruited and randomized into VR (n = 10) and SG (n = 10) groups. All 20 participants completed the first phase and 17 completed the second phase of the study. Aggregate global assessment scores were significantly higher for VR than SG group (17.5 vs. 7.5, p < 0.001), including scores in all individual categories. The percentage of steps completed correctly was significantly higher in the VR group compared to the SG group (63% vs. 25%, p < 0.002). Average improvement between the first and second phases of the study were higher in the VR group compared to SG group across all 5-categories of the global assessment scale, and significantly higher for knowledge of instruments (50% vs. 11%, p, 0.01). DISCUSSION: VR training was more effective than a passive SG in our model of simulated tibia IMN for novice medical students. Virtual reality training may be a useful method to augment orthopedic education.

The Role of Gender, Academic Affiliation, and Subspecialty in Relation to Industry Payments to Orthopaedic Surgeons.

BACKGROUND: The Physician-Payments-Sunshine-Act (PPSA) was introduced in 2010 to provide transparency regarding physician-industry payments by making these payments publicly available. Given potential ethical implications, it is important to understand how these payments are being distributed, particularly as the women orthopaedic workforce increases. The purpose of this study was thus to determine the role of gender and academic affiliation in relation to industry payments within the orthopaedic subspecialties. METHODS: The PPSA website was used to abstract industry payments to Orthopaedic surgeons. The internet was then queried to identify each surgeon's professional listing and gender. Mann-Whitney U, Chi-square tests, and multivariable regression were used to explore the relationships. Significance was set at a value of P < 0.05. RESULTS: In total, 22,352 orthopaedic surgeons were included in the study. Payments were compared between 21,053 men and 1299 women, 2756 academic and 19,596 community surgeons, and across orthopaedic subspecialties. Women surgeons received smaller research and non-research payments than men (both, P < 0.001). There was a larger percentage of women in academics than men (15.9% vs 12.1%, P < 0.001). Subspecialties with a higher percentage of women (Foot & Ankle, Hand, and Pediatrics) were also the subspecialties with the lowest mean industry payments (all P < 0.001). Academic surgeons on average, received larger research and non-research industry payments, than community surgeons (both, P < 0.001). Multivariable linear regression demonstrated that male gender (P = 0.006, P = 0.029), adult reconstruction (both, P < 0.001) and spine (P = 0.008, P < 0.001) subspecialties, and academic rank (both, P < 0.001) were independent predictors of larger industry research and non-research payments. CONCLUSIONS: A large proportion of the US orthopaedic surgeon workforce received industry payments in 2014. Academic surgeons received larger payments than community surgeons. Despite having a larger percentage of surgeons in academia, women surgeons received lower payments than their male counterparts. Women also had a larger representation in the subspecialties with the lowest payments.