Determining the True Incidence of Glenohumeral Instability Among Players in the National Football League: An Epidemiological Study of Non-Missed Time Shoulder Instability Injuries.

Background: Shoulder instability encompasses a spectrum of glenohumeral pathology ranging from subluxation to dislocation. While dislocation frequently leads to removal from play, athletes are often able to play through subluxation. Previous research on glenohumeral instability among athletes has largely focused on missed-time injuries, which has likely disproportionately excluded subluxation injuries and underestimated the overall incidence of shoulder instability. Purpose: To describe the epidemiology of shoulder instability injuries resulting in no missed time beyond the date of injury (non-missed time injuries) among athletes in the National Football League (NFL). Study Design: Descriptive epidemiology study. Methods: The NFL's electronic medical record was retrospectively reviewed to identify non-missed time shoulder instability injuries during the 2015 through 2019 seasons. For each injury, player age, player position, shoulder laterality, instability type, instability direction, injury timing, injury setting, and injury mechanism were recorded. For injuries that occurred during games, incidence rates were calculated based on time during the season as well as player position. The influence of player position on instability direction was also investigated. Results: Of the 546 shoulder instability injuries documented during the study period, 162 were non-missed time injuries. The majority of non-missed time injuries were subluxations (97.4%), occurred during games (70.7%), and resulted from a contact mechanism (91.2%). The overall incidence rate of game-related instability was 1.6 injuries per 100,000 player-plays and was highest during the postseason (3.5 per 100,000 player-plays). The greatest proportion of non-missed time injuries occurred in defensive secondary players (28.4%) and offensive linemen (19.8%), while kickers/punters and defensive secondary players had the highest game incidence rates (5.5 and 2.1 per 100,000 player-plays, respectively). In terms of direction, 54.3% of instability events were posterior, 31.9% anterior, 8.5% multidirectional, and 5.3% inferior. Instability events were most often anterior among linebackers and wide receivers (50% and 100%, respectively), while posterior instability was most common in defensive linemen (66.7%), defensive secondary players (58.6%), quarterbacks (100.0%), running backs (55.6%), and tight ends (75.0%). Conclusion: The majority of non-missed time shoulder instability injuries (97.4%) were subluxations, which were likely excluded from or underreported in previous shoulder instability studies due to the inherent difficulty of detecting and diagnosing shoulder subluxation.

Comparing Skill Acquisition and Validity of Immersive Virtual Reality with Cadaver Laboratory Sessions in Training for Reverse Total Shoulder Arthroplasty.

Immersive virtual reality (iVR) allows surgical trainees to practice skills without risking harm to patients or the need for cadaveric training resources. However, iVR has never been directly compared with cadaver training, the longtime gold standard for surgical skill training. We aimed to compare skill acquisition using cadaver laboratory and iVR training methods for augmented baseplate implantation during reverse total shoulder arthroplasty (rTSA). Methods: In a randomized controlled trial, junior orthopaedic surgery residents were assigned to a 1-hour training with either iVR or a cadaveric laboratory session with shoulder specimens. Before training, all participants viewed an overview lecture and technique video demonstrating key steps of augmented baseplate implantation for rTSA. Participants were assessed by a blinded evaluator using validated competency checklists during cadaveric glenoid baseplate implantation. Continuous and categorial variables were analyzed using the 2-sample t test and Fisher exact test. Results: Fourteen junior residents (3 incoming matched postgraduate year [PGY1], 6 PGY1s, 1 PGY2, and 4 PGY3s) were randomized to training with either iVR (n = 6) or cadaver laboratory (n = 8). There were no significant differences in demographic data, previous experience with rTSA, or previous use of iVR (p > 0.05). There were no significant difference in total Objective Structured Assessment of Technical Skill score (91.2% [15.2] vs. 93.25% [6.32], -0.1406 to 0.1823, p = 0.763), Global Rating Scale score (4.708 [0.459] vs. 4.609 [0.465], -0.647 to 0.450, p = 0.699), or time to completion (546 seconds [158] vs. 591 seconds [192], -176.3 to 266.8, p = 0.655) in cadaveric glenoid baseplate implantation. Average cost of iVR hardware and a 1-year software license was $4,900, and average cost of a single cadaver laboratory was $1,268.20 per resident. Conclusions: Among junior orthopaedic residents, there is similar skill acquisition when training with either cadaver laboratory or iVR. Although additional research into this field is needed, iVR may provide an important and cost-effective tool in surgical education. Clinical Relevance: Emerging simulation and iVR technology simulation in surgical training programs can increase access to effective and high-level surgical training across the globe and improve quality of care.

Effects of Malpositioning of the Knee on Radiographic Measurements: The Influence of Adduction, Abduction, and Malrotation on Measured Tibial Slope.

Background: Increased posterior tibial slope (PTS) is a risk factor for knee pathology. Accurate measurement of PTS is predicated on a quality lateral knee radiograph; however, little is known about how the quality of the radiograph affects the measured PTS. Purposes: To (1) describe a method for measuring malalignment on lateral knee radiographs, (2) assess the effects of malpositioning of the knee on radiographic measures of malalignment, and (3) determine any correlations between malalignment and the measured PTS. Study Design: Descriptive laboratory study. Methods: Using a setup similar to that of a standard radiology suite, 25 sets of radiographs were taken using 5 sawbone models. Each set included a true lateral view and separate malpositioned radiographs at 5°, 10°, and 15° of adduction, abduction, internal rotation, and external rotation. Malalignment for each radiograph was quantified as the anterior-posterior distance (APD) and proximal-distal distance (PDD) between femoral condyles. The medial PTS was measured in duplicate, and the interrater reliability was calculated. Results: The interrater reliability was excellent, with intraclass correlation coefficients of 0.92, 0.91, and 0.96 for the APD, PDD, and PTS, respectively. Malrotation significantly affected the APD (P < .001), with a mean change of 5.6 mm per 5°. Malpositioning in abduction/adduction significantly affected the PDD (P < .001), with a mean change of 5.1 mm per 5°. There was no significant impact of rotation or APD on the PTS. Abduction/adduction did affect the PTS (P < .001) above a threshold of 5° of malpositioning. The PTS decreased as the PDD increased, moving from adduction to abduction (R2 = 0.5687). Conclusion: The measured PTS was more sensitive to malpositioning by abduction/adduction than by malrotation. Malrotation affected the APD, while abduction/adduction affected the PDD. Thus, the accuracy of the measured PTS was compromised more by poorly aligned distal femoral condyles than it was by poorly aligned posterior femoral condyles. Clinical Relevance: To minimize the effects of malpositioning, we recommend utilizing radiographs with a |PDD| of <5 mm and an |APD| of <15 mm when measuring the PTS.

Common Orthopaedic Injuries in CrossFit Athletes.

CrossFit is a high-intensity exercise program that has gained popularity over the past few decades. CrossFit combines movements from Olympic weight lifting, gymnastics, powerlifting, and high-intensity interval training. As CrossFit continues to expand, knowledge of the associated orthopaedic injuries to aid providers in diagnosis, treatment, and prevention will be increasingly important. The most common CrossFit injuries occur in the shoulder (25% of all injuries), spine (14%), and knee (13%). Male athletes are markedly more likely to experience injuries than female athletes, and injuries occur markedly less when there is supervised coaching of the athletes. The most common causes of injury in CrossFit include improper form and exacerbation of a prior injury. The purpose of this article was to review the literature to aid clinicians in identifying and treating common orthopaedic injuries in CrossFit athletes. Understanding the injury patterns, treatment, and prevention options is important for a successful recovery and return to sport.

Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction.

PURPOSE: This study aimed to develop machine learning (ML) models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following anterior cruciate ligament reconstruction (ACLR). Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective ACLR from 2012 to 2018. Artificial neural network ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the receiver operating characteristic curve. Regression analyses were used to identify variables that were significantly associated with the predicted outcomes. RESULTS: A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission included White race, obesity, hypertension, and American Society of Anesthesiologists classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared with logistic regression models, artificial neural network models reported superior area under the receiver operating characteristic curve values in predicting overnight stay (0.835 vs 0.589), 30-day complications (0.742 vs 0.590), reoperation (0.842 vs 0.601), ACLR-related readmission (0.872 vs 0.606), deep-vein thrombosis (0.804 vs 0.608), and surgical-site infection (0.818 vs 0.596). CONCLUSIONS: The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. ML models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay. LEVEL OF EVIDENCE: IV, retrospective comparative prognostic trial.

Using machine learning methods to predict nonhome discharge after elective total shoulder arthroplasty.

BACKGROUND: Machine learning has shown potential in accurately predicting outcomes after orthopedic surgery, thereby allowing for improved patient selection, risk stratification, and preoperative planning. This study sought to develop machine learning models to predict nonhome discharge after total shoulder arthroplasty (TSA). METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective TSA from 2012 to 2018. Boosted decision tree and artificial neural networks (ANN) machine learning models were developed to predict non-home discharge and 30-day postoperative complications. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and overall accuracy (%). Multivariate binary logistic regression analyses were used to identify variables that were significantly associated with the predicted outcomes. RESULTS: There were 21,544 elective TSA cases identified in the National Surgical Quality Improvement Program registry from 2012 to 2018 that met inclusion criteria. Multivariate logistic regression identified several variables associated with increased risk of nonhome discharge including female sex (odds ratio [OR] = 2.83; 95% confidence interval [CI] = 2.53-3.17; P < .001), age older than 70 years (OR = 3.19; 95% CI = 2.86-3.57; P < .001), American Society of Anesthesiologists classification 3 or greater (OR = 2.70; 95% CI = 2.41-2.03; P < .001), prolonged operative time (OR = 1.38; 95% CI = 1.20-1.58; P < .001), as well as history of diabetes (OR = 1.56; 95% CI = 1.38-1.75; P < .001), chronic obstructive pulmonary disease (OR = 1.71; 95% CI = 1.46-2.01; P < .001), congestive heart failure (OR = 2.65; 95% CI = 1.72-4.01; P < .001), hypertension (OR = 1.35; 95% CI = 1.20-1.52; P = .004), dialysis (OR = 3.58; 95% CI = 2.01-6.39; P = .002), wound infection (OR = 5.67; 95% CI = 3.46-9.29; P < .001), steroid use (OR = 1.43; 95% CI = 1.18-1.74; P = .010), and bleeding disorder (OR = 1.84; 95% CI = 1.45-2.34; P < .001). The boosted decision tree model for predicting nonhome discharge had an AUC of 0.788 and an overall accuracy of 90.3%. The ANN model for predicting nonhome discharge had an AUC of 0.851 and an overall accuracy of 89.9%. For predicting the occurrence of 1 or more postoperative complications, the boosted decision tree model had an AUC of 0.795 and an overall accuracy of 95.5%. The ANN model yielded an AUC of 0.788 and an overall accuracy of 92.5%. CONCLUSIONS: Both the boosted decision tree and ANN models performed well in predicting nonhome discharge with similar overall accuracy, but the ANN had higher discriminative ability. Based on the findings of this study, machine learning has the potential to accurately predict nonhome discharge after elective TSA. Surgeons can use such tools to guide patient expectations and to improve preoperative discharge planning, with the ultimate goal of decreasing hospital length of stay and improving cost-efficiency.

Revision Lateral Ankle Reconstruction 40 Years After Chrisman-Snook Procedure: A Case Report.

CASE: A 70-year-old active woman presented with lateral ankle instability 40 years after a lateral ankle reconstruction procedure. Examination demonstrated gross instability, and advanced imaging revealed attenuation of her previous graft. She underwent anatomic reconstruction through a modified Brostrom-Gould technique and was able to return to hiking without pain. CONCLUSION: Recurrent lateral ankle instability after reconstruction represents a unique challenge for orthopaedic surgeons. Utilization of a modified Brostrom-Gould procedure with suture tape augmentation is a promising alternative to allograft or autograft reconstruction for patients with active lifestyle goals in the context of recurrent instability.

Exercise as a Treatment for Schizophrenia: A Review.

Schizophrenia is a mental disorder that is characterized by progressive cognitive impairment in areas of attention, working memory, and executive functioning. Although no clear etiology of schizophrenia has been discovered, many factors have been identified that contribute to the development of the disease, such as neurotransmitter alterations, decreased synaptic plasticity, and diminished hippocampal volume. Historically, antipsychotic medications have targeted biochemical alterations in the brains of patients with schizophrenia but have been ineffective in alleviating cognitive and hippocampal deficits. Other modalities, such as exercise therapy, have been proposed as adjuvant or primary therapy options. Exercise therapy has been shown to improve positive and negative symptoms, quality of life, cognition, and hippocampal plasticity, and to increase hippocampal volume in the brains of patients with schizophrenia. This article will briefly review the clinical signs, symptoms and proposed etiologies of schizophrenia, and describe the current understanding of exercise programs as an effective treatment in patients with the disease.