Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017

Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player\u27s future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury\u27s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers

Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

© The Author(s) 2020. Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR (P \u3c.0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR (P \u3c.0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season

Interobserver and Intraobserver Reliability of an MRI-Based Classification System for Injuries to the Ulnar Collateral Ligament.

BACKGROUND: Despite improvements in understanding biomechanics and surgical options for ulnar collateral ligament (UCL) tears, there remains a need for a reliable classification of UCL tears that has the potential to guide clinical decision making. PURPOSE: To assess the intra- and interobserver reliability of the newly proposed magnetic resonance imaging (MRI)-based classification for UCL tears. Secondary objectives included assessing the effect of additional views, discrimination between distal and nondistal tears, and correlation of imaging reads with intraoperative findings of the UCL. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Nine fellowship-trained specialists from 7 institutions independently completed 4 surveys consisting of 60 elbow MRI scans with UCL tears using a newly proposed 6-stage classification system. The first and third surveys contained 60 coronal images, while the second and fourth contained the same images with coronal and axial views presented in a random order to assess intraobserver variability via the weighted kappa value and the effect of additional imaging views. Weighted kappa values were also calculated for each of the 4 surveys to acquire interobserver reliability. Reliability analysis was repeated through a 2-group classification analysis for distal and nondistal tears. Observer readings were compared with intraoperative UCL findings. RESULTS: For the newly proposed 6-stage MRI-based classification, intra- and interobserver reliability demonstrated near perfect and substantial agreement, respectively. These values increased only when substratified into the 2-group distal and nondistal tear classification ( P \u3c .05). The additional axial view did not statistically improve the agreement within and among readers. When compared with intraoperative findings from 30 elbows, observer readings were accurate for tear grade (partial and complete), proximal location, and distal location but not midsubstance tears. CONCLUSION: The newly proposed 6-stage MRI-based classification utilizing grade and location of the injury had substantial to near perfect agreement among and within fellowship-trained observers

Evaluation of Endothelial and Vascular-Derived Progenitor Cell Populations in the Proximal and Distal UCL of the Elbow: A Comparative Study

Background: Vascular-derived progenitor and endothelial cell populations (CD31, CD34, CD146) are capable of multipotent differentiation at the site of injured ligamentous tissue to aid in the intrinsic healing response. Proximal ulnar collateral ligament (UCL) tears have been reported to have better healing capability when compared with distal UCL tears. Purpose: To compare the vascular composition of the proximal and distal insertions of the anterior bundle of the UCL of the elbow via known markers of endothelial and vascular-derived progenitor cells (CD31, CD34, CD146). Study Design: Descriptive laboratory study. Methods: UCLs were harvested from 10 nonpaired fresh-frozen human cadaveric elbows and transected into proximal and distal portions. Endothelial and vascular-derived progenitor cell densities were assessed with 4 staining groups: CD31 (immunohistochemistry) and CD31/α-smooth muscle actin (α-SMA), CD34/α-SMA, and CD146/α-SMA (immunofluorescence). CD31 immunohistochemistry identified endothelial progenitor cells in the UCL. Later staining of the same slides with α-SMA demonstrated the relationship of progenitor cells to the surrounding vasculature. Fluorescent staining was quantified by calculating the proportion of positively stained nuclei versus the total number of nuclei in the proximal and distal UCL. Results: CD31+ cells were present in the proximal and distal sections of all 10 UCLs. Fluorescent staining revealed no significant differences in the ratio of CD31 to total nuclei between the distal (median, 36% [range, 23%-53%]) and proximal UCL (39% [22%-56%]) (P = .432, Wilcoxon signed-rank test). Similarly, no differences were seen between CD34 distal (39% [24%-64%]) and proximal regions (46% [28%-63%]) (P = .846, Wilcoxon signed-rank test) or CD146 distal (40% [12%-65%]) and proximal regions (40% [22%-51%]) (P ≥ .999, Wilcoxon signed-rank test). Conclusion: Analysis of UCL tissues demonstrated equal distributions of vascular endothelial and vascular-derived progenitor cell markers throughout the proximal and distal UCL. Unlike that of the medial collateral ligament of the knee, the microvascular composition of the proximal and distal UCL insertions was not different, suggesting a well-vascularized ligament throughout its course

The shoulders of professional beach volleyball players: high prevalence of infraspinatus muscle atrophy

BACKGROUND: Beach volleyball is an Olympic overhead sport. It is not well known which clinical and imaging findings are normal and which are associated with symptoms. HYPOTHESIS: There are typical clinical and imaging findings in the hitting shoulders of fully competitive professional beach volleyball players, as compared with their nonhitting shoulders. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: During the Beach Volleyball Grand Slam Tournament in Klagenfurt, Austria, 84 professional players (54 men, 30 women) underwent a questionnaire-based interview and a complete physical examination, including scoring and sonography of both shoulders. Twenty-nine players had shoulder MRIs. RESULTS: The mean age of the athletes was 28 years. Atrophy of the infraspinatus muscle was found in 30% of the hitting shoulders, and it was not typically recognized by the players. The absolute Constant score was significantly lower in the hitting shoulder (87 versus 93 points, P < .0001). Average external rotation strength was decreased in the hitting shoulder (8.2 versus 9.5 kg, P < .0001). There were more abnormalities on the sonography of the hitting shoulder (1.7 versus 0.4, P < .0001) and in the same shoulders on MRI than on sonography (P = .0231). Compression of the suprascapular nerve was not observed. Pain in the hitting shoulder was present in 63% of the players, without clear correlations to the investigated clinical and imaging parameters. CONCLUSION: The prevalence of infraspinatus muscle atrophy in professional beach volleyball players is 30%. The typical, fully competitive player has subjectively unrecognized decreased strength of external rotation and frequent unspecific shoulder pain. Therefore, abnormal clinical and imaging findings in the beach volleyball player should be interpreted with care

Classification and return-to-play considerations for stress fractures

Stress fractures are common injuries, particularly in endurance athletes. Stress fracture management should take into consideration the injury site (low risk versus high risk), the grade (extent of microdamage accumulation), and the individual’s competitive situation. The authors briefly discuss the pathophysiology and diagnostic process of stress fractures and expand on the classification of stress fractures and its impact on return-to-play decision making based on the relative risk of the fracture