The Relationship Between Humeral Torsion and Arm Injury in Baseball Players: A Systematic Review and Meta-analysis

A narrative review of little league shoulder: proximal humeral physis widening is only one piece of the puzzle, it is time to consider posterior glenoid dysplasia

Baseball athletes across all levels of play are at an increased risk for upper extremity injury due to the supraphysiologic demands on the shoulder and elbow during overhead throwing. Little league baseball players present with a unique subset of injuries that can affect the growth plate, commonly at the shoulder or the elbow. Ascertaining a diagnosis and plan of care for little league shoulder (LLS) historically focuses on the proximal humeral physis in skeletally immature throwing athletes presenting with shoulder pain. However, while not a current standard of care, posterior glenoid dysplasia is often present in youth baseball athletes presenting with LLS, warranting a shift in the way clinicians evaluate for and treat the youth baseball athlete's pathologic shoulder. Therefore, purpose of this narrative review is 2-fold: first, to describe the current standard of care as it relates to a diagnosis of LLS, and second, to critically describe a comprehensive evaluation process for youth throwing athletes with shoulder pain that includes screening for evidence of posterior glenoid dysplasia. This paper summarizes the current state of the available evidence for anatomic considerations of LLS in the baseball athletes throwing shoulder. Additionally, we provide a framework for clinical evaluation using a multidisciplinary approach to evaluate the entire kinetic chain of the youth baseball athlete presenting with LLS and posterior glenoid dysplasia. A case study is presented to describe common presentations, clinical and objective examinations, and a plan of care from time of evaluation to return to throwing.

Organizational risk profiling and education associated with reduction in professional pitching arm injuries: a natural experiment

Background

Risk profiling and education are strategies implemented to help reduce injury risk; however, currently. there is little evidence on the effect of these interventions on injury incidence. The purpose of this study was to evaluate the influence of risk profiling and education on upper extremity injury incidence in minor league (MiLB) pitchers and to stratify by injury severity.

Methods

A prospective natural experiment study was conducted from 2013 to 2019 on MiLB pitchers. Beginning in the 2015 season, pitchers were examined and risk profiled for upper extremity injury. Shoulder external, internal, total range of motion, horizontal adduction, and humeral torsion were measured. Organizational risk profiling and education was implemented starting in 2015, based on preseason assessments. Chi-squared test was performed to investigate potential differences between shoulder range of motion risk categories between 2013-2014 (pre) and 2015-2019 (post) seasons. Interrupted time series analyses were performed to assess the association between organizational risk profiling and education on arm injury in MiLB pitchers and were repeated for 7-27 and 28+ day injury severity.

Results

297 pitchers were included (pre: 119, post: 178). Upper extremity injury incidence was 1.5 injuries per 1000 athletic exposures. Pitchers in the 2015-2019 seasons demonstrated increased preseason shoulder injury risk for internal (P = .003) and external (P = .007), while the 2013-2014 seasons demonstrated greater horizontal adduction risk (P = .04). There were no differences between seasons for total range of motion risk (P =.76). Risk profiling and education resulted in an adjusted time loss upper extremity injury reduction for the 2015-2019 seasons (0.68 (95% CI: 0.47, 0.99)), which impacted 7-27 days (0.62 (95% CI: 0.42, 0.93)) but not for 28+ days (0.71 (95% CI: 0.47, 1.06)) time loss. There was no reduction in combined trunk and lower extremity injuries for the 2015-2019 seasons (1.55 (95% CI: 0.79, 3.01)).

Conclusions

Organizational risk profiling and education appear to reduce professional pitching overall and 7-27-day upper extremity injury risk by 33%-38%. There was no difference in trunk and lower extremity injuries over the period, strengthening the reduction in upper extremity injury risk results. This suggests that while injury risk increased over time, organizational risk profiling mitigated the expected increase in upper extremity injury rates. Risk profiling and education can be used as a clinical screening and intervention tool to help decrease upper extremity injuries in professional baseball populations.

Reliability Analysis of In-person and Virtual Goniometric Measurements of the Upper Extremity

Background

Virtual healthcare has forced clinicians to modify or eliminate parts of the musculoskeletal evaluation such as motion assessment. Although acceptable to excellent levels of in-person goniometric reliability is achievable, reliability of virtual assessments is unknown.

Purpose

To determine if similar upper extremity goniometric measurements could be obtained in-person and virtually.

Study Design

Reliability study; classroom setting.

Methods

Publicly recruited sample over 18 years of age with no upper extremity injuries. Each subject was tested in a standing position with dominant arm facing the clinicians to visualize the landmarks for goniometer placement. Flexion and extension of the shoulder, elbow and wrist were measured. Prior to performing in-person goniometric measurements for each joint, an image was captured of each pre-determined joint position using a mobile device with a camera. This image represented the screenshot on a virtual platform. Four clinicians performed in-person measurements twice during the same session on each subject. The following week clinicians measured virtual images using the same techniques. Inter-rater and intra-rater reliability were determined via intraclass correlation coefficients (ICC).

Results

Inter-rater reliability for five of the six in-person (ICC≥0.81) and virtual measurements (ICC≥0.78 ) were classified as excellent. In-person wrist extension (ICC=0.60) and virtual wrist flexion (ICC=0.65) were classified as good. Intra-rater reliability for individual clinicians were between good and excellent for the in-person measurements (ICC:0.61-0.96) and virtual measurements (ICC:0.72-0.97). There were a greater number of excellent ICC values for the virtual measurements (90%) compared to in-person measurements (70%). There were statistically significant differences between in-person and virtual sessions for five of six measurements (p≤0.006). Only elbow extension did not differ between sessions (p=0.966).

Conclusion

Virtual assessment compared to goniometric measurements showed good to excellent inter- and intra-rater reliabilities (ICC > 0.60), which suggests clinicians can utilize goniometry either in person or on a virtual platform.

Level of Evidence

3b©The Author(s).

Development of an Injury Burden Prediction Model in Professional Baseball Pitchers

Background

Baseball injuries are a significant problem and have increased in incidence over the last decade. Reporting injury incidence only gives context to rate but not in relation to severity or injury time loss.

Hypothesis/Purpose

The purpose of this study was to 1) incorporate both modifiable and non-modifiable factors to develop an arm injury burden prediction model in Minor League Baseball (MiLB) pitchers; and 2) understand how the model performs separately on elbow and shoulder injury burden.

Study Design

Prospective longitudinal study.

Methods

The study was conducted from 2013 to 2019 on MiLB pitchers. Pitchers were evaluated in spring training arm for shoulder range of motion and injuries were followed throughout the season. A model to predict arm injury burden was produced using zero inflated negative binomial regression. Internal validation was performed using ten-fold cross validation. Subgroup analyses were performed for elbow and shoulder separately. Model performance was assessed with root mean square error (RMSE), model fit (R2), and calibration with 95% confidence intervals (95% CI).

Results

Two-hundred, ninety-seven pitchers (94 injuries) were included with an injury incidence of 1.15 arm injuries per 1000 athletic exposures. Median days lost to an arm injury was 58 (11, 106). The final model demonstrated good prediction ability (RMSE: 11.9 days, R2: 0.80) and a calibration slope of 0.98 (95% CI: 0.92, 1.04). A separate elbow model demonstrated weaker predictive performance (RMSE: 21.3; R2: 0.42; calibration: 1.25 [1.16, 1.34]), as did a separate shoulder model (RMSE: 17.9; R2: 0.57; calibration: 1.01 [0.92, 1.10]).

Conclusions

The injury burden prediction model demonstrated excellent performance. Caution should be advised with predictions between one to 14 days lost to arm injury. Separate elbow and shoulder prediction models demonstrated decreased performance. The inclusion of both modifiable and non-modifiable factors into a comprehensive injury burden model provides the most accurate prediction of days lost in professional pitchers.

Level of Evidence

2.

Differences in Glenohumeral Range of Motion and Humeral Torsion Between Right-Handed and Left-Handed Professional Baseball Pitchers

BACKGROUND

Elite pitchers have demonstrated significant differences in glenohumeral range of motion and humeral torsion compared with the nonthrowing population. Furthermore, abnormal shoulder range of motion measurements have been associated with different injury risks and challenges in assessing rehabilitation progress. Variations in range of motion and torsion due to handedness in the asymptomatic professional population have yet to be investigated in the literature.

HYPOTHESIS

No significant differences in glenohumeral range of motion and humeral torsion would exist between asymptomatic right- and left-handed professional pitchers.

STUDY DESIGN

Cross-sectional study; Level of evidence, 3.

METHODS

217 Major League Baseball pitchers from a single organization were evaluated over a 7-year period between 2013 and 2020. Range of motion was measured with a standard goniometer. Ultrasound scanning was used to determine neutral position of the shoulder, and the degree of humeral torsion was measured with a goniometer.

RESULTS

Right-handed pitchers demonstrated significantly greater values of glenohumeral external rotation (118.5° vs 112.7°; P < .001) in their throwing arms compared with their left-handed counterparts. Right-handed pitchers also showed greater values of glenohumeral internal rotation deficit (13.9° vs 4.8°; P < .001) and side-to-side differences in humeral retrotorsion (-23.1° vs -2.2°; P < .001). Left-handed pitchers demonstrated significantly greater flexion deficits in the throwing arm compared with their right-handed counterparts (7.5° vs 0.0°; P < .001).

CONCLUSION

In the throwing arm, right-handed pitchers demonstrated significantly greater measures of external rotation, glenohumeral internal rotation deficit, and humeral retrotorsion compared with left-handed counterparts. Furthermore, right-handed pitchers demonstrated a significant side-to-side difference in retrotorsion, whereas left-handed pitchers did not. However, left-handed pitchers demonstrated a side-to-side shoulder flexion deficit that was not present in the cohort of right-handed pitchers. The correlation between humeral retrotorsion and increased external rotation indicates that osseous adaptations may play a role in range of motion differences associated with handedness. Additionally, these findings may explain observed differences in several throwing metrics between right- and left-handed pitchers. Knowledge of these differences can inform rehabilitation programs and shoulder maintenance regimens.

Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers

Background

Humeral torsion is an important osseous adaptation in throwing athletes that can contribute to arm injuries. Currently there are no cheap and easy to use clinical tools to measure humeral torsion, inhibiting clinical assessment. Models with low error and "good" calibration slope may be helpful for prediction.

Hypothesis/Purpose

To develop prediction models using a range of machine learning methods to predict humeral torsion in professional baseball pitchers and compare these models to a previously developed regression-based prediction model.

Study Design

Prospective cohort.

Methods

An eleven-year professional baseball cohort was recruited from 2009-2019. Age, arm dominance, injury history, and continent of origin were collected as well as preseason shoulder external and internal rotation, horizontal adduction passive range of motion, and humeral torsion were collected each season. Regression and machine learning models were developed to predict humeral torsion followed by internal validation with 10-fold cross validation. Root mean square error (RMSE), which is reported in degrees (°) and calibration slope (agreement of predicted and actual outcome; best = 1.00) were assessed.

Results

Four hundred and seven pitchers (Age: 23.2 +/-2.4 years, body mass index: 25.1 +/-2.3 km/m2, Left-Handed: 17%) participated. Regression model RMSE was 12° and calibration was 1.00 (95% CI: 0.94, 1.06). Random Forest RMSE was 9° and calibration was 1.33 (95% CI: 1.29, 1.37). Gradient boosting machine RMSE was 9° and calibration was 1.09 (95% CI: 1.04, 1.14). Support vector machine RMSE was 10° and calibration was 1.13 (95% CI: 1.08, 1.18). Artificial neural network RMSE was 15° and calibration was 1.03 (95% CI: 0.97, 1.09).

Conclusion

This is the first study to show that machine learning models do not improve baseball humeral torsion prediction compared to a traditional regression model. While machine learning models demonstrated improved RMSE compared to the regression, the machine learning models displayed poorer calibration compared to regression. Based on these results it is recommended to use a simple equation from a statistical model which can be quickly and efficiently integrated within a clinical setting.

Levels of Evidence

2.

Risk Factors of Shoulder and Elbow Injuries in Baseball: A Scoping Review of 3 Types of Evidence

Background

Shoulder and elbow overuse injuries are the most common problems in baseball players. No scoping review has compared the findings from different types of evidence.

Purpose

To map the broad evidence from 3 types of evidence (epidemiological, biomechanical, and narrative) on potential risk factors for shoulder and elbow injuries in baseball and identify gaps in the existing literature to guide future research.

Study Design

Scoping review.

Methods

Eight electronic databases were searched from inception to May 14, 2020. Any peer-reviewed papers that investigated or discussed potential risk factors for shoulder and elbow injuries in baseball were included.

Results

A total of 302 studies (107 epidemiological studies, 85 biomechanical studies, and 110 narrative reviews) were included. Risk factors were categorized into 9 domains: sports profiles, physical characteristics/functions, pitching mechanics, performance, behavioral, psychosocial, biological and developmental, injury/sports profiles, and environmental factors. Studies were consistent in supporting limited shoulder range of motion (ROM) and player positions (pitchers or catchers) as risk factors for shoulder injuries. For elbow injuries, the majority of the included studies suggested that being pitchers or catchers and working with higher ball velocity can be risk factors.

Conclusion

Findings were consistent in some risk factors, such as limited shoulder ROM and positions. However, findings were inconsistent or limited for most factors, and substantial research gaps were identified. Research assessing those factors with inconsistent or limited evidence in the current literature were recognized to be priorities for future studies.

Development and internal validation of a humeral torsion prediction model in professional baseball pitchers

BACKGROUND

Humeral torsion (HT) has been linked to pitching arm injury risk after controlling for shoulder range of motion. Currently measuring HT uses expensive equipment, which inhibits clinical assessment. Developing an HT predictive model can aid clinical baseball arm injury risk examination. Therefore, the purpose of this study was to develop and internally validate an HT prediction model using standard clinical tests and measures in professional baseball pitchers.

METHODS

An 11-year (2009-2019) prospective professional baseball cohort was used for this study. Participants were included if they were able to participate in all practices and competitions and were under a Minor League Baseball contract. Preseason shoulder range of motion (external rotation [ER], internal rotation [IR], horizontal adduction [HA]) and HT were collected each season. Player age, arm dominance, arm injury history, and continent of origin were also collected. Examiners were blinded to arm dominance. An a priori power analysis determined that 244 players were needed for accurate prediction models. Missing data was low (<3%); thus, a complete case analysis was performed. Model development followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) recommendations. Regression models with restricted cubic splines were performed. Following primary model development, bootstrapping with 2000 iterations were performed to reduce overfitting and assess optimism shrinkage. Prediction model performance was assessed through root mean square error (RMSE), R², and calibration slope with 95% confidence intervals (CIs). Sensitivity analyses included dominant and nondominant HT.

RESULTS

A total of 407 professional pitchers (age: 23.2 [standard deviation 2.4] years, left-handed: 17%; arm history prevalence: 21%) participated. Predictors with the highest influence within the model include IR (0.4, 95% CI 0.3, 0.5; P < .001), ER (-0.3, 95% CI -0.4, -0.2; P < .001), HA (0.3, 95% CI 0.2, 0.4; P < .001), and arm dominance (right-handed: -1.9, 95% CI -3.6, -0.1; P = .034). Final model RMSE was 12, R² was 0.41, and calibration was 1.00 (95% CI 0.94, 1.06). Sensitivity analyses demonstrated similar model performance.

CONCLUSIONS

Every 3° of IR explained 1° of HT. Every 3° of ER explained 1° less of HT, and every 7° of HA explained 1° of HT. Right-handers had 2° less HT. Models demonstrated good predictive performance. This predictive model can be used by clinicians to infer HT using standard clinical test and measures. These data can be used to enhance professional baseball arm injury examination.

Relationship of clinical measures with humeral torsion in young adults: a pilot study

PURPOSE

Humeral retroversion alters range of motion and has been linked to injury risk. Clinically,palpation of the bicipital groove is used to quantify humeral torsion, but the accuracy of this procedure has not been fully examined. The purpose of this study was to investigate the relationship between clinical and diagnostic ultrasound (US) assessment of humeral torsion while considering shoulder position of the participant and clinical expertise of the examiner.

METHODS

Seventeen participants (34 shoulders, 16/17 right handed, 10/17 history of throwing) were recruited. US was assessed by an experienced assessor. Two clinical assessments of humeral torsion were performed by two assessors of different experience (expert and novice). Humeral torsion was assessed at 90 degrees shoulder abduction (Palp90) and 45 degrees shoulder abduction (Palp45). Within assessor intraclass correlation coefficients (ICC (3, 1) were calculated. Correlation coefficients (Pearson's) were generated to determine relationship between clinical and US examination findings.

RESULTS

Intra-rater reliability for clinical tests were good (ICCs .73 - .92) for both raters. Of the palpation tests, only the expert assessor was significantly correlated to the US measurement (p<.001) at Palp45 (r = .64) and Palp90 (r = .62). For the expert, there was a significantly lower angle calculated for Palp45 compared to Palp90 (p<.001).

CONCLUSION

The accuracy of both palpation methods for assessing humeral retrotorsion may depend on the training background of the assessor. Further, the glenohumeral position of the patient during palpation should be consistent for the purposes of repeated testing.