The use of a pitch count estimator to calculate exposure in collegiate baseball pitchers

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.

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.

Including Modifiable and Nonmodifiable Factors Improves Injury Risk Assessment in Professional Baseball Pitchers

OBJECTIVES

To (1) evaluate an injury risk model that included modifiable and nonmodifiable factors into an arm injury risk prediction model in Minor League Baseball (MiLB) pitchers and (2) compare model performance separately for predicting the incidence of elbow and shoulder injuries.

DESIGN

Prospective cohort.

METHODS

A 10-year MiLB injury risk study was conducted. Pitchers were evaluated during preseason, and pitches and arm injuries were documented prospectively. Nonmodifiable variables included arm injury history, professional experience, arm dominance, year, and humeral torsion. Modifiable variables included BMI, pitch count, total range of motion, and horizontal adduction. We compared modifiable, nonmodifiable, and combined model performance by R2, calibration (best = 1.00), and discrimination (area under the curve [AUC]; higher number is better). Sensitivity analysis included only arm injuries sustained in the first 90 days.

RESULTS

In this study, 407 MiLB pitchers (141 arm injuries) were included. Arm injury incidence was 0.27 injuries per 1000 pitches. The arm injury model (calibration 1.05 [0.81-1.30]; AUC: 0.74 [0.69-0.80]) had improved performance compared to only using modifiable predictors (calibration: 0.91 [0.68-1.14]; AUC: 0.67 [0.62-0.73]) and only shoulder range of motion (calibration: 0.52 [0.29, 0.75]; AUC: 0.52 [0.46, 58]). Elbow injury model demonstrated improved performance (calibration: 1.03 [0.76-1.33]; AUC: 0.76 [0.69-0.83]) compared to the shoulder injury model (calibration: 0.46 [0.22-0.69]; AUC: 0.62 [95% CI: 0.55, 0.69]). The sensitivity analysis demonstrated improved model performance compared to the arm injury model.

CONCLUSION

Arm injury risk is influenced by modifiable and nonmodifiable risk factors. The most accurate way to identify professional pitchers who are at risk for arm injury is to use a model that includes modifiable and nonmodifiable risk factors. J Orthop Sports Phys Ther 2022;52(9):630-640. Epub: 9 July 2022. doi:10.2519/jospt.2022.11072.

Initial kinematic chain injuries increase hazard of subsequent arm injuries in professional baseball pitchers

BACKGROUND

Currently, there are few studies that have evaluated the relationship between a lower extremity or trunk injury (kinematic chain) and subsequent arm injury. The purpose of this study was (1) to investigate the relationship between initial kinematic chain (lower extremity or trunk) injury and subsequent arm injury; and (2) to investigate the relationship between initial shoulder or elbow injury and subsequent arm injury.

METHODS

A 7-year prospective injury risk study was conducted with Minor League Baseball pitchers. Pitches, pitching appearances, athlete exposures (AEs), and arm injuries (≥1-day time loss) were documented throughout the season. Cox survival analyses with 95% confidence intervals (95% CIs) were performed. Confounders controlled for included age, body mass index, arm dominance, pitching role, previous arm injury, number of pitching appearances, and seasonal pitch load.

RESULTS

A total of 297 pitchers participated (total player days = 85,270). Arm injury incidence was 11.4 arm injuries/10,000 AEs, and kinematic chain incidence was 5.2 injuries/10,000 AEs. Pitchers who sustained a kinematic chain injury demonstrated a greater hazard (2.6 [95% CI: 1.2, 5.6], P = .019) of sustaining an arm injury. Pitchers who sustained an initial shoulder injury demonstrated a greater hazard (9.3 [95% CI: 1.1, 83], P = .047) of sustaining a subsequent shoulder or elbow injury compared with pitchers who sustained an initial elbow injury.

CONCLUSIONS

Pitchers who sustained an initial lower extremity or trunk injury demonstrated an increased subsequent arm injury hazard compared with pitchers who did not. Pitchers who sustained an initial shoulder injury demonstrated a greater hazard of sustaining a subsequent arm injury compared with pitchers who sustained an initial elbow injury. However, this secondary analysis should be interpreted with caution. Clinicians should monitor risk with workload accumulation, which may be related to pitching compensatory strategies in a fatigued state. Pitchers who sustain a shoulder injury should be evaluated and perform both shoulder and elbow rehabilitation strategies before return to sport.

Hazard of Arm Injury in Professional Starting and Relief Pitchers

CONTEXT

How different pitching roles affect the risk of arm injury in professional pitchers is currently unclear.

OBJECTIVES

To investigate differences between professional baseball starting and relief pitchers in the hazard of (1) arm injury and (2) elbow and shoulder injury.

DESIGN

Prospective cohort study.

SETTING

Minor League Baseball (MiLB) from 2013 to 2019.

PATIENTS OR OTHER PARTICIPANTS

Pitchers in MiLB.

MAIN OUTCOME MEASURE(S)

Pitchers were followed for the entire MiLB season, and athlete-exposures and injuries were recorded. Risk ratios and risk difference were calculated between starting and relieving MiLB pitchers. A Cox survival analysis was then performed in relation to time to arm injury between starting and relieving MiLB pitchers. Subgroup analyses were conducted for elbow and shoulder injuries.

RESULTS

A total of 297 pitchers were included, with 85 270 player-days recorded. The incidence of arm injuries was 11.4 per 10 000 athlete-exposures. Starting pitchers demonstrated a greater risk ratio (1.2 [95% CI = 1.1, 1.3]), risk difference (13.6 [95% CI = 5.6, 21.6]), and hazard of arm injury (2.4 [95% CI = 1.5, 4.0]) than relief pitchers. No differences were observed for the hazard of elbow injury between starting and relief pitchers (1.9; 95% CI = 0.8, 4.2). Starting pitchers had a greater hazard of shoulder injury than relief pitchers (3.8 [95% CI = 2.0, 7.1]).

CONCLUSIONS

Starting pitchers displayed a 2.4 times greater hazard of arm injury than relief pitchers. Subgroup analyses indicated that starters exhibited a greater hazard of shoulder injury than relievers, but no differences occurred for the hazard of elbow injury. However, due to the wide CIs, these subgroup analyses should be interpreted with caution. Clinicians may need to consider cumulative exposure and fatigue and how these factors relate to different pitching roles when assessing the risk of pitching arm injury.

Unaccounted Workload Factor: Game-Day Pitch Counts in High School Baseball Pitchers-An Observational Study

Background:

Throwing injuries are common in high school baseball. Known risk factors include excessive pitch counts, year-round pitching, and pitching with arm pain and fatigue. Despite the evidence, the prevalence of pitching injuries among high school players has not decreased. One possibility to explain this pattern is that players accumulate unaccounted pitch volume during warm-up and bullpen activity, but this has not yet been examined.

Hypotheses:

Our primary hypothesis was that approximately 30% to 40% of pitches thrown off a mound by high school pitchers during a game-day outing are unaccounted for in current data but will be revealed when bullpen sessions and warm-up pitches are included. Our secondary hypothesis was that there is wide variability among players in the number of bullpen pitches thrown per outing.

Study Design:

Cross-sectional study; Level of evidence, 3.

Methods:

Researchers counted all pitches thrown off a mound during varsity high school baseball games played by 34 high schools in North Central Florida during the 2017 season.

Results:

We recorded 13,769 total pitches during 115 varsity high school baseball starting pitcher outings. The mean ± SD pitch numbers per game were calculated for bullpen activity (27.2 ± 9.4), warm-up (23.6 ±8.0), live games (68.9 ±19.7), and total pitches per game (119.7 ± 27.8). Thus, 42.4% of the pitches performed were not accounted for in the pitch count monitoring of these players. The number of bullpen pitches thrown varied widely among players, with 25% of participants in our data set throwing fewer than 22 pitches and 25% throwing more than 33 pitches per outing.

Conclusion:

In high school baseball players, pitch count monitoring does not account for the substantial volume of pitching that occurs during warm-up and bullpen activity during the playing season. These extra pitches should be closely monitored to help mitigate the risk of overuse injury.