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 modi...
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| Format: | Article |
| Language: | English |
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North American Sports Medicine Institute
2022-12-01
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| Series: | International Journal of Sports Physical Therapy |
| Online Access: | https://doi.org/10.26603/001c.39741 |
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| _version_ | 1825196967753416704 |
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| author | Garrett Bullock Charles Thigpen Gary Collins Nigel Arden Thomas Noonan Michael Kissenberth Ellen Shanley |
| author_facet | Garrett Bullock Charles Thigpen Gary Collins Nigel Arden Thomas Noonan Michael Kissenberth Ellen Shanley |
| author_sort | Garrett Bullock |
| collection | DOAJ |
| description | # 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, R^2^: 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; R^2^: 0.42; calibration: 1.25 \[1.16, 1.34]), as did a separate shoulder model (RMSE: 17.9; R^2^: 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 |
| format | Article |
| id | doaj-art-d54648027780457fb12dea017c138418 |
| institution | Kabale University |
| issn | 2159-2896 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | North American Sports Medicine Institute |
| record_format | Article |
| series | International Journal of Sports Physical Therapy |
| spelling | doaj-art-d54648027780457fb12dea017c1384182025-02-11T20:28:21ZengNorth American Sports Medicine InstituteInternational Journal of Sports Physical Therapy2159-28962022-12-01177Development of an Injury Burden Prediction Model in Professional Baseball PitchersGarrett BullockCharles ThigpenGary CollinsNigel ArdenThomas NoonanMichael KissenberthEllen Shanley# 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, R^2^: 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; R^2^: 0.42; calibration: 1.25 \[1.16, 1.34]), as did a separate shoulder model (RMSE: 17.9; R^2^: 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 2https://doi.org/10.26603/001c.39741 |
| spellingShingle | Garrett Bullock Charles Thigpen Gary Collins Nigel Arden Thomas Noonan Michael Kissenberth Ellen Shanley Development of an Injury Burden Prediction Model in Professional Baseball Pitchers International Journal of Sports Physical Therapy |
| title | Development of an Injury Burden Prediction Model in Professional Baseball Pitchers |
| title_full | Development of an Injury Burden Prediction Model in Professional Baseball Pitchers |
| title_fullStr | Development of an Injury Burden Prediction Model in Professional Baseball Pitchers |
| title_full_unstemmed | Development of an Injury Burden Prediction Model in Professional Baseball Pitchers |
| title_short | Development of an Injury Burden Prediction Model in Professional Baseball Pitchers |
| title_sort | development of an injury burden prediction model in professional baseball pitchers |
| url | https://doi.org/10.26603/001c.39741 |
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