We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Development of an Injury Burden Prediction Model in Professional Baseball Pitchers.
- Authors
Bullock, Garrett; Thigpen, Charles; Collins, Gary; Arden, Nigel; Noonan, Thomas; Kissenberth, Michael; Shanley, Ellen
- Abstract
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]).
- Subjects
BASEBALL; RANGE of motion of joints; THROWING (Sports); BASEBALL injuries; DESCRIPTIVE statistics; RESEARCH funding; PREDICTION models; SENSITIVITY &; specificity (Statistics); LONGITUDINAL method
- Publication
International Journal of Sports Physical Therapy, 2022, Vol 17, Issue 7, p1358
- ISSN
2159-2896
- Publication type
Article
- DOI
10.26603/001c.39741