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OBJECTIVE: to 1) evaluate an injury risk model that included modifiable and non-modifiable 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. Non-modifiable variables included: arm injury history, professional experience, arm dominance, year, humeral torsion. Modifiable variables included: BMI, pitch count, total range of motion, and horizontal adduction. We compared modifiable, non-modifiable, 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: 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 ROM (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. CONCLUSIONS: Arm injury risk is influenced by modifiable and non-modifiable 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 non-modifiable risk factors.

Original publication




Journal article


J Orthop Sports Phys Ther

Publication Date



1 - 42


Calibration, Discrimination, Humeral Torsion, Internal Validation, Prognostic Model