Improving predictor selection for injury modelling methods in male footballers

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.