Development and internal validation of a humeral torsion prediction model in professional baseball pitchers

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Journal of shoulder and elbow surgery


Background Humeral torsion (HT) has been linked to pitching arm injury risk after controlling for shoulder range of motion. Currently measuring HT uses expensive equipment, which inhibits clinical assessment. Developing an HT predictive model can aid clinical baseball arm injury risk examination. Therefore, the purpose of this study was to develop and internally validate an HT prediction model using standard clinical tests and measures in professional baseball pitchers. Methods An 11-year (2009-2019) prospective professional baseball cohort was used for this study. Participants were included if they were able to participate in all practices and competitions and were under a Minor League Baseball contract. Preseason shoulder range of motion (external rotation [ER], internal rotation [IR], horizontal adduction [HA]) and HT were collected each season. Player age, arm dominance, arm injury history, and continent of origin were also collected. Examiners were blinded to arm dominance. An a priori power analysis determined that 244 players were needed for accurate prediction models. Missing data was low (<3%); thus, a complete case analysis was performed. Model development followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) recommendations. Regression models with restricted cubic splines were performed. Following primary model development, bootstrapping with 2000 iterations were performed to reduce overfitting and assess optimism shrinkage. Prediction model performance was assessed through root mean square error (RMSE), R2, and calibration slope with 95% confidence intervals (CIs). Sensitivity analyses included dominant and nondominant HT. Results A total of 407 professional pitchers (age: 23.2 [standard deviation 2.4] years, left-handed: 17%; arm history prevalence: 21%) participated. Predictors with the highest influence within the model include IR (0.4, 95% CI 0.3, 0.5; P < .001), ER (−0.3, 95% CI −0.4, −0.2; P < .001), HA (0.3, 95% CI 0.2, 0.4; P < .001), and arm dominance (right-handed: −1.9, 95% CI −3.6, −0.1; P = .034). Final model RMSE was 12, R2 was 0.41, and calibration was 1.00 (95% CI 0.94, 1.06). Sensitivity analyses demonstrated similar model performance. Conclusions Every 3° of IR explained 1° of HT. Every 3° of ER explained 1° less of HT, and every 7° of HA explained 1° of HT. Right-handers had 2° less HT. Models demonstrated good predictive performance. This predictive model can be used by clinicians to infer HT using standard clinical test and measures. These data can be used to enhance professional baseball arm injury examination.

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