Limits...
A Preseason Checklist for Predicting Elbow Injury in Little League Baseball Players.

Yukutake T, Kuwata M, Yamada M, Aoyama T - Orthop J Sports Med (2015)

Bottom Line: To investigate the effectiveness of a risk factor checklist for predicting elbow injury in Little League baseball players during 1 season.Six checklist items associated with a medical history of throwing injury, pitch volume, and arm fatigue were found to be significant.The ability to predict which Little League baseball players are predisposed to elbow injury allows parents and coaches to initiate preventive measures in those players prior to and during the baseball season, which could lead to fewer elbow injuries.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Medicine, Human Health Sciences, Kyoto University, Kyoto, Japan.

ABSTRACT

Background: Despite pitch count limits, the incidence of Little League elbow is increasing. A risk-evaluation tool capable of predicting which players are predisposed to throwing injury could potentially prevent injuries.

Purpose: To investigate the effectiveness of a risk factor checklist for predicting elbow injury in Little League baseball players during 1 season. The hypothesis was that a preseason risk-evaluation checklist could predict which players were predisposed to elbow injury.

Study design: Case-control study; Level of evidence, 3.

Methods: A preseason risk-evaluation checklist was distributed to Little League baseball teams in Japan. Six months later, a follow-up questionnaire was mailed to determine injuries sustained during the season. Logistic regression analysis was performed, assigning presence or absence of elbow injury during the season as the dependent variable, and an injury risk score (IRS) was developed based on the statistically significant variables. Receiver operating characteristic (ROC) curve analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS.

Results: Data from 389 Little League players were analyzed. Among them, 53 players experienced an elbow injury requiring medical treatment during the season. Six checklist items associated with a medical history of throwing injury, pitch volume, and arm fatigue were found to be significant. Responses to the items could predict the players who were susceptible to injury during the season, with a two-thirds cutoff value for a 6-item checklist (area under the curve, 0.810; sensitivity, 0.717; specificity, 0.771).

Conclusion: Results from a 6-item preseason checklist can predict which Little League players are to sustain an elbow injury by the end of the season.

Clinical relevance: The ability to predict which Little League baseball players are predisposed to elbow injury allows parents and coaches to initiate preventive measures in those players prior to and during the baseball season, which could lead to fewer elbow injuries.

No MeSH data available.


Related in: MedlinePlus

Receiver operating characteristic (ROC) curve analysis for injury risk score (IRS). ROC analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS, assigning occurrence of elbow injury as a state variable. We were able to predict the players who were injured during the season with a two-thirds cutoff value for a 6-item checklist (area under the curve [AUC], 0.810; sensitivity, 0.717; specificity, 0.771).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2 - License 3
getmorefigures.php?uid=PMC4555587&req=5

fig3-2325967114566788: Receiver operating characteristic (ROC) curve analysis for injury risk score (IRS). ROC analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS, assigning occurrence of elbow injury as a state variable. We were able to predict the players who were injured during the season with a two-thirds cutoff value for a 6-item checklist (area under the curve [AUC], 0.810; sensitivity, 0.717; specificity, 0.771).

Mentions: Using the 6 variables that were significant in the logistic regression analysis, we calculated the IRS going up to 6 points. In the injured player group, the mean IRS was 3.44 ± 0.64, whereas that in the noninjured player group was 1.27 ± 0.67 (P < .01) (Figure 2). The ROC curve had a relatively high AUC for the IRS (0.810), and we determined that a two-thirds cutoff point had a sensitivity of 0.717 and a specificity of 0.771 (Figure 3). Among players with an IRS of 3 to 6 (n = 115), 38 players had been injured during the season (injury rate, 33.0%). Among players with an IRS of 0 to 2 (n = 274), 15 players (injury rate, 5.5%) had been injured (Figure 2).


A Preseason Checklist for Predicting Elbow Injury in Little League Baseball Players.

Yukutake T, Kuwata M, Yamada M, Aoyama T - Orthop J Sports Med (2015)

Receiver operating characteristic (ROC) curve analysis for injury risk score (IRS). ROC analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS, assigning occurrence of elbow injury as a state variable. We were able to predict the players who were injured during the season with a two-thirds cutoff value for a 6-item checklist (area under the curve [AUC], 0.810; sensitivity, 0.717; specificity, 0.771).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4555587&req=5

fig3-2325967114566788: Receiver operating characteristic (ROC) curve analysis for injury risk score (IRS). ROC analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS, assigning occurrence of elbow injury as a state variable. We were able to predict the players who were injured during the season with a two-thirds cutoff value for a 6-item checklist (area under the curve [AUC], 0.810; sensitivity, 0.717; specificity, 0.771).
Mentions: Using the 6 variables that were significant in the logistic regression analysis, we calculated the IRS going up to 6 points. In the injured player group, the mean IRS was 3.44 ± 0.64, whereas that in the noninjured player group was 1.27 ± 0.67 (P < .01) (Figure 2). The ROC curve had a relatively high AUC for the IRS (0.810), and we determined that a two-thirds cutoff point had a sensitivity of 0.717 and a specificity of 0.771 (Figure 3). Among players with an IRS of 3 to 6 (n = 115), 38 players had been injured during the season (injury rate, 33.0%). Among players with an IRS of 0 to 2 (n = 274), 15 players (injury rate, 5.5%) had been injured (Figure 2).

Bottom Line: To investigate the effectiveness of a risk factor checklist for predicting elbow injury in Little League baseball players during 1 season.Six checklist items associated with a medical history of throwing injury, pitch volume, and arm fatigue were found to be significant.The ability to predict which Little League baseball players are predisposed to elbow injury allows parents and coaches to initiate preventive measures in those players prior to and during the baseball season, which could lead to fewer elbow injuries.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Medicine, Human Health Sciences, Kyoto University, Kyoto, Japan.

ABSTRACT

Background: Despite pitch count limits, the incidence of Little League elbow is increasing. A risk-evaluation tool capable of predicting which players are predisposed to throwing injury could potentially prevent injuries.

Purpose: To investigate the effectiveness of a risk factor checklist for predicting elbow injury in Little League baseball players during 1 season. The hypothesis was that a preseason risk-evaluation checklist could predict which players were predisposed to elbow injury.

Study design: Case-control study; Level of evidence, 3.

Methods: A preseason risk-evaluation checklist was distributed to Little League baseball teams in Japan. Six months later, a follow-up questionnaire was mailed to determine injuries sustained during the season. Logistic regression analysis was performed, assigning presence or absence of elbow injury during the season as the dependent variable, and an injury risk score (IRS) was developed based on the statistically significant variables. Receiver operating characteristic (ROC) curve analysis was conducted to determine the predictive validity of the checklist and the optimal cutoff IRS.

Results: Data from 389 Little League players were analyzed. Among them, 53 players experienced an elbow injury requiring medical treatment during the season. Six checklist items associated with a medical history of throwing injury, pitch volume, and arm fatigue were found to be significant. Responses to the items could predict the players who were susceptible to injury during the season, with a two-thirds cutoff value for a 6-item checklist (area under the curve, 0.810; sensitivity, 0.717; specificity, 0.771).

Conclusion: Results from a 6-item preseason checklist can predict which Little League players are to sustain an elbow injury by the end of the season.

Clinical relevance: The ability to predict which Little League baseball players are predisposed to elbow injury allows parents and coaches to initiate preventive measures in those players prior to and during the baseball season, which could lead to fewer elbow injuries.

No MeSH data available.


Related in: MedlinePlus