Clinical Mental Health Measures and Prediction of Postconcussion Musculoskeletal Injury

Sport Type and Risk of Subsequent Injury in Collegiate Athletes Following Concussion: a LIMBIC MATARS Consortium Investigation

OBJECTIVE

To investigate the association between sport type (collision, contact, non-contact) and subsequent injury risk following concussion in collegiate athletes.

MATERIALS AND METHODS

This retrospective chart review of 248 collegiate athletes with diagnosed concussions (age: 20.0 ± 1.4 years; height: 179.6 ± 10.9 cm; mass: 79.0 ± 13.6 kg, 63% male) from NCAA athletic programs (n = 11) occurred between the 2015-2020 athletic seasons. Acute injuries that occurred within six months following concussion were evaluated. Subsequent injuries were grouped by lower extremity, upper extremity, trunk, or concussion. The independent variable was sport type: collision, contact, non-contact. A Cox proportional hazard model was used to assess the risk of subsequent injury between sport types.

RESULTS

Approximately 28% (70/248) of athletes sustained a subsequent acute injury within six months post-concussion. Collision sport athletes had a significantly higher risk of sustaining any injury (HR: 0.41, p < 0.001, 95% CI: 0.28, 0.62), lower extremity (HR: 0.55, p = 0.04, 95% CI: 0.32, 0.97), and upper extremity (HR: 0.41, p = 0.01, 95% CI: 0.20, 0.81) injuries following concussion. No differences between sport types were observed for other injuries.

CONCLUSION

Collision sport athletes had a higher rate of any subsequent injury, lower, and upper extremity injuries following concussion. Future research should focus on sport-specific secondary injury prevention efforts.

Integrative data analysis to identify persistent post-concussion deficits and subsequent musculoskeletal injury risk: project structure and methods

Concussions are a serious public health problem, with significant healthcare costs and risks. One of the most serious complications of concussions is an increased risk of subsequent musculoskeletal injuries (MSKI). However, there is currently no reliable way to identify which individuals are at highest risk for post-concussion MSKIs. This study proposes a novel data analysis strategy for developing a clinically feasible risk score for post-concussion MSKIs in student-athletes. The data set consists of one-time tests (eg, mental health questionnaires), relevant information on demographics, health history (including details regarding the concussion such as day of the year and time lost) and athletic participation (current sport and contact level) that were collected at a single time point as well as multiple time points (baseline and follow-up time points after the concussion) of the clinical assessments (ie, cognitive, postural stability, reaction time and vestibular and ocular motor testing). The follow-up time point measurements were treated as individual variables and as differences from the baseline. Our approach used a weight-of-evidence (WoE) transformation to handle missing data and variable heterogeneity and machine learning methods for variable selection and model fitting. We applied a training-testing sample splitting scheme and performed variable preprocessing with the WoE transformation. Then, machine learning methods were applied to predict the MSKI indicator prediction, thereby constructing a composite risk score for the training-testing sample. This methodology demonstrates the potential of using machine learning methods to improve the accuracy and interpretability of risk scores for MSKI.

Self-Reported Mental Health Measures of Incoming Collegiate Student-Athletes With a History of COVID-19

BACKGROUND

People with a history of COVID-19 may experience persistent neuropsychological disruptions such as lower satisfaction with life, depression, and anxiety. Although student-athletes are at low risk for severe COVID-19 complications, the effect of COVID-19 on mental health has not been elucidated.

OBJECTIVE

To compare patient-reported mental health outcomes for incoming collegiate athletes with (COVID+) or without (COVID-) a history of COVID-19.

DESIGN

Case-control study.

SETTING

Laboratory.

PATIENTS OR OTHER PARTICIPANTS

A total of 178 student-athletes, consisting of 79 in the COVID+ group (44.3%; age = 18.90 ± 0.16 years) and 99 in the COVID- group (55.6%; age = 18.95 ± 0.16 years).

MAIN OUTCOME MEASURE(S)

Participants completed the Satisfaction With Life Scale (SWLS), the Hospital Anxiety and Depression Scale (HADS), and the State-Trait Anxiety Inventory (STAI). Unadjusted 1-way analyses of variance were conducted across all patient-reported outcomes. Analyses of covariance were calculated to determine the interaction of COVID-19 group, sex, and race and ethnicity on outcomes. Post hoc Bonferroni testing was performed to identify specific differences between groups. A χ2 analysis was computed to compare the number of athletes in each group who met the standard clinical cut points.

RESULTS

We observed a between-groups difference for HADS depression (P = .047), whereby athletes in the COVID+ group had higher ratings (2.86 ± 0.26). We found group differences for the SWLS (P = .02), HADS anxiety (P = .003), and STAI state anxiety (P = .01) such that all scores were higher for the COVID+ group in the adjusted model. Post hoc testing revealed that female student-athletes in the COVID+ group had worse HADS anxiety (P = .01) and STAI trait anxiety (P = .002) scores than individuals in all other groups. We did not demonstrate differences between groups in the percentage of responses below established diagnostic thresholds.

CONCLUSIONS

Incoming collegiate student-athletes who reported a previous COVID-19 diagnosis displayed higher depression scores, suggesting that clinicians may need to provide appropriate identification and referral for mental health conditions. However, we were encouraged that most participants, regardless of a history of COVID-19 diagnosis, had mental health scores that did not exceed established diagnostic threshold values.