Lauren L. Czerniak

Sport-related concussion (SRC) is one of the most complex injuries in sports medicine, and many athletes experience one or multiple SRCs over their collegiate career. However, from the start to the end of an athlete's collegiate career, it remains unclear which methods can accurately predict the change, if any, in an athlete's reported symptom burden, cognitive status, and risk of psychological distress. It is also unclear which factors (e.g., number of concussions) have the greatest influence on this change. We consider 3201 (1668 male, 1533 female) collegiate varsity athletes from the Concussion Assessment, Research, and Education (CARE) Consortium. Using five machine learning methods, we predict the change in athletes' reported symptom burden (i.e., Sport Concussion Assessment Tool [SCAT]), cognitive status (i.e., Standardized Assessment of Concussion [SAC]), and risk of psychological distress (i.e., Brief Symptom Inventory 18 [BSI-18]) over their collegiate careers. All machine learning methods outperform a simple model that assumes no change with respect to mean squared error (i.e., machine learning helps avoid large prediction errors). We find that the initial baseline evaluation score is of greatest importance across all metrics of interest (e.g., SCAT/SAC/BSI-18). We also find that when an athlete has a poorer performance on the initial baseline evaluation, the prediction for the change in score is often larger and change for the better. This research provides insights on an athlete's change in critical areas of functioning over their collegiate career, and how machine learning can help improve the prediction of this change.

Background and Objective Computer-based neurocognitive tests are widely used in sport-related concussion management, but the performance of these tests is not well understood in the participant population with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD). This research estimates the sensitivity and specificity performance of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) computer-based neurocognitive test in identifying concussion in this population. Methods Study participants consisted of collegiate university athletes and military service academy cadets from the National Collegiate Athletic Association-Department of Defense CARE Consortium who completed the ImPACT test between 2014 and 2021. Participants who self-identified as belonging to one of the subgroups of interest (ADHD with or without LD [ADHD:LD+/−], LD with or without ADHD [LD:ADHD+/−], ADHD and/or LD [ADHD a/o LD]) and completed a baseline (1874 ADHD:LD+/−, 779 LD:ADHD+/−, 2338 ADHD a/o LD) or 24–48 h post-concussion (175 ADHD:LD+/−, 77 LD:ADHD+/−, 216 ADHD a/o LD) ImPACT assessment were included. Sensitivity and specificity were calculated using a normative data method and three machine learning classification methods: logistic regression, classification and regression tree, and random forest. Results Using the four methods, participants with ADHD:LD+/− had sensitivities that ranged from 0.474 to 0.697, and specificities that ranged from 0.538 to 0.686. Participants with LD:ADHD+/− had sensitivities that ranged from 0.455 to 0.688, and specificities that ranged from 0.456 to 0.588. For participants with ADHD a/o LD, sensitivities ranged from 0.542 to 0.755, and specificities ranged from 0.451 to 0.724. Conclusions For all subgroups and analytical methods, the results illustrate sensitivity and specificity values below typically accepted levels indicative of clinical utility. These findings support that using ImPACT alone may be insufficient to inform concussion diagnoses and encourages the use of a multi-dimensional concussion assessment.

Background To optimally care for concussed individuals, a multi-dimensional approach is critical and a key component of this assessment in the athletic environment is computer-based neurocognitive testing. However, there continues to be concerns about the reliability and validity of these testing tools. The purpose of this study was to determine the sensitivity and specificity of three common computer-based neurocognitive tests (Immediate Post-Concussion Assessment and Cognitive Testing [ImPACT], CNS Vital Signs, and CogState Computerized Assessment Tool [CCAT]), to provide guidance on their clinical utility. Methods This study analyzed assessments from a cohort of collegiate athletes and non-varsity cadets from the NCAA-DoD CARE Consortium. The data were collected from 2014–2018. Study participants were divided into two testing groups [concussed, n = 1414 (baseline/24–48 h) and healthy, n = 8305 (baseline/baseline)]. For each test type, change scores were calculated for the components of interest. Then, the Normative Change method, which used normative data published in a similar cohort, and the Reliable Change Index (RCI) method were used to determine if the change scores were significant. Results Using the Normative Change method, ImPACT performed best with an 87.5%-confidence interval and 1 number of components failed (NCF; sensitivity = 0.583, specificity = 0.625, F1 = 0.308). CNS Vital Signs performed best with a 90%-confidence interval and 1 NCF (sensitivity = 0.587, specificity = 0.532, F1 = 0.314). CCAT performed best when using a 75%-confidence interval and 2 NCF (sensitivity = 0.513, specificity = 0.715, F1 = 0.290). When using the RCI method, ImPACT performed best with an 87.5%-confidence interval and 1 NCF (sensitivity = 0.626, specificity = 0.559, F1 = 0.297). Conclusion When considering all three computer-based neurocognitive tests, the overall low sensitivity and specificity results provide additional evidence for the use of a multi-dimensional assessment for concussion diagnosis, including symptom evaluation, postural control assessment, neuropsychological status, and other functional assessments.

Sensation-seeking, or the need for novel and exciting experiences, is thought to play a role in sport-related concussion (SRC), yet much remains unknown regarding these relationships and, more importantly, how sensation-seeking influences SRC risk. The current study assessed sensation-seeking, sport contact level, and SRC history and incidence in a large sample of NCAA collegiate athletes. Data included a full study sample of 22,374 baseline evaluations and a sub-sample of 2037 incident SRC. Independent samples t-test, analysis of covariance, and hierarchical logistic regression were constructed to address study hypotheses. Results showed that (1) among participants without SRC, sensation-seeking scores were higher in athletes playing contact sports compared to those playing limited- or non-contact sports (p < 0.001, R² = 0.007, η²_p = 0.003); (2) in the full study sample, a one-point increase in sensation-seeking scores resulted in a 21% greater risk of prior SRC (OR = 1.212; 95% CI: 1.154–1.272), and in the incident SRC sub-sample, a 28% greater risk of prior SRC (OR = 1.278; 95% CI: 1.104–1.480); (3) a one-point increase in sensation-seeking scores resulted in a 12% greater risk of incident SRC among the full study sample; and (4) sensation-seeking did not vary as a function of incident SRC (p = 0.281, η²_p = 0.000). Our findings demonstrate the potential usefulness of considering sensation-seeking in SRC management.