Project Summary/Abstract
This goal of this project is to design and optimize an equitable and value-based approach to baseline testing
for sports-related concussion by synthesizing machine learning and systems science methods. Concussion,
one of the most common types of traumatic brain injury, afflicts upwards of 3.6 million people annually and is a
major public health issue. Timely and effective concussion management is considered a major factor in
mitigating both short-term and long-term consequences of the injury. Baseline testing is a widely used practice
that provides physicians and athletic trainers a reference point indicating someone’s “normal” performance
across several concussion-specific functional domains. Baseline testing is also a resource-intensive process,
requiring specific expertise in the time-consuming administration of a multi-dimensional concussion
assessment battery; nevertheless, baseline testing is considered essential to the injury management process
for those at elevated risk of concussion, including student-athletes and military personnel. Despite widespread
use of baseline testing, there is a lack of evidence-based guidance on who should be prioritized for baseline
testing in resource-limited environments.
The multidisciplinary research team aims to address this knowledge gap by synthesizing machine learning and
systems science methods with data from the Concussion Assessment, Research, and Education Consortium –
one of the largest multi-site datasets available on sports-related concussion. Specifically, the project aims to
first use interpretable machine learning methods and statistical modeling to estimate the diagnostic utility of
baseline testing in a heterogeneous cohort of student-athletes. Next, the project aims to design a decision-
analytic model that can optimally allocate baseline tests. This model will take into account: (1) personalized
estimates for the diagnostic utility of baseline tests, (2) individualized risk for sport-related concussion, (3)
resource constraints at a given institution, and (4) equity considerations in the allocation of baseline tests. This
research can transform how clinicians, athletic trainers, and other trained medical staff approach baseline
testing and concussion diagnosis for those who may be under-represented in the development of existing
clinical guidelines, leading to more timely and accurate diagnosis of concussion. Moreover, resources saved
through an efficient allocation of baseline tests can be reallocated to other valuable tasks performed by
specialized medical personnel, including other tasks along the concussion care continuum, heat illnesses
prevention, and COVID-19 screening.
