Optimizing the Combination of Common Clinical Concussion Batteries to Predict Persistent Postconcussion Symptoms in a Prospective Cohort of Concussed Youth

Abstract

Background:

Studies have evaluated individual factors associated with persistent postconcussion symptoms (PPCS) in youth concussion, but no study has combined individual elements of common concussion batteries with patient characteristics, comorbidities, and visio-vestibular deficits in assessing an optimal model to predict PPCS.

Purpose:

To determine the combination of elements from 4 commonly used clinical concussion batteries and known patient characteristics and comorbid risk factors that maximize the ability to predict PPCS.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

We enrolled 198 concussed participants—87 developed PPCS and 111 did not—aged 8 to 19 years assessed within 14 days of injury from a suburban high school and the concussion program of a tertiary care academic medical center. We defined PPCS as a Post-Concussion Symptom Inventory (PCSI) score at 28 days from injury of ≥3 points compared with the preinjury PCSI score—scaled for younger children. Predictors included the individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, 5th Edition (SCAT-5), King-Devick test, and PCSI, in addition to age, sex, concussion history, and migraine headache history. The individual elements of these tests were grouped into interpretable factors using sparse principal component analysis. The 12 resultant factors were combined into a logistic regression and ranked by frequency of inclusion into the combined optimal model, whose predictive performance was compared with the VVE, initial PCSI, and the current existing predictive model (the Predicting and Prevention Postconcussive Problems in Pediatrics (5P) prediction rule) using the area under the receiver operating characteristic curve (AUC).

Results:

A cluster of 2 factors (SCAT-5/PCSI symptoms and VVE near point of convergence/accommodation) emerged. A model fit with these factors had an AUC of 0.805 (95% CI, 0.661-0.929). This was a higher AUC point estimate, with overlapping 95% CIs, compared with the PCSI (AUC, 0.773 [95% CI, 0.617-0.912]), VVE (AUC, 0.736 [95% CI, 0.569-0.878]), and 5P Prediction Rule (AUC, 0.728 [95% CI, 0.554-0.870]).

Conclusion:

Among commonly used clinical assessments for youth concussion, a combination of symptom burden and the vision component of the VVE has the potential to augment predictive power for PPCS over either current risk models or individual batteries.