Prediction of risk of prolonged post-concussion symptoms: Derivation and validation of the TRICORDRR (Toronto Rehabilitation Institute Concussion Outcome Determination and Rehab Recommendations) score
PLoS Medicine Aug 12, 2021
Langer LK, Alavinia SM, Lawrence DW, et al. - Findings suggested an association of premorbid psychiatric conditions, pre-injury health system usage, and older age with increased risk of prolonged recovery from concussion. This risk score helps clinicians to estimate a person's chances of needing treatment more than 6 months after a concussion.
In a retrospective analysis, data from a cohort study (Ontario Concussion Cohort study, 2008 to 2016; n = 1,330,336) were used, which included all adults with a concussion diagnosis by either a primary care physician or in the emergency department, as well as 2 years of healthcare tracking postinjury (2008 to 2014, n = 587,057).
Females made up approximately 42.4% of the cohort, with individuals aged 18 to 30 making up the largest age group (31.0%).
Nearly 13% (73,122) of the cohort had prolonged post-concussion symptoms (PPCS; defined as 2 or more specialist visits for concussion-related symptoms more than 6 months after injury index date).
Based upon injury index year, total cohort was divided into Derivation (2009 to 2013, n = 417,335) and Validation cohorts (2009 and 2014, n = 169,722).
In the Derivation Cohort, variables like psychiatric disorders, migraines, sleep disorders, demographic factors, and pre-injury healthcare patterns were entered into multivariable logistic regression and CART modeling to calculate PPCS estimates, and the Validation Cohort used a forward selection logistic regression model.
Age > 61 years, bipolar disorder, high pre-injury primary care visits per year, personality disorders, and anxiety and depression were the variables with the highest risk of PPCS derived in the Derivation Cohort.
The area under the curve for the derivation model was 0.79, 0.79 for the Derivation Cohort's bootstrap internal validation, and 0.64 for the Validation model.
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