Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion. (1st December 2016)

• Record Type: Journal Article
• Authors: Ransom, Danielle M.; Burns, Alison R.; Youngstrom, Eric A.; Vaughan, Christopher G.; Sady, Maegan D.; Gioia, Gerard A.
• Editors: Woodard, John L.; Seidman, Larry J.; Stout, Julie C.
• Is Part Of: Journal of the International Neuropsychological Society. Volume 22:Number 10(2016)
• Page Start: 1038
• Page End: 1049
• Publication Date: 2016-12-01
• Subjects: Brain injury, -- Post-concussion symptoms, -- Pediatrics, -- Receiver operating characteristic, -- Academics, -- Evidence based practice; Neuropsychology -- Periodicals; 616.8
• DOI: 10.1017/S1355617716000916
• Languages: English
• ISSNs: 1355-6177

Abstract

Abstract: Objectives: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems.Methods: Participants included 142 students diagnosed with concussion (age: M =14.95; SD =1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems.Results: Latent class analysis categorized participants into "high" (44%) and "low" (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance ( ps <.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems.Conclusions: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. ( JINS, 2016, 22, 1038–1049)