Multidomain concussion symptoms in adolescents: A network analysis.
Jonathan PreszlerLisa ManderinoVanessa Fazio-SumrokShawn R EagleCynthia HollandMichael W CollinsAnthony P KontosPublished in: Applied neuropsychology. Child (2022)
Concussion is a heterogeneous injury involving symptoms and impairment that represent multiple domains (e.g., anxiety, cognitive, vestibular). Network analysis, a modeling technique that estimates relationships among symptoms, provides a statistically sound and clinically practical method for evaluating these interrelationships. The purpose of this study was to examine, using network analysis, relationships among clinical assessments and multidomain symptom report within a sample of adolescent patients following a concussion. Participants included 326 patients (49.7% female) aged 10-21 years presenting to a concussion specialty clinic within 28 days of a diagnosed concussion. Participants completed the Post-Concussion Symptom Scale (PCSS) and Vestibular-Ocular Motor Screening (VOMS) tool at initial visit. Network models were applied to PCSS symptoms initially, and then applied to VOMS and PCSS symptom data together. Dizziness (Expected influence (EI) = 1.10) and sadness (EI = 1.91) were most central (i.e., highest cumulative partial correlations) to the symptom network. Numerous interdomain relationships were supported, including irritability with mental fogginess (edgeweight = 0.12), dizziness with headache (edgeweight = 0.16), and dizziness with vision problems (edgeweight = 0.13). Community analyses resulted in VOMS groupings by domain (e.g., vestibular) and symptom (e.g., dizziness). The findings suggest a more direct focus on symptom interrelationships, such as how dizziness contributes to emotional symptoms, may help guide and better target treatments. Also, results suggest grouping VOMS assessment by symptom (e.g., dizziness) and item (e.g., vestibular-ocular reflex) may better reflect underlying impairments reflected by these symptom-item combinations.
Keyphrases
- network analysis
- patient reported
- mild traumatic brain injury
- end stage renal disease
- mental health
- sleep quality
- young adults
- ejection fraction
- chronic kidney disease
- newly diagnosed
- healthcare
- prognostic factors
- peritoneal dialysis
- machine learning
- depressive symptoms
- patient reported outcomes
- artificial intelligence
- optic nerve