Association of Preinjury Medical Diagnoses With Pediatric Persistent Postconcussion Symptoms in Electronic Health Records

Abstract

Objective: To identify risk factors and generate hypotheses for pediatric persistent postconcussion symptoms (PPCS). Setting: A regional healthcare system in the Southeastern United States. Participants: An electronic health recordbased algorithm was developed and validated to identify PPCS cases and controls from an institutional database of more than 2.8 million patients. PPCS cases (n = 274) were patients aged 5 to 18 years with PPCS-related diagnostic codes or with PPCS key words identified by natural language processing of clinical notes. Age, sex, and year of index event-matched controls (n = 1096) were patients with mild traumatic brain injury codes only. Patients with moderate or severe traumatic brain injury were excluded. All patients used our healthcare system at least 3 times 180 days before their injury. Design: Case-control study. Main Measures: The outcome was algorithmic classification of PPCS. Exposures were all preinjury medical diagnoses assigned at least 180 days before the injury. Results: Cases and controls both had a mean of more than 9 years of healthcare system use preinjury. Of 221 preinjury medical diagnoses, headache disorder was associated with PPCS after accounting for multiple testing (odds ratio [OR] = 2.9; 95% confidence interval [CI]: 1.6-5.0; P = 2.1e-4). Six diagnoses were associated with PPCS at a suggestive threshold for statistical significance (false discovery rate P < .10): gastritis/duodenitis (OR = 2.8; 95% CI: 1.6-5.1; P = 5.0e-4), sleep disorders (OR = 2.3; 95% CI: 1.4-3.7; P = 7.4e-4), abdominal pain (OR = 1.6; 95% CI: 1.2-2.2; P = 9.2e-4), chronic sinusitis (OR = 2.8; 95% CI: 1.5-5.2; P = 1.3e-3), congenital anomalies of the skin (OR = 2.9; 95% CI: 1.5-5.5; P = 1.9e-3), and chronic pharyngitis/nasopharyngitis (OR = 2.4; 95% CI: 1.4-4.3; P = 2.5e-3). Conclusions: These results support the strong association of preinjury headache disorders with PPCS. An

Natural language processing in clinical neuroscience and psychiatry: A review

“…Dai et al ( 100 ) tackled this problem from a generic standpoint, demonstrating that cohort selection of longitudinal patient records can be formulated as a multiple instance learning task. Candidate identification can be applied to very different medical topics: surgical intervention for drug-resistant pediatric epilepsy ( 101 ), risk factors for pediatric post-concussion symptoms ( 102 ), error reduction in determining eligibility for intravenous thrombolytic therapy ( 103 ).…”