Biomarker-informed prediction of neurosurgical intervention in pediatric traumatic brain injury: a proof-of-concept study.

Abstract

Objective: Emerging traumatic brain injury (TBI) guidelines increasingly recognize the role of blood-based biomarkers in improving diagnostic and triage accuracy, yet their integration into pediatric care remains limited and underexplored. This study examined the performance of a blood-based biomarker panel in predicting which pediatric patients with TBI received neurosurgical intervention (NSI).

Methods: This was a prospective cohort study of 425 children (aged 0-17 years) treated at a pediatric tertiary hospital between March 2017 and June 2021 with nonpenetrating TBI (Glasgow Coma Scale [GCS] score 3-15). Standard of care was rendered. NSI included craniotomy, craniectomy, external ventricular drain (EVD) placement, and intracranial pressure (ICP) monitor placement. Blood samples collected within 24 hours of injury were measured for plasma glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light (NfL), and total tau (t-tau) with the Quanterix Simoa platform, as well as osteopontin (OPN) quantification via ELISA. Logistic regression and area under the curve (AUC) analysis with 95% confidence intervals (CI) were used to measure predictive discrimination.

Results: NSI occurred in 14.35% of patients. These children were younger (mean age 6.0 vs 10.1 years) with more severe injury (median GCS score 9.0 vs 15.0) than those who did not require NSI. All median biomarker concentrations were higher in NSI compared with non-NSI patients (p < 0.001). After age and GCS score were controlled for, GFAP significantly predicted NSI (AUC 0.83, 95% CI 0.75-0.90), defining the best predictive model for NSI.

Conclusions: Blood-based biomarkers predicted NSI following pediatric TBI, independent of age and admission GCS score. These findings support further prospective trials to determine whether integrating blood-based biomarkers into clinical triage algorithms and TBI guidelines can improve neurosurgical decision-making and patient outcomes.