The CBI-M framework for characterization of traumatic brain injury: Evaluation of performance using data from the CENTER-TBI and TRACK-TBI prospective cohort studies.

backgroundThe CBI-M Framework, incorporating Clinical, Biomarker, Imaging, and Modifier pillars, was recently proposed to provide a comprehensive characterization of patients with acute traumatic brain injury (TBI). Such detailed characterization reflects the clinical and biological heterogeneity of TBI, and is considered essential to inform appropriate clinical management. Still unclear is the extent to which CBI-M captures clinically relevant heterogeneity in relation to diagnosis, clinical care pathways, and longer-term outcome. Prior to broad implementation into clinical practice and research, its diagnostic and prognostic performance needs to be ascertained. In this study, we aim to explore how CBI-M and its constituent pillars relate to diagnosis, clinical care pathways, and outcome in two large contemporary TBI cohorts. METHODS AND

findingsWe analyzed data of patients with acute TBI enrolled in the European-based CENTER-TBI (N = 4,509) and US-based TRACK-TBI (N = 2,552) prospective observational studies. We assessed associations of the CBI-M Framework, its pillars, and pillar components with key clinical endpoints: diagnosis (traumatic intracranial injury on computed tomography); care pathways (hospital admission, intensive care admission, intracranial pressure monitoring, and major cranial surgery); and outcomes at 6 months post-injury (mortality, Glasgow Outcome Scale-Extended (GOSE), and health-related quality of life (HRQoL)). Discriminatory performance was calculated using the AUC and explained variability expressed with Nagelkerke's pseudo-R2. Prognostic performance was compared to the established IMPACT-Lab and CRASH-CT prediction models in their respective target populations. For diagnostic and care path endpoints full CBI-M models achieved excellent discrimination (AUC CENTER-TBI: from 0.83 to 0.95; TRACK-TBI: from 0.88 to 0.98), and high proportions of explained variability (pseudo-R2 CENTER-TBI: from 0.41 to 0.72; TRACK-TBI: from 0.55 to 0.80). Contributions of individual pillars varied by endpoint, with the Biomarker pillar contributing most for CT-defined abnormalities and the Imaging pillar to major cranial surgery. For mortality and unfavorable outcome (GOSE ≤ 4) similarly strong performance was observed: CENTER-TBI: AUC 0.89 (95% Confidence intervals (CI) [0.88,0.90]) and 0.92 (95% CI [0.91,0.94]) respectively; pseudo-R2 0.53 (95% CI [0.50,0.57]) and 0.55 (95% CI [0.51,0.59]) respectively; TRACK-TBI: AUC 0.91 (95% CI [0.89,0.93]) and 0.95 (95% CI [0.93,0.97]) respectively; pseudo-R2 0.57 (95% CI [0.52,0.62] and 0.57 (95% CI [0.50, 0.64] respectively), whereas performance for HRQoL was modest. Compared to the IMPACT-Lab and CRASH-CT models in their respective target populations, pseudo-R2 values suggest that the full CBI-M model was more strongly associated with 6-month mortality and unfavorable outcomes. Methodological limitations include the restriction to analysis of data from high-income settings, limiting generalizability, the absence of three elements of the modifier pillar in the datasets accessed, and risk of possible reverse causality in some analyses.

conclusionsCBI-M provides a robust multimodal characterization of acute TBI that aligns closely with clinical decision-making and outcomes. Further prospective validation and optimization are justified and needed to inform implementation of the CBI-M Framework in clinical practice and research.