Blood biomarkers on admission in acute traumatic brain injury: Relations to severity, CT findings and care path in the CENTER-TBI study

Abstract

Background: Serum biomarkers may inform and improve care in traumatic brain injury (TBI). We aimed to correlate serum biomarkers with clinical severity, care path and imaging abnormalities in TBI, and explore their incremental value over clinical characteristics in predicting computed tomographic (CT) abnormalities.

Methods: We analyzed six serum biomarkers (S100B, NSE, GFAP, UCH-L1, NFL and t-tau) obtained <24 h post-injury from 2867 patients with any severity of TBI in the Collaborative European NeuroTrauma Effectiveness Research (CENTER-TBI) Core Study, a prospective, multicenter, cohort study. Univariable and multivariable logistic regression analyses were performed. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals.

Findings: All biomarkers scaled with clinical severity and care path (ER only, ward admission, or ICU), and with presence of CT abnormalities. GFAP achieved the highest discrimination for predicting CT abnormalities (AUC 0•89 [95%CI: 0•87-0•90]), with a 99% likelihood of better discriminating CT-positive patients than clinical characteristics used in contemporary decision rules. In patients with mild TBI, GFAP also showed incremental diagnostic value: discrimination increased from 0•84 [95%CI: 0•83-0•86] to 0•89 [95%CI: 0•87-0•90] when GFAP was included. Results were consistent across strata, and injury severity. Combinations of biomarkers did not improve discrimination compared to GFAP alone.

Interpretation: Currently available biomarkers reflect injury severity, and serum GFAP, measured within 24 h after injury, outperforms clinical characteristics in predicting CT abnormalities. Our results support the further development of serum GFAP assays towards implementation in clinical practice, for which robust clinical assay platforms are required.

Funding: CENTER-TBI study was supported by the European Union 7th Framework program (EC grant 602150).

Keywords: Biomarkers; Clinical decision rule; Computerized tomography; Diagnostic; GFAP; Injury severity; Serum; Traumatic brain injury.

Fig. 1

Flow chart for biomarker cohort in the CENTER TBI core study.

Fig. 2

Correlation plots displaying associations between biomarkers in each stratum. The diagonal part with the name of the biomarker contains the distribution plot specific for the log-transformed biomarker. Scatter plots of correlations between biomarkers are presented below the diagonal, and Spearman correlation coefficients above the diagonal. The font size is indicative of the strength of correlation.

Fig. 3

Biomarker values by stratum and by clinical severity, differentiated for the absence (blue) or presence (pink) of traumatic intracranial CT abnormalities.P-values of the Mann–Whitney U tests are presented in the Supplemental material, Table 11.

Fig. 4

Heat map demonstrating the discriminative ability of single biomarkers in comparison to a regression model that includes clinical characteristics contained in CT decision rules. The heat map summarizes the percentage of bootstrap replicates in which the model with the biomarker outperforms (higher c-statistic) the model with CT decision rule variables. The lower number of positive replicates for GFAP in the ER stratum may be due to lower number of events in this stratum (86/636 CT positive).

Fig. 5

Incremental discriminative ability of biomarkers to predict CT positivity. Plots show the difference in Area under the ROC curve (AUC) with 95% confidence intervals (bars) of logistic regression models combining age, time interval (injury to needle time) as well as clinical parameters included in current CT rules with and without biomarkers. Panels are presented for the overall sample (n=2867), according to stratum (ER, Admission, ICU), and for the mild (GCS 13–15; GCS 13–14; GCS 15) groups. Six biomarkers are considered separately and in combination (“all”). The dotted line indicates the predictive value of the clinical parameters and serves as a reference. The absolute values are presented in the Supplementary material, Table S7.

Fig. 6

Reclassification plots illustrating superior performance of GFAP compared to clinical characteristics in predicting traumatic intracranial CT abnormalities. GFAP is added to a logistic regression model that includes clinical parameters from current CT rules. The model with GFAP (y-axis), is compared to the model without GFAP (x-axis). Panels are presented for the overall sample (n=2867), for the strata (ER, Admission, ICU), and for the mild (GCS 13–15), and GCS 15 subgroups. Black dots represent patients with a negative CT scan, red crosses represent patients with a positive CT scan. The percentage of correct reclassification, indicated with green (higher probability of CT positivity if CT is positive, and lower probability of CT positivity if CT is negative), is displayed in the top of each plot.