Interleukin-6 and its association with outcome in traumatic brain injury: a prospective cohort

Abstract

Background: Traumatic brain injury (TBI) continues to be a major cause of death and disability worldwide. Biomarkers for treatment and prognostication are needed for counseling and clinical management.

Objective: In this study, we evaluated the ability of serum IL-6 to predict mortality and disability in a population whith moderate and severe TBI (msTBI).

Methods: Adult patients with msTBI were included consecutively from December 2019 to August 2023. Clinical data were collected during hospital stays and functional outcome was established at 6 months using GOSE. Serum IL-6 levels were measured on day 0, day 3 and day 7 after injury.

Results: Eighty-eight patients were recruited and completed 6-month follow-up. Clinical variables associated with the 6-month adverse outcome were admission GCS (OR 0.77 95% CI 0.67-0.87, p < 0.001), age (OR 1.10 95% CI 1.03-1.1, p = 0.001), Rotterdam score (OR 2.8 95% CI 1.7-5.0, p < 0.001), hospital infections (OR 4.7 95% CI 1.9-12.1, p < 0.001) and day-0 IL-6 (OR 1.1 95% CI 1.08-1.13, p < 0.001). When adjusted for age, severity of injury,and the presence of a hospital infection, day-0 IL-6 was significantly associated with the adverse outcome at 6 months (OR 1.15 95% CI 1.1-1.2, p = 0.031). Area under the curve (AUC) of 89% (95% CI 82%-96%). Calculated sensitivity and specificity were 75% and 89%, respectively, at a cut-off point of 59 pg/ml.

Conclusion: In a population of msTBI, levels of serum interleukin-6 within the first 24 h after injury is an independent predictor of 6-month mortality and disability with a net benefit in clinical decision-making across relevant threshold probabilities.

Keywords: Biomarker; Inflammation; Inflammatory response; Interleukin; Prognostication; Trauma; Traumatic brain injury.

Fig. 1

Functional outcome at 6 months in the entire cohort. Six months after TBI, the outcome of the 88 surviving patients was assessed using the GOSE. The percentage by category has been placed on top of each bar

Fig. 2

IL-6 levels according to time points and favorable versus adverse outcome

Fig. 3

Receiver Operating Characteristic (ROC) Curve for IL-6 model Predicting Adverse Outcomes. This ROC curve illustrates the diagnostic performance of day-0 IL-6 levels in predicting adverse outcomes in patients with traumatic brain injury (TBI). The blue line represents the true positive rate (sensitivity) plotted against the false positive rate (1-specificity) across a range of IL-6 thresholds. The area under the curve (AUC) is a measure of the test’s overall accuracy, with higher values indicating better discriminative ability. In this analysis, IL-6 achieved an AUC of approximately 0.89, suggesting strong predictive power for identifying patients at risk of adverse outcomes. The diagonal gray line represents a reference for random chance (AUC = 0.5), highlighting the improvement provided by IL-6 levels over a non-informative model

Fig. 4

Decision Curve Analysis (DCA) for IL-6 as a Prognostic Biomarker in Traumatic Brain Injury (TBI). This Decision Curve Analysis (DCA) plot evaluates the clinical utility of IL-6 levels on admission for predicting adverse outcomes in TBI patients. The x-axis represents the threshold probability, or the probability above which a clinician might consider intervening based on predicted poor outcomes. The y-axis shows the net benefit, which is calculated by weighing the true positives against the false positives across different threshold probabilities. The green line (“Treat None”) represents the net benefit of not treating any patients for an adverse outcome, while the red line (“Treat All”) represents the net benefit of treating all patients for a potential adverse outcome. The blue line represents the net benefit of using IL-6 levels as a predictive biomarker. At threshold probabilities ranging from 0% to approximately 75%, the IL-6 model (blue line) demonstrates a higher net benefit compared to both the “Treat All” and “Treat None” strategies, suggesting that using IL-6 as a prognostic marker could help guide clinical decisions by identifying patients more likely to experience poor outcomes. This is particularly relevant for cases where clinicians are considering escalation of care or providing prognostic information to families. The observed net benefit of IL-6 in this range indicates its potential clinical value in distinguishing between patients who might benefit from early interventions and those who may not

Fig. 5

Receiver Operating Characteristic (ROC) Curve for the IMPACT Model Predicting Adverse Outcomes. This ROC curve represents the performance of the IMPACT model in predicting adverse outcomes in traumatic brain injury (TBI) patients. The blue line shows the sensitivity (true positive rate) versus the false positive rate (1-specificity) at various threshold probabilities. The area under the curve (AUC) quantifies the model's overall ability to discriminate between patients with and without adverse outcomes. Here, the IMPACT model achieves a high AUC (0.87 95%CI: 0.79–0.95), indicating strong predictive accuracy. The diagonal gray line represents a random chance reference (AUC = 0.5), emphasizing the IMPACT model's improvement in predictive performance over a non-informative model