Abstract
Purpose
We analysed the impact of early systemic insults (hypoxemia and hypotension, SIs) on brain injury biomarker profiles, acute care requirements during intensive care unit (ICU) stay, and 6-month outcomes in patients with traumatic brain injury (TBI).
Methods
From patients recruited to the Collaborative European neurotrauma effectiveness research in TBI (CENTER-TBI) study, we documented the prevalence and risk factors for SIs and analysed their effect on the levels of brain injury biomarkers [S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), neurofilament light (NfL), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and protein Tau], critical care needs, and 6-month outcomes [Glasgow Outcome Scale Extended (GOSE)].
Results
Among 1695 TBI patients, 24.5% had SIs: 16.1% had hypoxemia, 15.2% had hypotension, and 6.8% had both. Biomarkers differed by SI category, with higher S100B, Tau, UCH-L1, NSE and NfL values in patients with hypotension or both SIs. The ratio of neural to glial injury (quantified as UCH-L1/GFAP and Tau/GFAP ratios) was higher in patients with hypotension than in those with no SIs or hypoxia alone. At 6 months, 380 patients died (22%), and 759 (45%) had GOSE ≤ 4. Patients who experienced at least one SI had higher mortality than those who did not (31.8% vs. 19%, p < 0.001).
Conclusion
Though less frequent than previously described, SIs in TBI patients are associated with higher release of neuronal than glial injury biomarkers and with increased requirements for ICU therapies aimed at reducing intracranial hypertension. Hypotension or combined SIs are significantly associated with adverse 6-month outcomes. Current criteria for hypotension may lead to higher biomarker levels and more negative outcomes than those for hypoxemia suggesting a need to revisit pressure targets in the prehospital settings.
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Early systemic insults after traumatic brain injury have become less common compared to the past. Their occurrence is closely tied to the severity of trauma and extracranial injuries |
Systemic insults are associated with brain biomarker release, characterised by distinct profiles. Given current thresholds, low blood pressure, rather than oxygen deficiency, exerts a more pronounced impact on neurons than glial cells, as suggested by different biomarker profiles, and on unfavourable neurological outcomes. When both systemic insults occur concurrently, this produces higher biomarkers release and is associated with a worse clinical outcome and mortality |
Introduction
Traumatic brain injury (TBI) is among the leading causes of mortality and long-term disability worldwide [1, 2]. Clinical outcome is substantially related to the severity of primary injury and its impact on driving secondary injury mechanisms [3,4,5]. However, the outcome can be considerably worsened by systemic insults (SIs), such as hypoxemia and hypotension, which can amplify the cascade of events after the initial damage and negatively impact mortality and long-term disability.
The incidence and detrimental effects of SIs on outcome after TBI were seminally documented over 45 years ago by Miller [6] and confirmed by Chesnut et al. in 1993 [7]. In 2007, the IMPACT-TBI study [8] corroborated the association between SIs and unfavourable 6 months outcomes [3]. SIs may enhance cellular, biochemical, and molecular events occurring after TBI, including local and systemic inflammation [9, 10], activate microglia [9] and coagulative cascades [11, 12] and alter blood–brain barrier (BBB) function [13].
However, while these associations are established, it remains unclear whether the worse outcomes are directly related to increased neurological injury caused by SIs in the acute phase or simply represent an association with worse intra- and extracranial injury. Indeed, we have little objective quantification of the impact of SIs on acute neurological injury or acute therapy needs—both of which are critical issues in determining the mechanisms by which SIs drive worse outcomes and how their impact might be mitigated. Little data on the impact of SIs on protein biomarker levels are available [13], a limitation of fundamental importance, as biomarkers are now known to reflect injury severity and brain computed tomography (CT) abnormalities [14,15,16] and contribute to prognostication [17].
To address this issue, we undertook an analysis of data from intensive care unit (ICU) patients enrolled in the Collaborative European neurotrauma effectiveness research in TBI (CENTER-TBI) study, aiming to investigate the occurrence of initial SIs, assess the factors associated with SIs occurrence, analyse the biomarker profiles related to these insults, and evaluate the correlation between early SIs, biomarkers, and 6-month outcomes. Finally, we explored the difference in the need for acute care and interventions during the ICU stay between patients with or without SIs, mainly focusing on therapies to reduce intracranial pressure (ICP).
Methods
Design and inclusion criteria
The CENTER-TBI study (clinicaltrials.gov NCT02210221) is a longitudinal, prospective observational study including TBI patients across 65 centres in Europe and Israel. Details regarding the study design, methodology, screening, and enrolment process have been previously described [18, 19]. The study was approved by the Medical Ethics Committees of each participating centre, and informed consent was obtained according to local regulations (https://www.center-tbi.eu/project/ethical-approval). This study, which was pre-registered on the CENTER-TBI proposal platform, was approved by the CENTER-TBI proposal review committee.
The inclusion criteria in this study were patients:
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Who experienced TBI and who were included in the CENTER-TBI study,
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Aged ≥ 18 years old,
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Admitted to ICU,
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With available data on arterial blood pressure and oxygenation in the prehospital settings and/or at hospital arrival,
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With a 6-month neurological outcome (Glasgow Outcome Score—Extended, GOSE) evaluated.
The study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (electronic supplementary material, ESM).
Data collection
Details on data collection and management of the CENTER-TBI study have been previously published [18, 19]. The CENTER-TBI core database v3.0 data was used and downloaded via Opal data warehouse [20].
Data collected included patients' demographic characteristics, pre-injury comorbidities, TBI mechanism, and a series of parameters at admission, such as neuroradiological features (as for Marshall CT score [21]), neurological status [as for Glasgow Coma Scale (GCS), pupil’s reactivity], presence of extracranial injury [quantified using the total Injury Severity Score (ISS), and with major extracranial injury defined as an Abbreviated Injury Scale [22] (AIS) score ≥ 3], arterial blood gas (ABG) values [i.e., pH, arterial partial pressure of oxygen (PaO2), arterial partial pressure of carbon dioxide (PaCO2), base excess], and laboratory data (e.g., creatinine, glucose).
We also collected variables regarding the need for neurosurgical treatment and ICP monitoring, the treatments used for ICP management [i.e., therapy intensity level (TIL) [23], etc.], the need for extracranial and intracranial surgeries (e.g., damage control procedures), need for blood transfusion, need for intubation and mechanical ventilation, need for tracheostomy.
Early serum biomarkers
We extracted the results of biomarker measurements in samples obtained within 48 h from hospital admission for six specific serum biomarkers associated with TBI [24]: S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), neurofilament light (NfL), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and protein Tau. Notably, GFAP and S100B are recognised as biomarkers of glial injury, UCH-L1 and NSE indicate neuronal cell body injury, and Tau and NfL are markers for dendritic and axonal damage. We then calculated the ratios between neuronal or dendritic and glial markers. Specifically, UCH-L1/GFAP was used to gauge the relative extent of neuronal body versus glial injury. Tau/GFAP was used to assess the degree of dendritic and axonal injury versus glial injury. The selection of these three biomarkers for the two ratios (GFAP, UCH-L1, and Tau) was based on their high specificity to the central nervous system, as well as their reasonably similar kinetics, with time-to-peak values of 8 h for GFAP and UCH-L1, and 24 h for Tau [25]. This choice allowed for a meaningful comparison of the impact of these serum indicators on different cellular components in the aftermath of TBI. It is important to note that, despite its short half-life, we opted not to include S100B in this analysis due to potential release from extracranial lesions. Furthermore, we excluded NSE and NfL from the analysis because of their significantly divergent kinetics, with time-to-peak values of 72 h and 1–2 weeks, respectively [25, 26].
Definition of early SIs
Data on hypotension and hypoxemia were collected in the prehospital settings and/or at hospital arrival. A hypotensive episode was defined, as per the Traumatic Coma Data Bank (TCDB [7]), as a systolic blood pressure (SBP) < 90 mmHg or a clinical definition of shock. A hypoxemic episode was described as a PaO2 < 60 mmHg and/or peripheral oxygen saturation (SpO2) < 90% or as evidenced by cyanosis, apnoea, or respiratory distress.
According to the occurrence of SIs in the prehospital setting or/and at arrival, patients were allocated into four different groups: “Hypoxemia” if they had a hypoxemic event, “Hypotension” if they had a hypotensive event, “Both” if both events were recorded, and “No” if no SI was observed.
Clinical outcomes
Mortality and functional outcome, defined by the GOSE [27], were assessed at 6 months. GOSE scores range from 1 (dead) to 8 (good recovery), and an unfavourable functional outcome was defined as GOSE ≤ 4. All responses were obtained by trained study personnel during a face-to-face visit, telephone interview, or postal questionnaire [28].
Statistical analysis
Data were expressed as median (interquartile ranges, IQR) or frequency (%) where appropriate. Comparisons between the four SIs groups were performed by the Chi-squared or Kruskal–Wallis test, according to the nature of the variables. Description of metabolic and biomarker profiles for each group were visualised using radar plots in which the median of each variable (metabolic or biomarkers) was mapped. The axes of each radar ranged from the minimum to the maximum value of the median calculated for each group. To identify factors associated with the occurrence of SIs, multivariable logistic regression was applied, including sex, age, cause of injury, and presence of extracranial injury (defined as AIS ≥ 3 for any of abdomen–pelvis, face–head–neck, thorax–chest, extremities, external, and spine region) as regressors. The differences in biomarker values and the two ratios (UCH-L1/GFAP and Tau/GFAP) among SIs were also evaluated and correlations quantified by the Pearson index. The Wilcoxon’s test was used to perform pairwise comparisons between SIs groups on the two ratios with corrections for multiple testing using the Benjamini–Hochberg approach.
A logistic regression model for unfavourable outcomes and a Cox regression model for mortality were fitted to estimate the impact of SIs on outcomes, by considering each biomarker and their potential interactions. In both cases, the models were also adjusted for the variables in the core IMPACT scheme (i.e., age, pupillary reactivity, and GCS motor at baseline). Finally, we described the need for acute care and interventions during the ICU stay among SIs in the first 48 h and during the hospital stay. Results are shown as the odds ratio (OR) or hazard ratio (HR) with the corresponding 95% confidence interval (CI). All analyses were performed using R software (version 4.3.1, “Beagle Scouts”).
Results
Patient characteristics, systemic insults occurrence
From 4509 patients in the CENTER-TBI study, 1695 patients fulfilled the inclusion criteria and were included in the analysis (ESM, Figure S1). They were mostly men (n = 1243, 73.3%) and the median age was 52 years (IQR = 33–67).
The most common causes of TBI were road traffic collisions (n = 741, 45.2%) or incidental falls (n = 689, 42%). Median GCS at arrival was 9 (IQR = 4–14), and 792 patients (49%) presented with GCS ≤ 8. On the first CT, 642 patients (43%) had a Marshall CT score of 3–6, indicating significant injury.
Among the study population, 1280 (75.5%) patients had no SIs in the prehospital setting and/or at arrival. Hypoxemic insults were documented in 158 (9.3%) and hypotension in 142 (8.4%), while 115 (6.8%) patients had documented both hypoxemic and hypotensive insults. In total, 273 (16.1%) patients had hypoxemia and 257 (15.2%) hypotension. When further splitting patients into different categories of GCS (Table 1), patients with GCS 3–5 had the highest incidence of SIs (48% hypoxemia, 44.4% hypotension, 66.7% both SIs, p < 0.001).
The four groups were similar in age and sex but had distinct clinical profiles (Table 1 and ESM, Table S1). The group with both SIs was characterised by significantly worse neurological status (defined using the GCS), lower pH, a higher incidence of major extracranial injury and extracranial injury severity, lower base excess, and higher lactate, glucose, and creatinine levels (p < 0.001, Table 1 and ESM, Table S1 and Figure S2). At multivariable analysis (ESM, Figure S3), the involvement in a road traffic collision or a suicide attempt increases the risk of occurrence of early SIs compared to incidental fall (OR = 1.52, 95% CI 1.16–2.01 and OR = 3.89, 95% CI 1.82–8.52, respectively). Moreover, the presence of abdominal/pelvic (OR = 2.35, 95% CI 1.73–3.2) and chest trauma (OR = 1.9, 95% CI 1.45–2.5) were independently associated with the occurrence of early SIs when compared to patients without extracranial injury.
Biomarker profiles and systemic insults
The values of the biomarkers and the ratios in the whole population and in the four SIs groups are presented in ESM, Table S2.
Figure 1 shows the biomarkers values according to the presence of SIs. Patients with hypotension and both SIs had higher biomarker values compared to patients with no SIs or only hypoxemia (Fig. 1). Surprisingly, GFAP values were not significantly different between the SIs groups. In contrast, the other biomarker values showed an increase in patients with at least one insult with the maximum values in patients with both SIs (ESM, Table S2).
Radar plot of serum biomarker levels according to the presence of systemic insults. The centre of the radar shows the minimum median value for each serum biomarker. The outer shows the maximum value. S100B S100 calcium-binding protein B, NSE Neuron Specific Enolase, NfL neurofilament light chain protein, UCH-L1 ubiquitin carboxy-terminal hydrolase L1, GFAP glial fibrillary acidic protein
The correlogram in Fig. 2 shows the correlation between the biomarkers (log-transformed) according to the presence of SIs. A significant correlation between individual biomarkers was observed, with correlation coefficients higher than 0.6 in most cases. However, these correlations were modified by SIs, most prominently in the case of hypotension and combined insults.
Correlation between the logarithm of serum biomarker levels in all patients and according to the presence of SIs. The labelling of the x and y axes is presented on each side of the figure. Following parameters are displayed in logarithm scale: logS100B, logNSE, logGFAP, logUCH-L1, logTau and logNfL. In the diagonal, the distributions of each serum biomarker by SIs are shown with different colours (yellow = No SIs, light blue = Hypoxemia, orange = Hypotension and grey = Both SIs). The lower triangular matrix comprises the bivariate scatter plots of serum biomarkers (on the logarithm scale). Patients in the four classes of SIs are represented with different colours. In the diagonal plots, the distribution of each biomarker is shown by the presence of SIs. The upper triangular matrix of the correlogram shows the Pearson correlation coefficient and its significance level estimated on all patients (Corr) and in each SIs group (No, Hypoxemia, Hypotension, Both). As an explicative example, in the middle panel correspondent to log GPAP and log UCH-L1 (row 4 and column 3), it is shown the scatter plots of the two biomarkers coloured according to the different SIs categories denoting high linear correlation among the two. In the panel at row 3 and column 4 the correlation coefficients between the two biomarkers are provided: 0.805 in the overall population, 0.83 in patients without SI, 0.79 in patients with only hypoxemia, 0.58 in those with only hypotension, and 0.69 in those with both SIs; all being significantly different from 0 (p < 0.001). In the diagonal panels (raw and column 3 and raw and column 4), we present the distribution of log GPAP and UCH-l1 among the different categories of SIs, respectively. P values are as follows: ***< 0.001, **0.001. Corr correlation, GFAP glial fibrillary acidic protein, NfL neurofilament light, NSE neuron-specific enolase, SIs systemic insults, S100B S100 calcium-binding protein B, UCH-L1 ubiquitin carboxy-terminal hydrolase L1
The relative balance of glial and neuronal injury, defined using early UCH-L1/GFAP and Tau/GFAP ratios, was significantly different between the SIs groups (Fig. 3), with lowest ratios in patients who did not suffer any SIs, higher values in those who suffered hypoxemia alone and hypotension alone, and highest values in individuals who sustained both insults (Fig. 3 and ESM, Table S2).
Need for ICU care and ICP lowering strategies
ICP monitoring was more frequently used in patients who presented with SIs compared to those who did not, i.e., in 59% of patients in the hypoxemia group and 54% of those with both SIs and only in 40% of patients without SIs (p < 0.001) (ESM, Table S3). The median TIL score across the entire cohort was 2 (IQR = 0–6) but varied across SIs groups. In particular, the median TIL was higher in patients with SIs, especially with both SIs [median TIL for both SIs was 4 (IQR = 1–6), versus 2 (IQR = 0–5) for no SIs, p < 0.001]. Blood transfusions, need for mechanical ventilation, and tracheostomy were more common in patients who experienced both SIs or hypotension alone (p < 0.001). Extracranial surgery in the first 48 h was more frequent in patients with SIs (p < 0.001) (ESM, Table S3).
6-month outcome
At 6 months, 380 patients had died (22%), and a total of 759 (45%) had GOSE ≤ 4. At unadjusted analysis, patients who experienced at least one SI had higher mortality than those who did not (31.8% vs. 19%, p < 0.001, ESM, Table S4 and Figure S4), with the highest percentage in patients with both SIs (45.2%). Further, poor neurological outcomes (defined as GOSE ≤ 4) were more frequently seen in patients who experienced hypotension (63.4%) or both SIs (64.3%) when compared to other groups (40.1% for no SI and 51.9% for hypoxemia alone, p < 0.001).
In the logistic regression model, patients with hypotension showed a significantly higher risk of unfavourable neurological outcomes (Table 2A). Increasing levels of individual biomarkers (log-transformed) were significantly associated with unfavourable outcomes. There was no interaction between SIs and any individual biomarker (ESM, Table S5).
In the Cox regression model adjusted for NfL, GFAP, or UCH-L1 biomarkers (log-transformed), patients with both SIs showed a significant increase in 6-month mortality. In contrast, no statistical differences were found in SIs groups in the models adjusted for NSE, S100B, or Tau biomarkers (Table 2B). In all models, increasing levels of individual biomarkers (log-transformed) were significantly associated with mortality at 6 months (Table 2B). There was no interaction between SIs and any biomarker on the outcome (ESM, Table S5).
Discussion
In this analysis of a large cohort of TBI patients admitted to the ICU, we have explored the incidence, associations, and impact of early hypotension and hypoxemia after brain injury, specifically focusing on their relationships with protein biomarkers of brain injury.
The main findings of our study can be summarised as follows:
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The incidence of SIs in the early phases of TBI is lower than in past studies.
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Patients who experience SIs have more frequent extracranial injuries and a more deranged metabolic profile at presentation.
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Patients with hypotensive or combined hypoxemic and hypotensive events presented with the most deranged biomarkers profile. All neuronal injury biomarkers had the highest values in patients with hypotension or both SIs compared to other subgroups.
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A significant correlation between individual biomarkers was observed in the overall population and stratified by SIs.
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However, while patients who suffered SIs did not show significantly higher GFAP levels than those without SIs, they showed increased neuronal injury biomarkers. Consequently, the UCH-L1/GFAP and Tau/GFAP ratios showed a clear hierarchy: No SI < hypoxemia alone < hypotension alone < combined hypoxemia and hypotension.
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Patients with SIs more frequently required tracheal intubation, mechanical ventilation, and extracranial surgery and had a significantly higher TIL score over the first week of their ICU stay.
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Hypotension and increasing levels of individual biomarkers were associated with increased mortality and unfavourable neurological outcomes, but there was no interaction between SIs and biomarker levels.
Previous reports indicate that patients presenting with TBI showed an incidence of 36% of hypotensive events and up to 44% of hypoxemia [7, 29, 30]. Over the last decade, the characteristics and epidemiology of TBI patients have been changing [1, 2] as road safety is improving and the population is becoming older, resulting in an increase in incidental falls and fewer road traffic accidents as cause of injury. Due to these changes and better out-of-hospital systems [31], the prevalence of SIs has also decreased over time [28]. Our results reflect these changes in epidemiology, with a lower incidence of hypoxemia and hypotension than previously described. However, the incidence presented here is higher than the incidence described in a previous CENTER-TBI manuscript [31], but this apparent discrepancy is explained by the different inclusion criteria and definition of systemic insults.
Even though the harmful effect of SIs on TBI patient outcomes has been previously recognised, little is known about the clinical features and the biomarkers profile that characterise these patients. Our large sample size and multicentre nature make our results unique in this context, reflecting contemporary European TBI management. Our defined SIs groups showed clear differences in the severity of TBI and the presence of extracranial injuries. We also found that patients with SIs had more significant metabolic compromises regarding tissue oxygen debt (higher lactate and base deficit), suggesting that both the injury and the ability of host physiology to cope with the injury insult were more deranged in patients with SIs. Critically, even when controlling for TBI severity, patients with SIs showed higher levels of brain injury biomarkers, suggesting that the systemic physiological compromise had translated to significantly increased neurological injury.
These associations of SIs with systemic physiological compromise and incremental neurological injury are novel findings. Notably, the latter finding allows us to dissociate the effect of injury from that of the SI insults. This increased neural injury translated to a more aggressive disease course requiring higher therapy intensity levels.
We also found that SIs were strongly associated with a need for extracranial surgery. This is unsurprising, given the association of SIs with major extracranial injury. However, while essential, these extracranial operations pose the risk of additional physiological stress and may deliver further insults to a particularly vulnerable brain. The issue of when extracranial surgery should be undertaken in patients with TBI is much debated, and individual considerations of risk and benefit are required to optimise the timing of such surgery. Our data suggest that a history of early SIs, particularly when associated with marked biomarker elevation, indicates a particularly vulnerable brain, and adds to the risk side of this calculation.
Additional novel insights provided by our results rest, in large part, on data regarding specific cerebral biomarkers and behaviour after SIs. A growing body of literature highlights the importance of biomarkers as prognostic tools and indicators of disease progression after TBI [14, 15]. Each biomarker, resulting from a specific pathophysiological mechanism, may reflect injury to specific cell populations [29]. GFAP and S100B have been suggested as markers of glial injury, UCH-L1 and NSE as markers of neural cell body injury, and NfL and total Tau as markers of axonal and dendritic injury [32]. However, these putative relationships are confounded by other factors and may not be reflected in clinical TBI. For example, an extracranial injury may result in immediate S100B elevation that does not reflect TBI [33, 34], and the prolonged release kinetics of NSE and NfL means that a definitive peak may be delayed by days or weeks [25]. Further, biomarkers have shown stronger correlations with the burden of injury seen on CT scans [35, 36] than with the pathoanatomical type of injury, and a panel of biomarkers have improved the ability to predict outcomes when added in established predictive models such as IMPACT and CRASH more than any biomarker alone [14]. The behaviour of biomarkers in response to SIs, provides an opportunity to examine the magnitude of insult posed by different classes of SIs, and, in turn, assess the cellular specificity of biomarkers from a different perspective. GFAP, UCH-L1, and Tau have sufficiently similar kinetics (time to peak: 8 h, 8 h, and 24 h, respectively [25]) to allow comparison of the effects of SIs on different cellular compartments following TBI.
Our aim was to compare the impact of physiological insults on glial versus neural (rather than dendritic/axonal) compartments. The range of blood biomarkers available in TBI reflects injury to different tissue compartments in the brain—including glia (S100B, GFAP), neuronal cell bodies (NSE, UCH-L1), and axons and dendrites (Tau, NfL) [37], and might be expected to show specificity for pathoanatomical types of injury. However, the levels of all biomarkers scale with the overall burden of injury rather than being specific to pathoanatomical kinds of injury [38, 39]. For example, GFAP, though a glial marker, is a sensitive marker of CT-occult diffuse axonal injury [40]. Given the lack of pathoanatomical specificity of individual biomarkers in relation to initial mechanical impact, we explored whether the more nuanced physiological insults provided by hypoxemia and hypotension might result in differential effects on cell populations, which would be reflected in the ratios of glial and neuronal derived biomarkers.
Interestingly, in the absence of SIs, GFAP, UCH-L1, and Tau showed high correlations, providing a benchmark for parallel amounts of glial and neuronal injury [41]. However, this correlation was altered by SIs, with a relatively more significant increase of biomarkers of neural injury (UCH-L1 and Tau) than glial injury (GFAP). None of the SIs were associated with higher levels of GFAP, but all showed increases in UCH-L1 and Tau, and, consequently, higher ratios of UCH-L1/GFAP and Tau/GFAP. These elevations in neuronal injury biomarkers were more prominent in patients who suffered hypotension rather than hypoxemia and were highest in those who suffered a combined SI. These findings are in keeping with data on vulnerability and responses of different cell types to modelled SIs [39, 42].
This imbalance between neural and glial injury was worse with hypotension than hypoxemia and further accentuated by the combination of both. These data suggest that all insults are associated with more significant neuronal than glial insults. This excess neuronal injury may be more prominent with hypotension than hypoxemia and the combination of both insults further accentuates this imbalance, suggesting that current thresholds defined for hypoxic SIs (PaO2 < 60 mmHg or SpO2 < 90%) and current thresholds defined for hypotensive SIs (SBP < 90 mmHg) may not be equally harmful. The dominant harms of a hypotensive SI threshold of SBP < 90 mmHg is in keeping with recent data suggesting that optimal blood pressure may be as high as 110–130 mmHg to minimise secondary hypotensive insults [40]. This finding also has implications for how aggressively we should manage prehospital systemic insults, given the increasing trend to allow permissive hypotension in extracranial injury with bleeding. This should pave the way for further studies that can help define the optimal threshold of arterial blood pressure to adopt in this population in the early phases [39, 43].
Our results highlight the need to identify and characterise patients at risk for second insults early through a more specific assessment and quantify their impact by measuring differential neuronal and glial injury biomarker levels. Such data could help us understand the pathophysiological events occurring after SIs and refine our understanding of how they impact outcomes. However, our results also support the integration of biomarkers in established prognostic models with SIs to improve precision in prognosis and decision-making in the context of multimodal strategies and evaluation. The lack of interaction of biomarkers with SIs on outcomes suggests that the precise behaviour of biomarkers after SIs needs still to be fully understood and requires specific studies exploring the changes over time of biomarkers and the dynamics of cerebral damage.
Limitations
Firstly, it is imperative to acknowledge that our study, by its observational nature, does not establish causal relationships concerning the observed outcomes. While we employed robust statistical models and adjusted for potential confounding variables, the limitations inherent to the observational studies' analysis persist.
Secondly, the absence of data on the dynamic biomarker changes limits our ability to comprehensively depict our TBI patients' disease progression and establish a more precise correlation with their outcomes. We cannot elucidate the trajectories of these biomarkers and discern the impact of therapeutic interventions on their kinetics. A single measurement obtained at the initial hospital presentation is probably inferior in defining prognosis to biomarker measurements taken later. These limitations emphasise the necessity for prudence when interpreting our results and underscore areas with potential for enhancement in future research endeavours. Notably, using point-of-care tests at the bedside, enabling real-time monitoring of biomarker trajectories, presents an exciting avenue for future research improvement. The inclusion criteria and the granularity of the data may not fully reflect the heterogeneity of the clinical picture. For instance, the Marshall-CT score does not take into account brainstem injuries, which are significantly associated with mortality and poor neurological outcomes. Further, no data are available regarding the duration of SIs and their trajectory over time, which are essential variables to quantify the dose of injury received by patients. Finally, the low incidence of SIs might be due to other factors, such as incomplete documentation, or the inclusion of patients with less severe TBI (GCS ≥ 9).
Conclusion
Systemic insults are less frequent in the early phases of TBI in our study when compared to past studies. They are generally related to the severity of trauma and the presence of extracranial injuries. They are associated with a more deranged metabolic profile and higher values of biomarkers at admission. Individual SIs are associated with different biomarker profiles, which provide insights into the specific vulnerability of neuronal and cellular populations to these insults. Given our current thresholds for defining hypoxic and hypotensive SIs, our data suggest a more significant insult to neuronal populations from hypotension than hypoxemia, and a still larger insult from the combination of the two. Further studies are warranted to validate and confirm our results.
Data availability
Data supporting the study's findings are available on reasonable request after approval of a proposal from the corresponding author (GC). Data collected for the analysis, including deidentified individual participant data and a data dictionary defining each field in the set, will be made available to others on request. Related documents such as the study protocol, statistical analysis plan, and informed consent form will also be available on request.
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Acknowledgements
We thank Matthew Fish, Boston University, for providing us with the R code for splitting into multiple graphs by biomarker. PR, SG and GC participated in the manuscript preparation during their personal involvement in the Italian Ministry of University MUR Dipartimenti di Eccellenza 2023-2027 (l. 232/2016, art. 1, commi 314–337).
The CENTER-TBI participants and investigators: Cecilia Åkerlund1, Krisztina Amrein2, Nada Andelic3, Lasse Andreassen4, Audny Anke5, Anna Antoni6, Gérard Audibert7, Philippe Azouvi8, Maria Luisa Azzolini9, Ronald Bartels10, Pál Barzó11, Romuald Beauvais12, Ronny Beer13, Bo-Michael Bellander14, Antonio Belli15, Habib Benali16, Maurizio Berardino17, Luigi Beretta9, Morten Blaabjerg18, Peter Bragge19, Alexandra Brazinova20, Vibeke Brinck21, Joanne Brooker22, Camilla Brorsson23, Andras Buki24, Monika Bullinger25, Manuel Cabeleira26, Alessio Caccioppola27, Emiliana Calappi 27, Maria Rosa Calvi9, Peter Cameron28, Guillermo Carbayo Lozano29, Marco Carbonara27, Simona Cavallo17, Giorgio Chevallard30, Arturo Chieregato30, Giuseppe Citerio31, 32, Hans Clusmann33, Mark Coburn34, Jonathan Coles35, Jamie D. Cooper36, Marta Correia37, Amra Čović 38, Nicola Curry39, Endre Czeiter24, Marek Czosnyka26, Claire DahyotFizelier40, Paul Dark41, Helen Dawes42, Véronique De Keyser43, Vincent Degos16, Francesco Della Corte44, Hugo den Boogert10, Bart Depreitere45, Đula Đilvesi 46, Abhishek Dixit47, Emma Donoghue22, Jens Dreier48, GuyLoup Dulière49, Ari Ercole47, Patrick Esser42, Erzsébet Ezer50, Martin Fabricius51, Valery L. Feigin52, Kelly Foks53, Shirin Frisvold54, Alex Furmanov55, Pablo Gagliardo56, Damien Galanaud16, Dashiell Gantner28, Guoyi Gao57, Pradeep George58, Alexandre Ghuysen59, Lelde Giga60, Ben Glocker61, Jagoš Golubovic46, Pedro A. Gomez 62, Johannes Gratz63, Benjamin Gravesteijn64, Francesca Grossi44, Russell L. Gruen65, Deepak Gupta66, Juanita A. Haagsma64, Iain Haitsma67, Raimund Helbok13, Eirik Helseth68, Lindsay Horton 69, Jilske Huijben64, Peter J. Hutchinson70, Bram Jacobs71, Stefan Jankowski72, Mike Jarrett21, Jiyao Jiang58, Faye Johnson73, Kelly Jones52, Mladen Karan46, Angelos G. Kolias70, Erwin Kompanje74, Daniel Kondziella51, Evgenios Kornaropoulos47, LarsOwe Koskinen75, Noémi Kovács76, Ana Kowark77, Alfonso Lagares62, Linda Lanyon58, Steven Laureys78, Fiona Lecky79, 80, Didier Ledoux78, Rolf Lefering81, Valerie Legrand82, Aurelie Lejeune83, Leon Levi84, Roger Lightfoot85, Hester Lingsma64, Andrew I.R. Maas43, Ana M. CastañoLeón62, Marc Maegele86, Marek Majdan20, Alex Manara87, Geoffrey Manley88, Costanza Martino89, Hugues Maréchal49, Julia Mattern90, Catherine McMahon91, Béla Melegh92, David Menon47, Tomas Menovsky43, Ana Mikolic64, Benoit Misset78, Visakh Muraleedharan58, Lynnette Murray28, Ancuta Negru93, David Nelson1, Virginia Newcombe47, Daan Nieboer64, József Nyirádi2, Otesile Olubukola79, Matej Oresic94, Fabrizio Ortolano27, Aarno Palotie95, 96, 97, Paul M. Parizel98, JeanFrançois Payen99, Natascha Perera12, Vincent Perlbarg16, Paolo Persona100, Wilco Peul101, Anna Piippo-Karjalainen102, Matti Pirinen95, Dana Pisica64, Horia Ples93, Suzanne Polinder64, Inigo Pomposo29, Jussi P. Posti 103, Louis Puybasset104, Andreea Radoi 105, Arminas Ragauskas106, Rahul Raj102, Malinka Rambadagalla107, Isabel Retel Helmrich64, Jonathan Rhodes108, Sylvia Richardson109, Sophie Richter47, Samuli Ripatti95, Saulius Rocka106, Cecilie Roe110, Olav Roise111,112, Jonathan Rosand113, Jeffrey V. Rosenfeld114, Christina Rosenlund115, Guy Rosenthal55, Rolf Rossaint77, Sandra Rossi100, Daniel Rueckert61 Martin Rusnák116, Juan Sahuquillo105, Oliver Sakowitz90, 117, Renan SanchezPorras117, Janos Sandor118, Nadine Schäfer81, Silke Schmidt119, Herbert Schoechl120, Guus Schoonman121, Rico Frederik Schou122, Elisabeth Schwendenwein6, Charlie Sewalt64, Ranjit D. Singh101, Toril Skandsen123, 124 , Peter Smielewski26, Abayomi Sorinola125, Emmanuel Stamatakis47, Simon Stanworth39, Robert Stevens126, William Stewart127, Ewout W. Steyerberg64, 128, Nino Stocchetti129, Nina Sundström130, Riikka Takala131, Viktória Tamás125, Tomas Tamosuitis132, Mark Steven Taylor20, Aurore Thibaut78, Braden Te Ao52, Olli Tenovuo103, Alice Theadom52, Matt Thomas87, Dick Tibboel133, Marjolein Timmers74, Christos Tolias134, Tony Trapani28, Cristina Maria Tudora93, Andreas Unterberg90, Peter Vajkoczy 135, Shirley Vallance28, Egils Valeinis60, Zoltán Vámos50, Mathieu van der Jagt136, Gregory Van der Steen43, Joukje van der Naalt71, Jeroen T.J.M. van Dijck 101, Inge A. M. van Erp101, Thomas A. van Essen101, Wim Van Hecke137, Caroline van Heugten138, Dominique Van Praag139, Ernest van Veen64, Thijs Vande Vyvere137, Roel P. J. van Wijk101, Alessia Vargiolu32, Emmanuel Vega83, Kimberley Velt64, Jan Verheyden137, Paul M. Vespa140, Anne Vik123, 141, Rimantas Vilcinis132, Victor Volovici67, Nicole von Steinbüchel38, Daphne Voormolen64, Petar Vulekovic46, Kevin K.W. Wang142, Daniel Whitehouse47, Eveline Wiegers64, Guy Williams47, Lindsay Wilson69, Stefan Winzeck47, Stefan Wolf143, Zhihui Yang113, Peter Ylén144, Alexander Younsi90, Frederick A. Zeiler47,145, Veronika Zelinkova20, Agate Ziverte60 , Tommaso Zoerle27.
1Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden; 2 János Szentágothai Research Centre, University of Pécs, Pécs, Hungary ; 3 Division of Clinical Neuroscience, Department of Physical Medicine and Rehabilitation, Oslo University Hospital and University of Oslo, Oslo, Norway; 4 Department of Neurosurgery, University Hospital Northern Norway, Tromso, Norway; 5 Department of Physical Medicine and Rehabilitation, University Hospital Northern Norway, Tromso, Norway; 6 Trauma Surgery, Medical University Vienna, Vienna, Austria; 7 Department of Anesthesiology & Intensive Care, University Hospital Nancy, Nancy, France; 8 Raymond Poincare hospital, Assistance Publique – Hopitaux de Paris, Paris, France; 9 Department of Anesthesiology & Intensive Care, S Raffaele University Hospital, Milan, Italy; 10 Department of Neurosurgery, Radboud University Medical Center, Nijmegen, The Netherlands; 11 Department of Neurosurgery, University of Szeged, Szeged, Hungary; 12 International Projects Management, ARTTIC, Munchen, Germany; 13 Department of Neurology, Neurological Intensive Care Unit, Medical University of Innsbruck, Innsbruck, Austria; 14 Department of Neurosurgery & Anesthesia & intensive care medicine, Karolinska University Hospital, Stockholm, Sweden; 15 NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK; 16 Anesthesie-Réanimation, Assistance Publique – Hopitaux de Paris, Paris, France; 17 Department of Anesthesia & ICU, AOU Città della Salute e della Scienza di Torino - Orthopedic and Trauma Center, Torino, Italy; 18 Department of Neurology, Odense University Hospital, Odense, Denmark ; 19 BehaviourWorks Australia, Monash Sustainability Institute, Monash University, Victoria, Australia; 20 Department of Public Health, Faculty of Health Sciences and Social Work, Trnava University, Trnava, Slovakia; 21 Quesgen Systems Inc., Burlingame, California, USA; 22 Australian & New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; 23 Department of Surgery and Perioperative Science, Umeå University, Umeå, Sweden; 24 Department of Neurosurgery, Medical School, University of Pécs, Hungary and Neurotrauma Research Group, János Szentágothai Research Centre, University of Pécs, Hungary; 25 Department of Medical Psychology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany; 26 Brain Physics Lab, Division of Neurosurgery, Dept of Clinical Neurosciences, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK; 27 Neuro ICU, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; 28 ANZIC Research Centre, Monash University, Department of Epidemiology and Preventive Medicine, Melbourne, Victoria, Australia; 29 Department of Neurosurgery, Hospital of Cruces, Bilbao, Spain; 30 NeuroIntensive Care, Niguarda Hospital, Milan, Italy; 31 School of Medicine and Surgery, Università Milano Bicocca, Milano, Italy; 32 NeuroIntensive Care Unit, Department Neuroscience, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy; 33Department of Neurosurgery, Medical Faculty RWTH Aachen University, Aachen, Germany ; 34 Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany ; 35 Department of Anesthesia & Neurointensive Care, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK; 36 School of Public Health & PM, Monash University and The Alfred Hospital, Melbourne, Victoria, Australia; 37 Radiology/MRI department, MRC Cognition and Brain Sciences Unit, Cambridge, UK; 38 Institute of Medical Psychology and Medical Sociology, Universitätsmedizin Göttingen, Göttingen, Germany; 39 Oxford University Hospitals NHS Trust, Oxford, UK ; 40 Intensive Care Unit, CHU Poitiers, Potiers, France; 41 University of Manchester NIHR Biomedical Research Centre, Critical Care Directorate, Salford Royal Hospital NHS Foundation Trust, Salford, UK; 42 Movement Science Group, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK; 43 Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium; 44 Department of Anesthesia & Intensive Care, Maggiore Della Carità Hospital, Novara, Italy; 45 Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium; 46 Department of Neurosurgery, Clinical centre of Vojvodina, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia; 47 Division of Anaesthesia, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK; 48 Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; 49 Intensive Care Unit, CHR Citadelle, Liège, Belgium; 50 Department of Anaesthesiology and Intensive Therapy, University of Pécs, Pécs, Hungary; 51 Departments of Neurology, Clinical Neurophysiology and Neuroanesthesiology, Region Hovedstaden Rigshospitalet, Copenhagen, Denmark; 52 National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand; 53 Department of Neurology, Erasmus MC, Rotterdam, the Netherlands; 54 Department of Anesthesiology and Intensive care, University Hospital Northern Norway, Tromso, Norway; 55 Department of Neurosurgery, Hadassah-hebrew University Medical center, Jerusalem, Israel; 56 Fundación Instituto Valenciano de Neurorrehabilitación (FIVAN), Valencia, Spain; 57 Department of Neurosurgery, Shanghai Renji hospital, Shanghai Jiaotong University/school of medicine, Shanghai, China; 58 Karolinska Institutet, INCF International Neuroinformatics Coordinating Facility, Stockholm, Sweden; 59 Emergency Department, CHU, Liège, Belgium; 60 Neurosurgery clinic, Pauls Stradins Clinical University Hospital, Riga, Latvia; 61 Department of Computing, Imperial College London, London, UK; 62 Department of Neurosurgery, Hospital Universitario 12 de Octubre, Madrid, Spain; 63 Department of Anesthesia, Critical Care and Pain Medicine, Medical University of Vienna, Austria; 64 Department of Public Health, Erasmus Medical Center-University Medical Center, Rotterdam, The Netherlands; 65 College of Health and Medicine, Australian National University, Canberra, Australia; 66 Department of Neurosurgery, Neurosciences Centre & JPN Apex trauma centre, All India Institute of Medical Sciences, New Delhi-110029, India; 67 Department of Neurosurgery, Erasmus MC, Rotterdam, the Netherlands; 68 Department of Neurosurgery, Oslo University Hospital, Oslo, Norway; 69 Division of Psychology, University of Stirling, Stirling, UK; 70 Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke’s Hospital & University of Cambridge, Cambridge, UK; 71 Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; 72 Neurointensive Care , Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK; 73 Salford Royal Hospital NHS Foundation Trust Acute Research Delivery Team, Salford, UK; 74 Department of Intensive Care and Department of Ethics and Philosophy of Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; 75 Department of Clinical Neuroscience, Neurosurgery, Umeå University, Umeå, Sweden; 76 Hungarian Brain Research Program - Grant No. KTIA_13_NAP-A-II/8, University of Pécs, Pécs, Hungary; 77 Department of Anaesthesiology, University Hospital of Aachen, Aachen, Germany; 78 Cyclotron Research Center , University of Liège, Liège, Belgium; 79 Centre for Urgent and Emergency Care Research (CURE), Health Services Research Section, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK; 80 Emergency Department, Salford Royal Hospital, Salford UK; 81 Institute of Research in Operative Medicine (IFOM), Witten/Herdecke University, Cologne, Germany; 82 VP Global Project Management CNS, ICON, Paris, France; 83 Department of Anesthesiology-Intensive Care, Lille University Hospital, Lille, France; 84 Department of Neurosurgery, Rambam Medical Center, Haifa, Israel; 85 Department of Anesthesiology & Intensive Care, University Hospitals Southhampton NHS Trust, Southhampton, UK; 86 Cologne-Merheim Medical Center (CMMC), Department of Traumatology, Orthopedic Surgery and Sportmedicine, Witten/Herdecke University, Cologne, Germany; 87 Intensive Care Unit, Southmead Hospital, Bristol, Bristol, UK; 88 Department of Neurological Surgery, University of California, San Francisco, California, USA; 89 Department of Anesthesia & Intensive Care,M. Bufalini Hospital, Cesena, Italy; 90 Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany; 91 Department of Neurosurgery, The Walton centre NHS Foundation Trust, Liverpool, UK; 92 Department of Medical Genetics, University of Pécs, Pécs, Hungary ; 93 Department of Neurosurgery, Emergency County Hospital Timisoara , Timisoara, Romania; 94 School of Medical Sciences, Örebro University, Örebro, Sweden; 95 Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; 96 Analytic and Translational Genetics Unit, Department of Medicine; Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; 97 Program in Medical and Population Genetics; The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; 98 Department of Radiology, University of Antwerp, Edegem, Belgium; 99 Department of Anesthesiology & Intensive Care, University Hospital of Grenoble, Grenoble, France; 100 Department of Anesthesia & Intensive Care, Azienda Ospedaliera Università di Padova, Padova, Italy; 101 Dept. of Neurosurgery, Leiden University Medical Center, Leiden, The Netherlands and Dept. of Neurosurgery, Medical Center Haaglanden, The Hague, The Netherlands; 102 Department of Neurosurgery, Helsinki University Central Hospital; 103 Division of Clinical Neurosciences, Department of Neurosurgery and Turku Brain Injury Centre, Turku University Hospital and University of Turku, Turku, Finland; 104 Department of Anesthesiology and Critical Care, Pitié -Salpêtrière Teaching Hospital, Assistance Publique, Hôpitaux de Paris and University Pierre et Marie Curie, Paris, France; 105 Neurotraumatology and Neurosurgery Research Unit (UNINN), Vall d'Hebron Research Institute, Barcelona, Spain; 106 Department of Neurosurgery, Kaunas University of technology and Vilnius University, Vilnius, Lithuania; 107 Department of Neurosurgery, Rezekne Hospital, Latvia; 108 Department of Anaesthesia, Critical Care & Pain Medicine NHS Lothian & University of Edinburg, Edinburgh, UK; 109 Director, MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK; 110 Department of Physical Medicine and Rehabilitation, Oslo University Hospital/University of Oslo, Oslo, Norway; 111 Division of Orthopedics, Oslo University Hospital, Oslo, Norway; 112 Institue of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway ; 113 Broad Institute, Cambridge MA Harvard Medical School, Boston MA, Massachusetts General Hospital, Boston MA, USA; 114 National Trauma Research Institute, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia; 115 Department of Neurosurgery, Odense University Hospital, Odense, Denmark; 116 International Neurotrauma Research Organisation, Vienna, Austria; 117 Klinik für Neurochirurgie, Klinikum Ludwigsburg, Ludwigsburg, Germany; 118 Division of Biostatistics and Epidemiology, Department of Preventive Medicine, University of Debrecen, Debrecen, Hungary; 119 Department Health and Prevention, University Greifswald, Greifswald, Germany; 120 Department of Anaesthesiology and Intensive Care, AUVA Trauma Hospital, Salzburg, Austria; 121 Department of Neurology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, the Netherlands; 122 Department of Neuroanesthesia and Neurointensive Care, Odense University Hospital, Odense, Denmark; 123 Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, NTNU, Trondheim, Norway; 124 Department of Physical Medicine and Rehabilitation, St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 125 Department of Neurosurgery, University of Pécs, Pécs, Hungary ; 126 Division of Neuroscience Critical Care, John Hopkins University School of Medicine, Baltimore, USA; 127 Department of Neuropathology, Queen Elizabeth University Hospital and University of Glasgow, Glasgow, UK; 128 Dept. of Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands ; 129 Department of Pathophysiology and Transplantation, Milan University, and Neuroscience ICU, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milano, Italy; 130 Department of Radiation Sciences, Biomedical Engineering, Umeå University, Umeå, Sweden; 131 Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland; 132 Department of Neurosurgery, Kaunas University of Health Sciences, Kaunas, Lithuania; 133 Intensive Care and Department of Pediatric Surgery, Erasmus Medical Center, Sophia Children’s Hospital, Rotterdam, The Netherlands; 134 Department of Neurosurgery, Kings college London, London, UK; 135 Neurologie, Neurochirurgie und Psychiatrie, Charité – Universitätsmedizin Berlin, Berlin, Germany; 136 Department of Intensive Care Adults, Erasmus MC– University Medical Center Rotterdam, Rotterdam, the Netherlands ; 137 icoMetrix NV, Leuven, Belgium; 138 Movement Science Group, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK; 139 Psychology Department, Antwerp University Hospital, Edegem, Belgium; 140 Director of Neurocritical Care, University of California, Los Angeles, USA; 141 Department of Neurosurgery, St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 142 Department of Emergency Medicine, University of Florida, Gainesville, Florida, USA; 143 Department of Neurosurgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; 144 VTT Technical Research Centre, Tampere, Finland; 145 Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
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Open access funding provided by Università degli Studi di Milano - Bicocca within the CRUI-CARE Agreement. CR is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). SG is partially supported by the grant PRIN 2022SYXEHJ.
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Conceptualisation and study definition: GC, DM, FG, CR. Funding acquisition: GC, AM, DKM. Patients' enrolment: CENTER-TBI participants and investigators. Data verification: FG. Access to raw data: FG, GC. Formal analysis: FG, PR, SG. Project administration, Data access and verification: GC. Writing—original draft: GC, DM, FG, CR. Writing—review and editing: All authors. The final responsibility for the decision to submit for publication: GC.
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GC reports grants and personal fees as a speakers' bureau member and advisory board member from Integra Neurosciences, NeurOptics, Biogen, Invex Ltd and Idorsia, all outside the submitted work. DKM reports consultancy fees, speaker fees, or research collaborations with NeuroTrauma Sciences, Lantmannen AB, GlaxoSmithKline Ltd, Pressura Neuro Ltd, Integra Neurosciences, and Invex Ltd. CR reports speaker fees from Edwards Life Sciences and Masimo. The other authors did not declare competing interests.
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The members of CENTER-TBI participants and investigators are listed in the Acknowledgement section.
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Robba, C., Graziano, F., Picetti, E. et al. Early systemic insults following traumatic brain injury: association with biomarker profiles, therapy for intracranial hypertension, and neurological outcomes—an analysis of CENTER-TBI data. Intensive Care Med 50, 371–384 (2024). https://doi.org/10.1007/s00134-024-07324-8
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DOI: https://doi.org/10.1007/s00134-024-07324-8