Using metabolomics to predict severe traumatic brain injury outcome (GOSE) at 3 and 12 months

Identifying early predictive and diagnostic biomarkers and exploring metabolic pathways for sepsis after trauma based on an untargeted metabolomics approach

Systemic inflammatory response syndrome (SIRS) and organ dysfunction make it challenging to predict which major trauma patients are at risk of developing sepsis. Additionally, the unclear pathogenesis of sepsis after trauma contributes to its high morbidity and mortality. Identifying early predictive and diagnostic biomarkers, as well as exploring related metabolic pathways, is crucial for improving early prevention, diagnosis, and treatment. This study prospectively analyzed plasma samples from patients with severe trauma collected between March 2022 and November 2023. Trauma patients were divided into two groups based on whether they developed sepsis within two weeks: the TDDS group (trauma patients who did not develop sepsis) and the TDS group (trauma patients who did develop sepsis). Plasma samples from the TDS group were collected at the time of sepsis diagnosis (Sepsis group). Metabolite concentrations were measured using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) through untargeted metabolomics. From the differential metabolites between the TDS and TDDS groups, we identified five significant metabolites (all area under the curve (AUC) ≥ 0.94) as early predictive biomarkers for sepsis after trauma: (1) docosatrienoic acid, (2) 7-alpha-carboxy-17-alpha-carboxyethylandrostan lactone phenyl ester, (3) sphingomyelin (SM) 8:1;2O/26:1, (4) N1-[1-(3-isopropenylphenyl)-1-methylethyl]-3-oxobutanamide, and (5) SM 34:2;2O. Furthermore, five significant metabolites (all AUC ≥ 0.85) were identified as early diagnostic biomarkers from the comparison between the TDS and TDDS groups: (1) lysophosphatidylcholine (LPC) O-22:1, (2) LPC O-22:0, (3) uric acid, (4) LPC O-24:2, and (5) LPC 22:0-SN1. 26 metabolites shared between two comparisons (TDS vs. TDDS and sepsis vs. TDS) were identified. Of which, 19 metabolites belong to lipid metabolism. The top three metabolic pathways related to sepsis after trauma under the impact of severe trauma were: (1) glycerophospholipid metabolism, (2) porphyrin metabolism, and (3) sphingolipid metabolism. The top three metabolic pathways related to sepsis after trauma under the impact of infection were: (1) caffeine metabolism, (2) biosynthesis of unsaturated fatty acids, and (3) steroid hormone biosynthesis. Our study identified early predictive and diagnostic biomarkers and explored metabolic pathways related to sepsis after trauma. These findings provide a foundation for future research on the onset and development of sepsis, facilitating its early prevention, diagnosis, and treatment based on specific metabolites and metabolic pathways.

Metabolomics Analysis Reveals Potential Biomarkers for Diffuse Axonal Injury Article Category: Original Work

BACKGROUND

Metabolism is essential for life maintenance, neurological function, and injury repair, yet its role in diffuse axonal injury (DAI) is not fully understood.

METHODS

Thirty patients with DAI and 34 patients without DAI were recruited based on the classification criteria using magnetic resonance imaging within 30 days of admission in this exploratory research. Serum samples and clinical parameters were collected on admission, with the Glasgow Outcome Scale Extended at 6 months after injury used as the neurological functional outcome. We did an untargeted metabolomic analysis using liquid chromatography-mass spectrometry.

RESULTS

The DAI group and non-DAI group showed significant differences in the expression levels of 27 metabolites in serum, as well as in pupillary light reflex, Glasgow Coma Scale score, and Marshall computed tomography score. Random forest analysis indicated that lysophosphatidylcholine 22:3 sn-2 and carnitine C8:1 greatly contributed to distinguishing patients with DAI from patients without DAI (MeanDecreaseGini: 3.81, 5.16). The combined prediction of DAI using these two metabolites yielded an area under the curve of 0.944, which was higher than the combination of clinical parameters.

CONCLUSIONS

The serum metabolomics revealed potential biomarkers for DAI and has significant value for exploring pathogenesis, determining early diagnosis, and improving long-term neurological function.

A pilot study on hemodynamically stable isolated chest trauma patients reveals dysregulation of oxidative metabolism

BACKGROUND

Metabolomic dysregulation precedes clinical deterioration following injury. However, despite receiving comparable treatment, patients with similar injury severity often follow different clinical trajectories and outcomes.

METHODS

This prospective cohort study at a level 1 trauma centre screened 4541 acutely injured patients with chest trauma between September 2019 and February 2023. Fifty hemodynamically stable patients with isolated chest trauma were recruited for the final analysis. Urine samples were collected on the injury days 1, 3, and 7. For healthy subjects, the urine sample was collected once. NMR-based metabolomics was performed.

RESULTS

The study found that the majority of injured patients were young (median age of 40 years), with road traffic injuries being the most common. The median time to presentation of the patient to the ED was 3.08 h, and 92% of patients had multiple rib fractures, pulmonary contusion (60%), and pleural involvement (88%). No patient died. The study found that twenty metabolites were dysregulated (p-value < 0.001). Twelve metabolites were upregulated, while the other eight showed downregulation. However, only five metabolites showed temporal association. 4-HPA, phenylalanine, aconitate, and carnitine represent a high potential for use as a biomarker in patients with isolated blunt trauma chest patients who remain hemodynamically stable. These differentially regulated metabolites were involved in Glyoxylate and dicarboxylate metabolism pathways, glycine, serine, and threonine metabolism, and the Citrate cycle (TCA cycle).

CONCLUSIONS AND RELEVANCE

Metabolomics can accurately characterize metabolism in isolated blunt chest trauma patients, revealing perturbed pathways of traits such as oxidative stress and amino acid metabolisms. These metabolites could serve as biomarkers to detect systemic changes following chest injuries early. Metabolic profiling following an injury can aid in detecting systemic changes early and identifying novel biomarkers, enabling targeted interventions to improve patient outcomes.

Metabolomic in severe traumatic brain injury: exploring primary, secondary injuries, diagnosis, and severity

BACKGROUND

Traumatic brain injury (TBI) is a major public health concern worldwide, contributing to high rates of injury-related death and disability. Severe traumatic brain injury (sTBI), although it accounts for only 10% of all TBI cases, results in a mortality rate of 30-40% and a significant burden of disability in those that survive. This study explored the potential of metabolomics in the diagnosis of sTBI and explored the potential of metabolomics to examine probable primary and secondary brain injury in sTBI.

METHODS

Serum samples from 59 adult patients with sTBI and 35 age- and sex-matched orthopedic injury controls were subjected to quantitative metabolomics, including proton nuclear magnetic resonance (1H-NMR) and direct infusion/liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS), to identify and quantify metabolites on days 1 and 4 post-injury. In addition, we used advanced analytical methods to discover metabo-patterns associated with sTBI diagnosis and those related to probable primary and secondary brain injury.

RESULTS

Our results showed different serum metabolic profiles between sTBI and orthopedic injury (OI) controls, with significant changes in measured metabolites on day 1 and day 4 post-brain injury. The number of altered metabolites and the extent of their change were more pronounced on day 4 as compared to day 1 post-injury, suggesting an evolution of mechanisms from primary to secondary brain injury. Data showed high sensitivity and specificity in separating sTBI from OI controls for diagnosis. Energy-related metabolites such as glucose, pyruvate, lactate, mannose, and polyamine metabolism metabolites (spermine and putrescine), as well as increased acylcarnitines and sphingomyelins, occurred mainly on day 1 post-injury. Metabolites of neurotransmission, catecholamine, and excitotoxicity mechanisms such as glutamate, phenylalanine, tyrosine, and branched-chain amino acids (BCAAs) increased to a greater degree on day 4. Further, there was an association of multiple metabolites, including acylcarnitines (ACs), lysophosphatidylcholines (LysoPCs), glutamate, and phenylalanine, with injury severity at day 4, while lactate, glucose, and pyruvate correlated with injury severity on day 1.

CONCLUSION

The results demonstrate that serum metabolomics has diagnostic potential for sTBI and may reflect molecular mechanisms of primary and secondary brain injuries when comparing metabolite profiles between day 1 and day 4 post-injury. These early changes in serum metabolites may provide insight into molecular pathways or mechanisms of primary injury and ongoing secondary injuries, revealing potential therapeutic targets for sTBI. This work also highlights the need for further research and validation of sTBI metabolite biomarkers in a larger cohort.

Lactate Is a Strong Predictor of Poor Outcomes in Patients with Severe Traumatic Brain Injury

Background: Lactate is a byproduct of glycolysis, often linked to oxygen deprivation. This study aimed to examine how lactate levels (LLs) affect clinical outcomes in patients with severe TBI, hypothesizing that higher LLs would correlate with worse outcomes. Methods: This is a level 1 single-center, retrospective study of patients with severe TBI between 1 January 2020 and 31 December 2023, inclusive. Results: Single-factor ANOVA indicated a significant decrease in LLs with increasing age. Linear regression models showed the same for hospital admission, Intensive Care Unit (ICU) admission LLs, and death LLs. Prognostic scores such as Injury Severity Scores (ISS) and Glasgow Coma Score (GCS) showed a strong correlation with both Hospital admission and ICU admission LLs. ANOVA indicated higher LLs with increasing ISS and increasing LLs with decreasing GCS. Linear regressions revealed a strong positive correlation between ISS and LLs. On linear regression, the LL measured at hospital admission and ICU admission was positively associated with the length of stay (LOS) in the hospital, LOS in the ICU, ventilator days, and mortality. Linear regression models showed that a decreased delta LL during ICU admission led to an increased LOS at the hospital and the ICU, as well as a higher number of days on a ventilator. Discussion: We discovered that high LLs were linked to higher AIS and GCS scores, longer stays in the hospital and ICU, more days requiring a ventilator, and higher mortality rates in patients with severe TBI. Conclusions: LLs can be considered a strong predictor of poor clinical outcomes in patients with severe TBI.

PaCO2 Association with Outcomes of Patients with Traumatic Brain Injury at High Altitude: A Prospective Single-Center Cohort Study

BACKGROUND

Partial pressure of carbon dioxide (PaCO2) is generally known to influence outcome in patients with traumatic brain injury (TBI) at normal altitudes. Less is known about specific relationships of PaCO2 levels and clinical outcomes at high altitudes.

METHODS

This is a prospective single-center cohort of consecutive patients with TBI admitted to a trauma center located at 2600 m above sea level. An unfavorable outcome was defined as a Glasgow Outcome Scale-Extended (GOSE) score < 4 at the 6-month follow-up.

RESULTS

We had a total of 81 patients with complete data, 80% (65/81) were men, and the median (interquartile range) age was 36 (25-50) years. Median Glasgow Coma Scale (GCS) score on admission was 9 (6-14); 49% (40/81) of patients had severe TBI (GCS 3-8), 32% (26/81) had moderate TBI (GCS 12-9), and 18% (15/81) had mild TBI (GCS 13-15). The median (interquartile range) Abbreviated Injury Score of the head (AISh) was 3 (2-4). The frequency of an unfavorable outcome (GOSE < 4) was 30% (25/81), the median GOSE was 4 (2-5), and the median 6-month mortality rate was 24% (20/81). Comparison between patients with favorable and unfavorable outcomes revealed that those with unfavorable outcome were older, (median age 49 [30-72] vs. 29 [22-41] years, P < 0.01), had lower admission GCS scores (6 [4-8] vs. 13 [8-15], P < 0.01), had higher AISh scores (4 [4-4] vs. 3 [2-4], P < 0.01), had higher Acute Physiology and Chronic Health disease Classification System II scores (17 [15-23] vs. 10 [6-14], P < 0.01), had higher Charlson scores (0 [0-2] vs. 0 [0-0], P < 0.01), and had higher PaCO2 levels (mean 35 ± 8 vs. 32 ± 6 mm Hg, P < 0.01). In a multivariate analysis, age (odds ratio [OR] 1.14, 95% confidence interval [CI] 1.1-1.30, P < 0.01), AISh (OR 4.7, 95% CI 1.55-21.0, P < 0.05), and PaCO2 levels (OR 1.23, 95% CI 1.10-1.53, P < 0.05) were significantly associated with the unfavorable outcomes. When applying the same analysis to the subgroup on mechanical ventilation, AISh (OR 5.4, 95% CI 1.61-28.5, P = 0.017) and PaCO2 levels (OR 1.36, 95% CI 1.13-1.78, P = 0.015) remained significantly associated with the unfavorable outcome.

CONCLUSIONS

Higher PaCO2 levels are associated with an unfavorable outcome in ventilated patients with TBI. These results underscore the importance of PaCO2 levels in patients with TBI and whether it should be adjusted for populations living at higher altitudes.

New insights into metabolism dysregulation after TBI

Traumatic brain injury (TBI) remains a leading cause of death and disability that places a great physical, social, and financial burden on individuals and the health system. In this review, we summarize new research into the metabolic changes described in clinical TBI trials, some of which have already shown promise for informing injury classification and staging. We focus our discussion on derangements in glucose metabolism, cell respiration/mitochondrial function and changes to ketone and lipid metabolism/oxidation to emphasize potentially novel biomarkers for clinical outcome prediction and intervention and offer new insights into possible underlying mechanisms from preclinical research of TBI pathology. Finally, we discuss nutrition supplementation studies that aim to harness the gut/microbiome-brain connection and manipulate systemic/cellular metabolism to improve post-TBI recovery. Taken together, this narrative review summarizes published TBI-associated changes in glucose and lipid metabolism, highlighting potential metabolite biomarkers for clinical use, the cellular processes linking these markers to TBI pathology as well as the limitations and future considerations for TBI "omics" work.

Postoperative hyperglycemia in patients with traumatic brain injury: development of a prediction model

Introduction

Blood glucose monitoring and management are very important for the prognosis of patients with traumatic brain injury (TBI). It is necessary to evaluate the status and influencing factors of hyperglycemia within 48 h after the operation in patients with TBI.

Material and methods

Patients with TBI who received craniocerebral surgery between March 1, 2022, and October 31, 2023, were enrolled. We assessed the clinical characteristics of TBI patients with and without the development of postoperative hyperglycemia. To identify potential risk factors associated with postoperative hyperglycemia, we performed both univariate and multivariate logistic regression analyses. Utilizing the regression coefficients derived from each significant risk factor, we subsequently constructed a predictive model aimed at forecasting postoperative hyperglycemia.

Results

A total of 216 TBI patients were included. The incidence of postoperative hyperglycemia was 31.48%. Correlation analysis indicated that age (r = 0.415), body mass index (BMI) (r = 0.441), diabetes (r = 0.513), Glasgow Coma Scale (GCS) score (r = 0.545) and length of hospital stay (r = 0.456) were all correlated with the postoperative hyperglycemia in TBI patients (all p < 0.05). Age ≥ 60 years (OR = 2.556, 95% CI: 1.831-3.641), BMI ≥ 24 kg/m2 (OR = 2.793, 95% CI: 2.305-3.679), diabetes (OR = 3.081, 95% CI: 2.326-3.811) and GCS score ≤ 8 (OR = 3.603, 95% CI: 1.956-4.086) were the independent factors influencing postoperative hyperglycemia in TBI patients (all p < 0.05). The area under the receiver operating characteristic curve and 95% CI were 0.795 (0.712, 0.849). The model had good discriminative ability to distinguish the occurrence of postoperative hyperglycemia in TBI patients (all p < 0.05).

Conclusions

Postoperative hyperglycemia in patients with TBI is common. For TBI patients with a total score ≥ 6 in the prediction model, early interventions and care are needed to reduce the postoperative hyperglycemia.

Importance of AIM2 as a serum marker for reflecting severity and predicting a poor outcome of human severe traumatic brain injury: A prospective longitudinal cohort study

BACKGROUND

Absent in melanoma 2 (AIM2) participates in neuroinflammation. Here, the prognostic significance of serum AIM2 was explored in severe traumatic brain injury (sTBI).

METHODS

A total of 135 sTBI patients and 80 healthy controls were recruited in this prospective cohort study. Serum C-reactive protein (CRP) and AIM2 levels were measured. Glasgow Coma Scale (GCS) and Rotterdam computed tomography (CT) classification were recorded as the severity indicators. Prognostic parameters were posttraumatic six-month extended Glasgow outcome scale (GOSE) scores and poor outcome (GOSE scores of 1-4).

RESULTS

As opposed to controls, there were significantly elevated serum AIM2 levels after sTBI. Serum AIM2 levels were independently correlated with serum CRP levels, GCS scores, Rotterdam CT scores, GOSE scores and poor outcome. Also, serum AIM2 levels were efficiently predictive of poor outcome under the receiver operating characteristic (ROC) curve. Under the restricted cubic spline, serum AIM2 levels were linearly correlated with risk of poor outcome. Using subgroup analysis, serum AIM2 levels did not significantly interact with other indices, such as age, gender, alcohol drinking, cigarette smoking, etc. Also, combination model, in which serum AIM2, GCS scores and Rotterdam CT scores were merged, was outlined using nomogram and performed well under calibration curve, ROC curve and decision curve.

CONCLUSIONS

Raised serum AIM2 levels after sTBI, in intimate correlation with systemic inflammation and trauma severity, are independently discriminative of posttraumatic six-month neurological outcome, substantializing serum AIM2 as an inflammatory prognostic biomarker of sTBI.

Update in Pediatric Neurocritical Care: What a Neurologist Caring for Critically Ill Children Needs to Know

Currently nearly one-quarter of admissions to pediatric intensive care units (PICUs) worldwide are for neurocritical care diagnoses that are associated with significant morbidity and mortality. Pediatric neurocritical care is a rapidly evolving field with unique challenges due to not only age-related responses to primary neurologic insults and their treatments but also the rarity of pediatric neurocritical care conditions at any given institution. The structure of pediatric neurocritical care services therefore is most commonly a collaborative model where critical care medicine physicians coordinate care and are supported by a multidisciplinary team of pediatric subspecialists, including neurologists. While pediatric neurocritical care lies at the intersection between critical care and the neurosciences, this narrative review focuses on the most common clinical scenarios encountered by pediatric neurologists as consultants in the PICU and synthesizes the recent evidence, best practices, and ongoing research in these cases. We provide an in-depth review of (1) the evaluation and management of abnormal movements (seizures/status epilepticus and status dystonicus); (2) acute weakness and paralysis (focusing on pediatric stroke and select pediatric neuroimmune conditions); (3) neuromonitoring modalities using a pathophysiology-driven approach; (4) neuroprotective strategies for which there is evidence (e.g., pediatric severe traumatic brain injury, post-cardiac arrest care, and ischemic stroke and hemorrhagic stroke); and (5) best practices for neuroprognostication in pediatric traumatic brain injury, cardiac arrest, and disorders of consciousness, with highlights of the 2023 updates on Brain Death/Death by Neurological Criteria. Our review of the current state of pediatric neurocritical care from the viewpoint of what a pediatric neurologist in the PICU needs to know is intended to improve knowledge for providers at the bedside with the goal of better patient care and outcomes.

Metabolome-Wide Mendelian Randomization Assessing the Causal Role of Serum and Cerebrospinal Metabolites in Traumatic Brain Injury

Previous studies have identified metabolites as biomarkers or potential therapeutic targets for traumatic brain injury (TBI). However, the causal association between them remains unknown. Therefore, we investigated the causal effect of serum metabolites and cerebrospinal fluid (CSF) metabolites on TBI susceptibility through Mendelian randomization (MR). Genetic variants related to metabolites and TBI were extracted from a corresponding genome-wide association study (GWAS). Causal effects were estimated through the inverse variance weighted approach, supplemented by a weighted median, weight mode, and the MR-Egger test. In addition, sensitivity analyses were further performed to evaluate the stability of the MR results, including the MR-Egger intercept, leave-one-out analysis, Cochrane's Q-test, and the MR-PRESSO global test. Metabolic pathway analysis was applied to uncover the underlying pathways of the significant metabolites in TBI. In blood metabolites, substances such as 4-acetaminophen sulfate and kynurenine showed positive links, whereas beta-hydroxyisovalerate and creatinine exhibited negative correlations. CSF metabolites such as N-formylanthranilic acid were positively related, while kynurenate showed negative associations. The metabolic pathway analysis highlighted the potential biological pathways involved in TBI. Of these 16 serum metabolites, 11 CSF metabolites and metabolic pathways may serve as useful circulating biomarkers in clinical screening and prevention, and may be candidate molecules for the exploration of mechanisms and drug targets.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

PaCO2 Association with Traumatic Brain Injury Patients Outcomes at High Altitude: A Prospective Single-Center Cohort Study

Background

partial pressure of carbon dioxide (PaCO2) is generally known to influence outcome in patients with traumatic brain injury (TBI) at normal altitudes. Less is known about specific relationships of PaCO2 levels and clinical outcomes at high altitudes.

Methods

This is a prospective single-center cohort of consecutive TBI patients admitted to a trauma center located at 2600 meter above sea level. An unfavorable outcome was defined as the Glasgow Outcome Scale-Extended (GOSE) < 4 at 6-month follow-up.

Results

81 patients with complete data, 80% (65/81) were men, and median (IQR) age was 36 (25-50) years). Median Glasgow Coma Scale (GCS) on admission was 9 (6-14), 49% (40/81) were severe (GCS: 3-8), 32% (26/81) moderate (GCS 12 - 9), and 18% (15/81) mild (GCS 13-15) TBI. The median (IQR) Abbreviated Injury Score of the Head (AISh) was 3 (2-4). Frequency of an unfavorable outcome (GOSE < 4) was 30% (25/81), median GOSE was 4 (2-5), and 6-month mortality was 24% (20/81). Comparison between patients with favorable and unfavorable outcomes revealed that those with unfavorable outcome were older, median [49 (30-72) vs. 29 (22-41), P < 0.01], had lower admission GCS [6 (4-8) vs. 13 (8-15), P < 0.01], higher AIS head [4 (4-4) vs. 3(2-4), p < 0.01], higher APACHE II score [17(15-23) vs 10 (6-14), < 0.01), higher Charlson score [0(0-2) vs. 0 (0-0), P < 0.01] and higher PaCO2 (mmHg), mean ± SD, 39 ± 9 vs. 32 ± 6, P < 0.01. In a multivariate analysis, age (OR 1.14 95% CI 1.1-1.30, P < 0.01), AISh (OR 4.7 95% CI 1.55-21.0, P < 0.05), and PaCO2 (OR 1.23 95% CI: 1.10-1.53, P < 0.05) were significantly associated with the unfavorable outcomes. When applying the same analysis to the subgroup on mechanical ventilation, AISh (OR 5.4 95% CI: 1.61-28.5, P = 0.017) and PaCO2 (OR 1.36 95% CI: 1.13-1.78, P = 0.015) remained significantly associated with the unfavorable outcome.

Conclusion

Higher PaCO2 levels are associated with an unfavorable outcome in ventilated TBI patients. These results underscore the importance of PaCO2 level in TBI patients and whether it should be adjusted for populations living at higher altitudes.

Importance of serum IRAK3 as a biochemical marker in relation to severity and neurological outcome of human severe traumatic brain injury: A prospective longitudinal cohort study

BACKGROUND

Interleukin-1 receptor-associated kinase 3 (IRAK3) may modulate inflammation in brain immunity. We determined the prognostic role of serum IRAK3 in severe traumatic brain injury (sTBI).

METHODS

In this prospective longitudinal cohort study, serum IRAK3 concentrations of 131 sTBI patients and 131 controls were quantified. Extended Glasgow outcome scale (GOSE) scores of 1-4 at 180 days after trauma signified a poor prognosis. Univariate and multivariate analyses were sequentially adopted to appraise severity correlations and prognosis associations.

RESULTS

There were significantly higher serum IRAK3 concentrations in patients than in controls. Serum IRAK3 concentrations of patients were independently correlated with Glasgow coma scale (GCS) scores, Rotterdam computed tomography (CT) scores and posttraumatic180-day GOSE scores. Also, IRAK3 concentrations were independently associated with 180-day poor prognosis, but not with death. Prognosis prediction model, in which GCS scores, Rotterdam scores and serum IRAK3 concentrations were merged, was portrayed using the nomogram. The model was rather stable, clinically usable and efficiently discriminative of poor prognosis under calibration curve, decision curve and receiver operating characteristic curve.

CONCLUSIONS

A substantial enhancement of serum IRAK3 concentrations after head trauma is independently related to severity and neurological outcome, substantializing serum IRAK3 as a promising prognostic biomarker of sTBI.