Prognosis Of Traumatic Brain Injury Research Articles

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

Expression and significance of hypoxia-inducible factor 1α and Bcl-2/adenovirus E1B19kDa-interacting protein 3 in children with traumatic brain injury

To dynamically observe the changes in hypoxia-inducible factor 1α (HIF-1α) and Bcl-2/adenovirus E1B19kDa-interacting protein 3 (BNIP3) in children with traumatic brain injury (TBI) and evaluate their clinical value in predicting the severity and prognosis of pediatric TBI. A prospective study included 47 children with moderate to severe TBI from January 2021 to July 2023, categorized into moderate (scores 9-12) and severe (scores 3-8) subgroups based on the Glasgow Coma Scale. A control group consisted of 30 children diagnosed and treated for inguinal hernia during the same period, with no underlying diseases. The levels of HIF-1α, BNIP3, autophagy-related protein Beclin-1, and S100B were compared among groups. The predictive value of HIF-1α, BNIP3, Beclin-1, and S100B for the severity and prognosis of TBI was assessed using receiver operating characteristic (ROC) curves. Serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in the TBI group were higher than those in the control group (P<0.05). Among the TBI patients, the severe subgroup had higher levels of HIF-1α, BNIP3, Beclin-1, and S100B than the moderate subgroup (P<0.05). Correlation analysis showed that the serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were negatively correlated with the Glasgow Coma Scale scores (P<0.05). After 7 days of treatment, serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in both non-surgical and surgical TBI patients decreased compared to before treatment (P<0.05). ROC curve analysis indicated that the areas under the curve for predicting severe TBI based on serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were 0.782, 0.835, 0.872, and 0.880, respectively (P<0.05), and for predicting poor prognosis of TBI were 0.749, 0.775, 0.814, and 0.751, respectively (P<0.05). Serum levels of HIF-1α, BNIP3, and Beclin-1 are significantly elevated in children with TBI, and their measurement can aid in the clinical assessment of the severity and prognosis of pediatric TBI.

Atox1 protects hippocampal neurons after traumatic brain injury via DJ-1 mediated anti-oxidative stress and mitophagy

Regulation of the oxidative stress response is crucial for the management and prognosis of traumatic brain injury (TBI). The copper chaperone Antioxidant 1 (Atox1) plays a crucial role in regulating intracellular copper ion balance and impacting the antioxidant capacity of mitochondria, as well as the oxidative stress state of cells. However, it remains unknown whether Atox1 is involved in modulating oxidative stress following TBI. Here, we investigated the regulatory role of Atox1 in oxidative stress on neurons both in vivo and in vitro, and elucidated the underlying mechanism through culturing hippocampal HT-22 cells with Atox1 mutation. The expression of Atox1 was significantly diminished following TBI, while mice with overexpressed Atox1 exhibited a more preserved hippocampal structure and reduced levels of oxidative stress post-TBI. Furthermore, the mice displayed notable impairments in learning and memory functions after TBI, which were ameliorated by the overexpression of Atox1. In the stretch injury model of HT-22 cells, overexpression of Atox1 mitigated oxidative stress by preserving the normal morphology and network connectivity of mitochondria, as well as facilitating the elimination of damaged mitochondria. Mechanistically, co-immunoprecipitation and mass spectrometry revealed the binding of Atox1 to DJ-1. Knockdown of DJ-1 in HT-22 cells significantly impaired the antioxidant capacity of Atox1. Mutations in the copper-binding motif or sequestration of free copper led to a substantial decrease in the interaction between Atox1 and DJ-1, with overexpression of DJ-1 failing to restore the antioxidant capacity of Atox1 mutants. The findings suggest that DJ-1 mediates the ability of Atox1 to withstand oxidative stress. And targeting Atox1 could be a potential therapeutic approach for addressing post-traumatic neurological dysfunction.

Clinical value of serum LINC02446 and S100B in early diagnosis and prognosis assessment of traumatic brain injury

Objective To detect the expression levels of LINC02446 and S100B in serum of patients with traumatic brain injury (TBI) and explore their values as diagnostic and prognostic indicators for TBI. Method Abnormal expressed RNAs in brain injury were screened from the dataset GSE1131475. Serums were collected from moderate to severe TBI patients at 1–3 and 4–12 h post injury. Quantitative polymerase chain reaction was used to detect the expression levels of LINC02446 and S100B in serum. The Glasgow Outcome Scale was used for prognostic evaluation. The diagnostic and prognostic efficacy of LINC02446 and S100B in TBI was evaluated using the receiver operating characteristic (ROC) curve. Result The serum expression levels of LINC02446 and S100B in the TBI group were significantly increased. The expression levels of LINC02446 and S100B in the severe TBI group were significantly higher than those in the mild TBI group. ROC curve analysis showed that the combination of LINC02446 and S100B can distinguish TBI patients from healthy controls, as well as mild TBI from moderate to severe TBI. At the 6-month follow-up, the expression levels of LINC02446 and S100B in TBI patients with poor prognosis were significantly higher than those in patients with good prognosis, and ROC results showed their differentiation value. Moreover, the expression level of LINC02446 at 0–3 h can serve as an independent prognostic factor for poor prognosis. Conclusion Serum LINC02446 and S100B hold clinical application value in the diagnosis and prognosis of TBI and are expected to become new potential biomarkers.

GSK’872 Improves Prognosis of Traumatic Brain Injury by Switching Receptor-Interacting Serine/Threonine-Protein Kinase 3-dependent Necroptosis to Cysteinyl Aspartate Specific Proteinase-8-Dependent Apoptosis

Traumatic brain injury (TBI) is an important health concern in the society. Previous studies have suggested that necroptosis occurs following TBI. However, the underlying mechanisms and roles of necroptosis are not well understood. In this study, we aimed to assess the role of receptor-interacting serine/threonine-protein kinase 3 (RIP3)-mediated necroptosis after TBI both in vitro and in vivo. We established a cell-stretching injury and mouse TBI model by applying a cell injury controller (CIC) and controlled cortical impactor (CCI) to evaluate the relationships among necroptosis, apotosis, inflammation, and TBI both in vitro and in vivo. The results revealed that necroptosis mediated by receptor-interacting serine/threonine-protein kinase 1 (RIP1), RIP3, and mixed lineage kinase domain-like protein (MLKL) was involved in secondary TBI. Additionally, protein kinase B (Akt), phosphorylated Akt (p-Akt), mammalian target of rapamycin (mTOR), and phosphorylated mTOR (p-mTOR) potentially contribute to necroptosis. The inhibition of RIP3 by GSK'872 (a specific inhibitor) blocked necroptosis and reduced the activity of Akt/mTOR, leading to the alleviation of inflammation by reducing the levels of NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3). Moreover, the inhibition of RIP3 by GSK'872 promoted the activity of cysteinyl aspartate specific proteinase-8(Caspase-8), an enzyme involved in apoptosis and inflammation. These data demonstrate that RIP3 inhibition could improve the prognosis of TBI, based on the attenuation of inflammation by switching RIP3-dependent necroptosis to Caspase-8-dependent apoptosis.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms

ObjectiveThis study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI). MethodsA single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complexity algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale (GCS) score on the first postoperative day and Glasgow Outcome Scale-Extended (GOSE) score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, three multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients’ discharge GOSE. ResultsThe logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge GOSE, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance. ConclusionsThis study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

Relationship between sodium level and in-hospital mortality in traumatic brain injury patients of MIMIC IV database.

An association between prognosis and high sodium levels in Traumatic Brain Injury (TBI) patients in Intensive Care Units (ICUs) has been noted, but limited research exists on the ideal sodium level in these patients or the impact on early mortality, using the MIMIC-IV database. A retrospective survey was conducted on TBI patients from the MIMIC-IV database. Patients were divided into two categories based on their highest serum sodium level within 24 h of admission exceeding 145 mmol/L: those with hypernatremia, and those with moderate-to-low sodium levels. Collected covariates encompasses demographic, clinical, laboratory, and intervention variables. A multivariate logistic regression model was implemented to forecast in-hospital mortality. The study included 1749 TBI patients, with 209 (11.5%) experiencing in-hospital deaths. A non-linear test exposed an L-shaped correlation between sodium level and in-hospital mortality, with mortality rates increasing after a turning point at 144.1 mmol/L. Compared to the moderate-to-low group's 9.3% mortality rate, the hypernatremia group had a significantly higher mortality rate of 25.3% (crude odds ratio = 3.32, 95% confidence interval: 2.37 ~ 4.64, p < 0.001). After adjusting for all covariates, the hypernatremia group continued to show a significant correlation with higher mortality risk (adjusted odds ratio = 2.19, 95% confidence interval: 1.38 ~ 3.47, p = 0.001). This trend remained consistent regardless of the analyses stratification. The study reveals an L-shaped relationship between sodium levels and in-hospital deaths, with a pivotal point at 144.1 mmol/L. TBI patients displaying hypernatremia were independently linked to higher in-hospital mortality, underlining the need for further studies into targeted management of sodium levels in these patients.

A Self‐Gelling Powder Directly Co‐Assembled by Natural Small Molecules for Traumatic Brain Injury

AbstractThe prognosis for traumatic brain injury (TBI) depends largely on prompt hemostasis and effective pharmacologic interventions. Natural all‐small‐molecule self‐gelling powder, integrating the advantages of self‐assembled small‐molecule hydrogels and powders, is expected to provide timely and effective prehospital management of TBI. However, the synthesis and application of natural all‐small‐molecule self‐gelling powder is still uncharted territory. In this study, an all‐small‐molecule co‐assembled MGF‐H3BO3‐RUT (MBR) self‐gelling powder is fabricated through the co‐assembly of mangiferin (MGF) and rutin (RUT) in H3BO3/NaOH aqueous solution. Both compounds can bind with boric acid, leading to co‐assembling into hydrogels through hydrogen‐bonding interactions and π–π stacking. MBR self‐gelling powder is then obtained by drying the as‐prepared hydrogels, thus integrating hemostasis and pharmacodynamics into one. Remarkably, it displays robust regeneration capabilities, while retaining excellent self‐healing properties and injectability after drying‐hydration cycles. Moreover, MBR self‐gelling powder not only achieves rapid effective hemostasis but also attenuates conspicuously cerebral edema and inflammatory response after TBI by in situ spraying, exhibiting notable neuroprotective effects without discernible toxic side effects. This study provides a novel assembly strategy and application form for self‐assembled gel materials originating from natural small molecules, offering promising avenues for the treatment of TBI in the acute phase.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.

Intraoperative application of intelligent, responsive, self-assembling hydrogel rectifies oxygen and energy metabolism in traumatically injured brain

In managing severe traumatic brain injury (TBI), emergency surgery involving the removal of damaged brain tissue and intracerebral hemorrhage is a priority. Secondary brain injury caused by oxidative stress and energy metabolic disorders, triggered by both primary mechanical brain damage and surgical insult, is also a determining factor in the prognosis of TBI. Unfortunately, the effectiveness of traditional postoperative intravenous neuroprotective agents therapy is often limited by the lack of targeting, timeliness, and side effects when neuroprotective agents systemically delivered. Here, we have developed injectable, intelligent, self-assembling hydrogels (P-RT/2DG) that can achieve precise treatment through intraoperative application to the target area. P-RT/2DG hydrogels were prepared by integrating a reactive oxygen species (ROS)-responsive thioketal linker (RT) into polyethylene glycol. By scavenging ROS and releasing 2-deoxyglucose (2DG) during degradation, these hydrogels functioned both in antioxidation and energy metabolism to inhibit the vicious cycle of post-TBI ROS–lactate which provoked secondary injury. In vitro and in vivo tests confirmed the absence of systemic side effects and the neuroprotective function of P-RT/2DG hydrogels in reducing edema, nerve cell apoptosis, neuroinflammation, and maintaining the blood-brain barrier. Our study thus provides a potential treatment strategy with novel hydrogels in TBI.

The value of computed tomography angiography in predicting the surgical effect and prognosis of severe traumatic brain injury

It is difficult to predict the surgical effect and outcome of severe traumatic brain injury (TBI) before surgery. This study aims to approve an evaluation method of computed tomography angiography (CTA) to predict the effect of surgery and outcome in severe TBI. Between January 2010 and January 2020, we retrospectively reviewed 358 severe TBI patients who underwent CTA at admission and reexamination. CTA data were evaluated for the presence of cerebrovascular changes, including cerebrovascular shift (CS), cerebral vasospasm (CVS), large artery occlusion (LAO), and deep venous system occlusion (DVSO). Medical records were reviewed for baseline clinical characteristics and the relationship between CTA changes and outcomes. Cerebrovascular changes were identified in 247 (69.0%) of 358 severe TBI patients; only 25 (10.12%) of them had poor outcomes, and 162 (65.6%) patients had a good recovery. Eighty-three (23.18%) patients were diagnosed with CVS, 10 (12.05%) had a good outcome, 57 (68.67%) had severe disability and 16 (19.28%) had a poor outcome. There were twenty-six (7.3%) patients who had LAO and thirty-one (8.7%) patients who had DVSO; no patients had good recovery regardless of whether they had the operation or not. Cerebrovascular injuries and changes are frequent after severe TBI and correlate closely with prognosis. CTA is an important tool in evaluating the severity, predicting the operation effect and prognosis, and guiding therapy for severe TBI. Well-designed, multicenter, randomized controlled trials are needed to evaluate the value of CTA for severe TBI in the future.

Sex-specific impacts of obesity on long-term prognosis of traumatic brain injury: a multicenter prospective study

Sex-specific impacts of obesity on long-term prognosis of traumatic brain injury: a multicenter prospective study

Antibody-labeled gold nanoparticle based resonance Rayleigh scattering detection of S100B.

Traumatic brain injury (TBI) is a sudden brain injury due to an external force that causes a large number of deaths and permanent disabilities every year. S100B has been recognized as a potential objective quantitative biomarker for screening the prognosis of TBI and severe head injury. In this article, an anti-S100B monoclonal antibody was immobilized on cysteamine (Cy) functionalized gold nanoparticles (AuNPs) by EDC-NHS chemistry, which enabled S100B resonance Rayleigh scattering (RRS) detection based on antibody-labeled gold nanoparticles. The prepared conjugates were characterized by ultraviolet-visible spectroscopy (UV-vis), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS) and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Based on the specific binding of the antibody and antigen, the RRS intensities at 381 nm and 541 nm wavelengths were significantly enhanced, and thus a dual wavelength overlapping resonance Rayleigh scattering (DWO-RRS) method was established. The scattering intensity of the two overlapping peaks was proportional to the concentration of S100B in the range of 0.05-4.5 ng mL-1 with a detection limit of 0.002 ng mL-1. The proposed DWO-RRS method is time-saving, simple, sensitive, and can be used to determine the concentration of S100B in human serum with satisfactory results, which has a promising application in the early diagnosis of TBI.

Integrating fractional amplitude of low-frequency fluctuation and functional connectivity to investigate the mechanism and prognosis of severe traumatic brain injury.

Functional magnetic resonance imaging (fMRI) has been used for evaluating residual brain function and predicting the prognosis of patients with severe traumatic brain injury (sTBI). This study aimed to integrate the fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to investigate the mechanism and prognosis of patients with sTBI. Sixty-five patients with sTBI were included and underwent fMRI scanning within 14 days after brain injury. The patient's outcome was assessed using the Glasgow Outcome Scale-Extended (GOSE) at 6 months post-injury. Of the 63 patients who met fMRI data analysis standards, the prognosis of 18 patients was good (GOSE scores ≥ 5), and the prognosis of 45 patients was poor (GOSE scores ≤ 4). First, we apply fALFF to identify residual brain functional differences in patients who present different prognoses and conjoined it in regions of interest (ROI)-based FC analysis to investigate the residual brain function of sTBI at the acute phase of sTBI. Then, the area under the curve (AUC) was used to evaluate the predictive ability of the brain regions with the difference of fALFF and FC values. Patients who present good outcomes at 6 months post-injury have increased fALFF values in the Brodmann area (7, 18, 31, 13, 39 40, 42, 19, 23) and decreased FC values in the Brodmann area (28, 34, 35, 36, 20, 28, 34, 35, 36, 38, 1, 2, 3, 4, 6, 13, 40, 41, 43, 44, 20, 28 35, 36, 38) at the acute phase of sTBI. The parameters of these alterations can be used for predicting the long-term outcomes of patients with sTBI, of which the fALFF increase in the temporal lobe, occipital lobe, precuneus, and middle temporal gyrus showed the highest predictive ability (AUC = 0.883). We provide a compensatory mechanism that several regions of the brain can be spontaneously activated at the acute phase of sTBI in those who present with a good prognosis in the 6-month follow-up, that is, a destructive mode that increases its fALFF in the local regions and weakens its FC to the whole brain. These findings provide a theoretical basis for developing early intervention targets for sTBI patients.

Sestrin2 can alleviate endoplasmic reticulum stress to improve traumatic brain injury by activating AMPK/mTORC1 signaling pathway.

Traumatic brain injury (TBI), as a serious central nervous system disease, can result in severe neurological dysfunction or even disability and death of patients. The early and effective intervention of secondary brain injury can improve the prognosis of TBI. Endoplasmic reticulum (ER) stress is one of the main reasons to recover TBI. ER stress inhibition may be beneficial in treating TBI. Sestrin2 is a crucial regulator of ER stress, and its activation can significantly improve TBI. In this paper, we analyze the biological function of sestrin2, the latest findings on ER stress, and the relationship between ER stress and TBI. We elucidate the relationship of sestrin2 inhibiting ER stress via activating the AMP-activated protein kinase (AMPK)/mammalian target of rapamycin complex 1 (MTORC1) signaling. Finally, we elaborate on the possible role of sestrin2 in TBI and explain how its activation potentially improves TBI.

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

Temporal change of serum xanthine oxidase levels and its relation to clinical outcome of severe traumatic brain injury: a prospective cohort study.

Xanthine oxidase (XO) may be involved in the induction of oxidative stress and inflammation. We measured serum XO levels at multiple days to determine whether it is associated with the severity and prognosis of severe traumatic brain injury (sTBI). In this prospective cohort study, we quantified serum XO levels in 112 sTBI patients and 112 controls. Serum XO levels of patients were measured at admission and at days 1, 3, 5, 7, and 10 after sTBI. Extended Glasgow outcome scale scores of 1-4 at post-trauma 180 days were defined as a poor prognosis. Multivariate analysis was employed to determine the relationship between poor prognosis and serum XO levels at multiple days. Serum XO levels were significantly increased at admission among patients, afterwards elevated gradually, peaked at day 3, and then diminished gradually until day 10, and were substantially higher during 10 days in patients than in controls. Serum XO levels at 6 different days were all correlated with admission Rotterdam computed tomography (CT) scores and Glasgow coma scale (GCS) scores. Serum XO levels at 6 different days were all substantially higher in patients with poor prognosis than in those with good prognosis. Serum XO levels at days 7 and 10, but not at days 1, 3, and 5, had significantly lower area under receiver operating characteristic (AUC) than those at admission. Serum XO levels at admission and at days 1 and 3, but not at day 5, were independently associated with 180-day poor prognosis. Prognostic prediction model containing GCS scores, Rotterdam CT scores, and serum XO levels at admission (or at days 1 and 3) showed substantially higher AUC than GCS scores and Rotterdam CT scores alone. The models were visually described using nomograms, which were comparatively stable under calibration curve and were relatively of clinical benefit under decision curve. Elevated serum XO levels during early period of sTBI are more closely associated with trauma severity and clinical adverse outcomes, assuming that serum XO may serve as a potential prognostic biomarker in sTBI.

Chronic kidney disease as a predictive factor for poor prognosis in traumatic brain injury among older adults: a case-control study

ObjectiveTraumatic brain injury (TBI) is a highly prevalent neurological disorder that affects a gradually increasing proportion of older adults. Chronic kidney disease (CKD) significantly contributes to global years of life lost, with an estimated one-tenth of the global population affected by CKD. However, it remains unclear whether CKD impacts TBI prognosis. We conducted a case-control study to investigate the clinical outcomes of TBI patients with or without CKD comorbidity and identified the risk factors associated with a poor prognosis.MethodsFrom January 2017 through April 2023, 11 patients with TBI and CKD were included, and 27 control TBI cases with normal kidney function were matched by age, gender, and admission Glasgow Coma Scale (GCS) score as the control group.ResultsThe CKD TBI group had a significantly lower GCS score upon discharge (7.1 ± 5.9) compared to the non-CKD TBI group (13.1 ± 2.6) (p &amp;lt; 0.01). ICU stay time and hospitalization expenses were higher in the CKD group than the non-CKD group, though there were no statistical differences. Additionally, patients in the CKD TBI group had a higher frequency of hospital-acquired infections (54.4%) compared with those in the non-CKD TBI group (7.4%) (p &amp;lt; 0.01). The two groups exhibited no differences in hemoglobin levels, albumin levels, or coagulation function. Logistic regression analysis showed that advanced age, low admission GCS score, elevated blood urea, and creatinine levels were associated with a poor neurological prognosis.ConclusionTBI patients comorbid with CKD have a poorer prognosis than those with normal kidney function.

Systemic Immune-Inflammation Index Predicts the Prognosis of Traumatic Brain Injury

ObjectiveSystemic inflammation following traumatic brain injury (TBI) has been extensively studied over the past decades, as it contributes significantly to the pathophysiological injury mechanisms and subsequent poor outcomes. Systemic immune-inflammation (SII) index is a novel biomarker of systemic inflammatory response. However, its predictive value regarding TBI prognosis in clinical practice remains insufficiently investigated. MethodsA total of 102 TBI patients admitted to Nanjing Drum Tower Hospital from July 2019 to February 2022 were enrolled. We employed various statistical analyses to evaluate the correlation between inflammatory indicators upon admission and patient prognosis, compared the predictive accuracy of these indicators, and generated ROC analysis to test their prognostic performance. ResultsThe SII index, platelet count (PLT), absolute lymphocyte count (ALC), and neutrophil/lymphocyte ratio (NLR) were capable of distinguishing TBI prognosis according to univariate logistic regression models (P<0.05). Multivariate logistic regression models revealed that increased SII index, PLT, and NLR upon admission were independent predictors of poor TBI prognosis (P<0.05). ROC analysis further demonstrated that the SII index (AUC=0.845, 95% CI 0.769-0.921, P=0.000) exhibited higher predictive ability than the NLR (AUC=0.694, 95% CI 0.591-0.796, P=0.001). ConclusionsOur findings suggested that increased SII index during the early stages of TBI was an independent risk factor for poor prognosis with satisfactory predictive value. The SII index provides a reliable, convenient, and cost-effective prognostic model to evaluate systemic inflammation after TBI and identify patients at risk of poor outcomes, thereby offering valuable guidance for clinical practice.