Mortality In Traumatic Brain Injury Patients Research Articles

Blood Urea Nitrogen-to-Albumin Ratio May Predict Mortality in Patients with Traumatic Brain Injury from the MIMIC Database: A Retrospective Study.

Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights the need for comprehensive outcome prediction, considering not just neurological but also systemic factors. Existing approaches relying on newly developed biomolecules face challenges in clinical implementation. Therefore, we investigated the potential of readily available clinical indicators, like the blood urea nitrogen-to-albumin ratio (BAR), for improved mortality prediction in TBI. In this study, we investigated the significance of the BAR in predicting all-cause mortality in TBI patients. In terms of research methodologies, we gave preference to machine learning methods due to their exceptional performance in clinical support in recent years. Initially, we obtained data on TBI patients from the Medical Information Mart for Intensive Care database. A total of 2602 patients were included, of whom 2260 survived and 342 died in hospital. Subsequently, we performed data cleaning and utilized machine learning techniques to develop prediction models. We employed a ten-fold cross-validation method to obtain models with enhanced accuracy and area under the curve (AUC) (Light Gradient Boost Classifier accuracy, 0.905 ± 0.016, and AUC, 0.888; Extreme Gradient Boost Classifier accuracy, 0.903 ± 0.016, and AUC, 0.895; Gradient Boost Classifier accuracy, 0.898 ± 0.021, and AUC, 0.872). Simultaneously, we derived the importance ranking of the variable BAR among the included variables (in Light Gradient Boost Classifier, the BAR ranked fourth; in Extreme Gradient Boost Classifier, the BAR ranked sixth; in Gradient Boost Classifier, the BAR ranked fifth). To further evaluate the clinical utility of BAR, we divided patients into three groups based on their BAR values: Group 1 (BAR < 4.9 mg/g), Group 2 (BAR ≥ 4.9 and ≤10.5 mg/g), and Group 3 (BAR ≥ 10.5 mg/g). This stratification revealed significant differences in mortality across all time points: in-hospital mortality (7.61% vs. 15.16% vs. 31.63%), as well as one-month (8.51% vs. 17.46% vs. 36.39%), three-month (9.55% vs. 20.14% vs. 41.84%), and one-year mortality (11.57% vs. 23.76% vs. 46.60%). Building on this observation, we employed the Cox proportional hazards regression model to assess the impact of BAR segmentation on survival. Compared to Group 1, Groups 2 and 3 had significantly higher hazard ratios (95% confidence interval (CI)) for one-month mortality: 1.77 (1.37-2.30) and 3.17 (2.17-4.62), respectively. To further underscore the clinical potential of BAR as a standalone measure, we compared its performance to established clinical scores, like sequential organ failure assessment (SOFA), GCS, and acute physiology score III(APS-III), using receiver operator characteristic curve (ROC) analysis. Notably, the AUC values (95%CI) of the BAR were 0.67 (0.64-0.70), 0.68 (0.65-0.70), and 0.68 (0.65-0.70) for one-month mortality, three-month mortality, and one-year mortality. The AUC value of the SOFA did not significantly differ from that of the BAR. In conclusion, the BAR is a highly influential factor in predicting mortality in TBI patients and should be given careful consideration in future TBI prediction research. The blood urea nitrogen-to-albumin ratio may predict mortality in TBI patients.

Liver fibrosis score is associated with the mortality of traumatic brain injury patients.

The fibrosis-4 score is a marker of liver fibrosis and has been confirmed to be associated with the prognosis of various diseases. There is no study exploring the prognostic value of the fibrosis-4 score in traumatic brain injury (TBI) patients. We design this study to explore the association between the fibrosis-4 score and mortality from TBI. TBI patients from the Medical Information Mart for Intensive Care-III database were extracted for the study. Univariate and multivariate logistic regressions were sequentially performed to analyze the association between fibrosis-4 and mortality in TBI. The area under the receiver operating characteristic curve (AUC) was drawn to evaluate the prognostic value of fibrosis-4 and other scores. A total of 1018 TBI patients were included, with a 30-day mortality of 24.2%. Non-survivors had older age, lower Glasgow Coma Scale (GCS), and higher injury severity score (ISS) than survivors. The aspartate aminotransferase platelet ratio index (APRI) and fibrosis-4 score were significantly higher in non-survivors. Univariate logistic regression showed that age, GCS, ISS, white blood cell, hemoglobin, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were associated with the mortality of TBI patients. Multivariate logistic regression presented that age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were independent risk factors of mortality in TBI patients after adjusting for confounding effects. The AUC of the GCS, ISS, APRI, and fibrosis-4 score for predicting mortality was 0.711, 0.625, 0.592, and 0.627, respectively. Composed of age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants, the predictive model had the highest AUC value of 0.790. The fibrosis-4 score is an independent risk factor for mortality in TBI. The model incorporating fibrosis-4 performs well in predicting the prognosis of TBI patients.

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department.

Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms. A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality. A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes. SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram.

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality. Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions. The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Serum cystatin C is correlated with mortality of traumatic brain injury patients partially mediated by acute kidney injury.

Evaluating risk of poor outcome for Traumatic Brain Injury (TBI) in early stage is necessary to make treatment strategies and decide the need for intensive care. This study is designed to verify the prognostic value of serum cystatin C in TBI patients. 415 TBI patients admitted to West China hospital were included. Logistic regression was performed to explore risk factors of mortality and testify the correlation between cystatin C and mortality. Mediation analysis was conducted to test whether Acute Kidney Injury (AKI) and brain injury severity mediate the relationship between cystatin C level and mortality. Area under the receiver operating characteristic curve (AUC) was used to evaluate the prognostic value of cystatin C and the constructed model incorporating cystatin C. The mortality rate of 415 TBI patients was 48.9%. Non-survivors had lower GCS (5 vs 8, p < 0.001) and higher cystatin C (0.92 vs 0.71, p < 0.001) than survivors. After adjusting confounding effects, multivariate logistic regression indicated GCS (p < 0.001), glucose (p < 0.001), albumin (p = 0.009), cystatin C (p < 0.001) and subdural hematoma (p = 0.042) were independent risk factors of mortality. Mediation analysis showed both AKI and brain injury severity exerted mediating effects on relationship between cystatin C and mortality of included TBI patients. The AUC of combining GCS with cystatin C was 0.862, which was higher than that of GCS alone (Z = 1.7354, p < 0.05). Both AKI and brain injury severity are mediating variables influencing the relationship between cystatin C and mortality of TBI patients. Serum cystatin C is an effective prognostic marker for TBI patients.

Comparision between the Glasgow Coma Scale and Glasgow Coma Scale Pupil reactivity score in predicting mortality in traumatic brain injury patients

Introduction: Glasgow Coma Scale (GCS) and the pupillary response are the key indicators of the severity of traumatic brain injury (TBI). Glasgow coma scale- Pupil reactivity (GCS-P) score is a tool to incorporate pupil reactivity and GCS into a simple single index. The main aim of this study was to compare GCS and GCS- P scores in predicting mortality in TBI patients in our institution. Materials and Methods: All patients admitted to Tribhuvan University Teaching Hospital (TUTH) with moderate to severe head injury from May 2018 to April 2019 were included in the study. Both GCS and GCS- P scores were recorded separately at admission. Outcome was measured with Glasgow Outcome Scale (GOS) at the time of discharge and in three months. Diagnostic accuracy of both these scoring systems were calculated using receiver-operating characteristics (ROC) curve, and correlation between them was estimated by using Pearson correlation coefficient. Results: Out of 136 patients enrolled, 98 patients had favorable outcome, 38 patients had unfavorable outcome at discharge. The Pearson correlation coefficient between GCS and GOS at discharge was 0.721 and GCS-P and GOS was 0.740 showing a good correlation between the GCS and GOS and GCS-P and GOS. The areas under ROC curve for GCS for prediction of mortality was 0.856 (95% CI; p&lt;0.001) and for GCS-P is 0.871(95%CI;p&lt;0.001) suggesting good discriminatory ability of both models. However, on statistical analysis, the discriminatory ability of GCS-P was not superior to GCS for mortality. Conclusion: GCS-P is as good as but not superior to GCS in predicting mortality in traumatic brain injury patients.  

Comparison of Frailty Indices and the Charlson Comorbidity Index in Traumatic Brain Injury.

Comorbidity scales for outcome prediction in traumatic brain injury (TBI) include the 5-component modified Frailty Index (mFI-5), the 11-component modified Frailty Index (mFI-11), and the Charlson Comorbidity Index (CCI). To compare the accuracy in predicting clinical outcomes in TBI of mFI-5, mFI-11, and CCI. The National Trauma Data Bank (NTDB) of the American College of Surgeons (ACS) was utilized to study patients with isolated TBI for the years of 2017 and 2018. After controlling for age and injury severity, individual multivariable logistic regressions were conducted with each of the 3 scales (mFI-5, mFI-11, and CCI) against predefined outcomes, including any complication, home discharge, facility discharge, and mortality. All 3 scales demonstrated adequate internal consistency throughout their individual components (0.63 for mFI-5, 0.60 for CCI, and 0.56 for mFI-11). Almost all studied complications were significantly more likely in frail patients. mFI-5 and mFI-11 had similar areas under the curve (AUC) for all outcomes, while CCI had lower AUCs (0.62-0.61-0.53 for any complication, 0.72-0.72-0.52 for home discharge, 0.78-0.78-0.53 for facility discharge, and 0.71-0.70-0.52 for mortality, respectively). mFI-5 and mFI-11 demonstrated similar accuracy in predicting any complication, home discharge, facility discharge, and mortality in TBI patients across the NTDB. In addition, CCI's performance was poor for the aforementioned metrics. Since mFI-5 is simpler, yet as accurate as the 2 other scales, it may be the most practical both for clinical practice and for future studies with the NTDB.

The Significance of Intracranial Pressure Monitoring for Reducing Mortality in Patients with Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Background Despite guidelines provided by the Brain Trauma Foundation (BTF) for treating patients with TBI, including advice to monitor intracranial pressure (ICP), the clinical application of ICP monitoring is far from universal. This laxity has been attributed to the relationship between mortality in TBI patients and ICP monitoring. Objective This systematic review and meta-analysis was aimed at determining the effect of intracranial pressure (ICP) monitoring on the mortality of patients with traumatic brain injury (TBI). Method A systematic search for articles was conducted on PubMed, Scopus, Cochrane Central Register of Control Trials (CENTRAL), and APA PsycNet for articles published from 1 January 2000 to 1 August 2022. Manager 5.4 was used to carry out statistical analysis. Results Article search yielded 1421 articles, but only 23 cohort studies were included in the systematic review and meta-analysis. The total number of study participants is 80,058. Seventeen studies reported unadjusted odds ratios (OR), and only 8 reported the adjusted odds ratio (OR). Nine out of seventeen studies reported an unadjusted OR of less than 1, and five out of eight studies reported an adjusted OR of less than 1. From this paper's analysis, the OR for in-hospital mortality was 1.01 [95% CI, 0.80, 1.28], with a p value of 0.92. OR for ICU mortality was 0.84 [95% CI, 0.52, 1.35], with a p value of 0.47. Conclusion But due to conflicting results, as evident above, it is unsatisfyingly challenging to draw any substantial conclusions from them. This paper thus calls for more research on this particular paper.

Initial Serum Magnesium Level Is Associated with Mortality Risk in Traumatic Brain Injury Patients.

Background: Electrolyte disorder is prevalent in traumatic brain injury (TBI) patients. This study is designed to explore the association between initial serum magnesium levels and mortality of TBI patients. Methods: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were screened for this study. Logistic regression analysis was used to explore risk factors for mortality of included TBI patients. The restricted cubic spline (RCS) was applied to fit the correlation between initial serum magnesium level and mortality of TBI. Results: The 30-day mortality of included TBI patients was 17.0%. Patients with first-tertile and third-tertile serum magnesium levels had higher mortality than those of the second tertile. Univariate regression analysis showed that the serum magnesium level was not associated with mortality. Unadjusted RCS indicated the relationship between serum magnesium level mortality was U-shaped. After adjusting confounding effects, multivariate regression analysis presented that serum magnesium level was positively associated with mortality. Conclusion: TBI patients with abnormally low or high levels of serum magnesium both have a higher incidence of mortality. At the same time, a higher initial serum magnesium level is independently associated with mortality in TBI patients. Physicians should pay attention to the clinical management of TBI patients, especially those with higher serum magnesium levels.

Risk of pre-existing hyponatremia and mortality in patients with traumatic brain injury across age groups

Traumatic brain injurySodiumClinical outcomes.

Glasgow coma scale compared to other trauma scores in discriminating in-hospital mortality of traumatic brain injury patients admitted to urban Indian hospitals: A multicentre prospective cohort study

BackgroundGlasgow Coma Scale (GCS) is one of the most commonly used trauma scores and is a good predictor of outcome in traumatic brain injury (TBI) patients. There are other more complex scores with additional physiological parameters. Whether they discriminate better than GCS in predicting mortality in TBI patients is debatable. The aim of this study was to compare the discrimination of GCS with that of MGAP, GAP, RTS and KTS for 24-hour and 30-day in-hospital mortality in adult TBI patients, in a resource limited LMIC setting. MethodWe analysed data from the multicentre, observational trauma cohort Towards Improved Trauma Care Outcome (TITCO) in India. We included all patients 18 years or older, admitted from the emergency department with TBI. The Area Under the Receiver Operating Characteristic (AUROC) curve was used to quantify and compare the discrimination of all scores: GCS; Revised Trauma Score (RTS); mechanism, GCS, age, systolic blood pressure (MGAP); GCS, age, systolic blood pressure (GAP) and Kampala Trauma Score (KTS) in the prediction of 24-hour and 30-day in-hospital mortality. ResultsA total of 3306 TBI patients were included in this study. The majority were within the GCS range 3-8. The commonest mechanism of injury was road traffic injuries [1907(58.0%)]. In-hospital mortality was 27.2% (899). There was no significant difference in discrimination in 24-hour in-hospital mortality when comparing GCS with MGAP and GAP. While GCS performed better than KTS, RTS performed better than GCS. For 30-day in-hospital mortality, GCS discriminated significantly better compared with KTS, but there was no significant difference when compared to MGAP and RTS. GAP discriminated significantly better when compared with GCS. ConclusionThis study shows that the discrimination of GCS is comparable to that of more complex trauma scores in predicting 24-hour and 30-day in-hospital mortality in adult TBI patients in a resource limited LMIC setting.

UPLC/Q-TOF MS-Based Urine Metabonomics Study to Identify Diffuse Axonal Injury Biomarkers in Rat.

Diffuse axonal injury (DAI) represents a frequent traumatic brain injury (TBI) type, significantly contributing to the dismal neurological prognosis and high mortality in TBI patients. The increase in mortality can be associated with delayed and nonspecific initial symptoms in DAI patients. Additionally, the existing approaches for diagnosis and monitoring are either low sensitivity or high cost. Therefore, novel, reliable, and objective diagnostic markers should be developed to diagnose and monitor DAI prognosis. Urine is an optimal sample to detect biomarkers for DAI noninvasively. Therefore, the DAI rat model was established in this work. Meanwhile, the ultraperformance liquid chromatography quadrupole-time-of-flight hybrid mass spectrometry- (UPLC/Q-TOF MS-) untargeted metabolomics approach was utilized to identify the features of urine metabolomics to diagnose DAI. This work included 57 metabolites with significant alterations and 21 abnormal metabolic pathways from the injury groups. Three metabolites, viz., urea, butyric acid, and taurine, were identified as possible biomarkers to diagnose DAI based on the great fold changes (FCs) and biological functions during DAI. The present study detected several novel biomarkers for noninvasively diagnosing and monitoring DAI and helped understand the DAI-associated metabolic events.

Cerebral Pulsatility Index and In-Hospital Mortality in Chinese Patients with Traumatic Brain Injury: A Retrospective Cohort Study.

There are limited studies on the relationship between the vascular transcranial Doppler (TCD) pulsatility index (PI) and in-hospital mortality in patients with traumatic brain injury (TBI). To address this issue, we conducted this study to explore whether, in newly diagnosed Chinese TBI patients, the PI is an independent predictor of the in-hospital mortality rate after adjusting for other covariates. This study is a retrospective cohort study. From 24 March 2019 to 24 January 2020, we recruited 144 Chinese patients with newly diagnosed TBI from a Chinese hospital. The independent variable was the PI, and the dependent variable was in-hospital mortality in TBI patients. The relationship between the PI and in-hospital mortality in TBI patients was nonlinear and had an inflection point of 1.11. In the multivariate analysis, after adjusting for potential confounders, the effect sizes and confidence intervals per additional 0.1 units on the left and right sides of the inflection point were 4.09 (1.30–12.83) and 1.42 (0.93–2.17). The relationship between the PI and in-hospital mortality was nonlinear. The PI was positively related with in-hospital mortality when the PI was less than 1.11.

Predictors of mortality in neurosurgical patients after traumatic brain injury

Introduction: Traumatic brain injury (TBI) is considered a relevant worldwide cause of mortality and morbidity. Objective: To evaluate independent factors at hospital admission and during hospitalization that predicts in-hospital mortality in patients with TBI submitted to neurosurgery. Methods: Retrospective cohort analyzed data of patients that underwent neurosurgery due to the TBI. Evaluated predictor factors were age, gender, Glasgow Coma Scale (GCS) at admission, sequels before and after neurosurgery, trauma mechanism, and neurosurgical procedure. The survival analyses were verified. Positive and negative predictive values were calculated. Results: A total of 312 patients were analyzed, majority male (81.4%) with mild TBI (55.8%), and mean age of 49.6 ± 23.5 years. GCS at admission was an independent predictor of mortality (HR = 0.87; 95% CI: 0.81 to 0.93; p &lt; 0.001). The GCS ≤ 13 was the optimal cut-off value to predict survival with a significant difference between GCS groups (≤13 or &gt;13; p&lt;0.001). Patients with GCS ≤ 13 had a six times greater risk of mortality (HR 6.04; 95 CI 2.39 to 15.3; p&lt;0.001). The positive and negative predictive values were 35.8% and 97%, respectively. Conclusions: The GCS in admission is an important predictor of in-hospital mortality in TBI patients that need a neurosurgical procedure.

Association between serum concentrations of anti-apoptotic B-cell lymphoma-2 protein and traumatic brain injury mortality

ABSTRACT Background There are scarce and contradictory data existing about B-cell lymphoma 2 (Bcl2), one of the Bcl2 family of anti-apoptotic proteins, in traumatic brain injury (TBI) patients. Thus, the objective of this study was to analyze whether blood concentrations of Bcl2 are associated with mortality. Methods Patients with isolated and severe TBI, defined as <10 points of the Injury Severity Score (ISS) in non-cranial aspects and <9 points in Glasgow Coma Scale (GCS), were included. This was an observational and prospective study carried out in five Intensive Care Units. Serum Bcl2 concentrations on day 1 of TBI were determined. Results Serum Bcl2 concentrations were lower (p < 0.001) in surviving patients (n = 59) compared to non-survivors (n = 24). We found an association between serum Bcl2 levels and mortality controlling for age and GCS (OR = 1.149; 95% CI = 1.056–1.251; p = 0.001) and controlling for computer tomography findings (OR = 1.147; 95% CI = 1.056–1.246; p = 0.001). Conclusions This study reports for the first time an association between serum Bcl2 levels and 30-day mortality in TBI patients.

Beta blocker use in traumatic brain injury based on the high-sensitive troponin status (BBTBBT): methodology and protocol implementation of a double-blind randomized controlled clinical trial

BackgroundBeta-adrenergic receptor blockers (BB) play an important role in the protection of organs that are susceptible for secondary injury due to stress-induced adrenergic surge. However, the use of BB in traumatic brain injury (TBI) patients is not yet the standard of care which necessitates clear scientific evidence to be used. The BBTBBT study aims to determine whether early administration of propranolol based on the high-sensitive troponin T(HsTnT) status will improve the outcome of TBI patients. We hypothesized that early propranolol use is effective in reducing 10- and 30-day mortality in TBI patients. Secondary outcomes will include correlation between serum biomarkers (troponin, epinephrine, cytokines, enolase, S100 calcium binding protein B) and the severity of injury and the impact of BB use on the duration of hospital stay and functional status at a 3-month period.MethodsThe BBTBBT study is a prospective, randomized, double-blinded, placebo-controlled three-arm trial of BB use in mild-to-severe TBI patients based on the HsTnT status. All enrolled patients will be tested for HsTnT at the first 4 and 6 h post-injury. Patients with positive HsTnT will receive BB if there is no contraindication (group 1). Patients with negative HsTnT will be randomized to receive either propranolol (group 2) or placebo (group 3). The time widow for receiving the study treatment is the first 24 h post-injury.DiscussionEarly BB use may reduce the catecholamine storm and subsequently the cascade of immune and inflammatory changes associated with TBI. HsTnT could be a useful fast diagnostic and prognostic tool in TBI patients. This study will be of great clinical interest to improve survival and functional outcomes of TBI patients.Trial registrationClinicalTrials.gov NCT04508244. Registered on 7 August 2020. Recruitment started on 29 December 2020 and is ongoing.

Early Antibiotic Administration is Independently Associated with Improved Survival in Traumatic Brain Injury

Central and systemic immune dysfunction after traumatic brain injury (TBI) can lead to infectious-related complications, which may result in delayed mortality. The role of early empiric antibiotics after TBI has not been characterized to date, but is recommended in select cases to decrease complications. We aimed to determine the relationship between early antibiotic use and in-hospital mortality in TBI patients. A retrospective review was conducted of TBI patients requiring ICU admission at an urban, academic, Level I trauma center from 01/2014 to 08/2016. Data collection included demographics, injury characteristics, details regarding antibiotic use, and outcomes. Early antibiotic administration was defined as any antibiotic given within 48 hs from admission. Patients given early antibiotics (EARLY) were compared to those who received their first dose later or did not receive any antibiotics (non-EARLY). Of the 488 TBI patients meeting inclusion criteria, 189 (38.7%) received early antibiotics. EARLY patients were younger (EARLY 54.2 versus non-EARLY 61.5 ys, P <0.01) and more likely to be male (71.4% versus 60.9%, P=0.02). Injury severity scores (23.6 versus 17.2, P <0.01) and regional head abbreviated injury scale scores (3.9 versus 3.7, P <0.01) were significantly higher in patients who received early antibiotics. Unadjusted in-hospital mortality rates were similar, however EARLY was associated with a lower mortality rate (AOR 0.17, 95% CI: 0.07 - 0.43, adjusted P <0.01) after adjusting for confounders. Despite presenting with a higher injury burden, TBI patients who received early antibiotics had a lower associated mortality rate compared to their counterparts. Future investigations are necessary to understand the underlying mechanisms that result in this potential survival benefit.

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury.

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

A Prognostic Model Incorporating Red Cell Distribution Width to Platelet Ratio for Patients with Traumatic Brain Injury.

BackgroundAs an inflammation-based marker, red cell distribution width to platelet ratio (RPR) has been verified to be associated with disease severity and outcome in many clinical settings. We designed this study to evaluate the prognostic value of RPR in patients with traumatic brain injury (TBI).MethodsA total of 420 patients admitted with TBI were included in this study. Laboratory and clinical data were collected from an electronic medical record system. Univariate and multivariate logistic regression analyses were sequentially performed to discover risk factors of in-hospital mortality. Receiver operating characteristic (ROC) curves were drawn to confirm the predictive value of different markers including RPR in training set and testing set.ResultsNon-survivors had higher level of RPR than survivors (P<0.001). Logistic regression analysis showed that RPR was significantly associated with mortality even after adjusting for confounding factors (P<0.001). The area under the ROC curve (AUC) value of Glasgow Coma Scale (GCS) for predicting mortality was 0.761 and 0775 in training set and testing set, respectively. And the constructed predictive model incorporating RPR had the highest AUC value of 0.858 and 0.884 in training set and testing set.ConclusionRPR is significantly associated with mortality in TBI patients. Utilizing RPR to construct a predictive model is valuable to evaluate prognosis of TBI patients.

Death after discharge: prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy

BackgroundDespite advances in decompressive craniectomy (DC) for the treatment of traumatic brain injury (TBI), these patients are at risk of having a poor long-term prognosis. The aim of this study was to predict 1-year mortality in TBI patients undergoing DC using logistic regression and random tree models.MethodsThis was a retrospective analysis of TBI patients undergoing DC from January 1, 2015, to April 25, 2019. Patient demographic characteristics, biochemical tests, and intraoperative factors were collected. One-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were used to evaluate model performance.ResultsOf the 230 patients, 70 (30.4%) died within 1 year. Older age (OR, 1.066; 95% CI, 1.045–1.087; P < 0.001), higher Glasgow Coma Score (GCS) (OR, 0.737; 95% CI, 0.660–0.824; P < 0.001), higher d-dimer (OR, 1.005; 95% CI, 1.001–1.009; P = 0.015), coagulopathy (OR, 2.965; 95% CI, 1.808–4.864; P < 0.001), hypotension (OR, 3.862; 95% CI, 2.176–6.855; P < 0.001), and completely effaced basal cisterns (OR, 3.766; 95% CI, 2.255–6.290; P < 0.001) were independent predictors of 1-year mortality. Random forest demonstrated better performance for 1-year mortality prediction, which achieved an overall accuracy of 0.810, sensitivity of 0.833, specificity of 0.800, and AUC of 0.830 on the testing data compared to the logistic regression model.ConclusionsThe random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC. Further external tests are required to verify our prognostic model.