Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity & mortality

BackgroundThere is evidence to suggest that traumatic brain injuries (TBI) are increasing in the United States. It is important to examine predictors of TBI outcomes to formulate better prevention and care strategies.Research designNational Trauma Data Bank (NTDB) data from 2016 were used to report the percentage of TBI by age, sex, race/ethnicity, health insurance status, intent/mechanism of injury, Glasgow Coma Scale (GCS), disposition at emergency department, and trauma center level. Logistic regression models were run to estimate the adjusted odds ratios of patient and facility characteristics on length of hospital stay and in-hospital mortality (analyzed in 2020).ResultsThere were 236,873 patients with TBI in the NTDB in 2016. Most patients with a TBI were male, non-Hispanic white, and had sustained a TBI due to an unintentional injury. After adjusting for other factors, individuals age 0-17, those who self-pay, and those with intentional injuries had increased odds of a shorter hospital stay. Older individuals, non-Hispanic black or Hispanic patients, those who had sustained an intentional injury, and those who were not seen in a Level I trauma center had higher odds of mortality following their TBI.ConclusionsPublic health professionals’ promotion of fall and other TBI prevention efforts and the development of strategies to improve access to Level I trauma centers, may decrease adverse TBI health outcomes. This may be especially important for older adults and other vulnerable populations.     

External validation of the PRESTO pediatric tool for predicting in-hospital mortality from traumatic injury

BackgroundBenchmarking is crucial for quality improvement of trauma systems. The Pediatric Resuscitation and Trauma Outcome (PRESTO) model allows risk-adjusted comparisons of in-hospital mortality for pediatric trauma populations in under-resourced environments. Our aim was to validate PRESTO in a high-resource setting using provincial Trauma Registry (TR) data and compare it to the standard benchmarking model, the Injury Severity Score (ISS).MethodsThis retrospective case-control study collected demographic, vital sign, and outcome data from the TR for patients aged <16 years sustaining major trauma from 2013 to 2021. The PRESTO model estimates predicted probability of in-hospital mortality (Pm) using the age, heart rate, blood pressure, oxygen saturation, neurological status, and use of airway supplementation. PRESTO was assessed by comparison of Pm in patients who died and survived and comparison of area under the receiver–operator curve (AUROC) with that of ISS. Statistical analysis was performed using R.ResultsWe included 647 patients, of which 69 died in-hospital (11%). The cohort was 37% female, with a median age of 8 and median ISS of 17. The median Pm for cases was significantly higher compared to controls (1.0 vs. 5.2 × 10⁻⁵, p < 0.001). The AUROC for PRESTO and ISS were not significantly different (0.819 and 0.816, respectively; p = 0.95).ConclusionPRESTO is valid in a resource-rich environment, such as a Canadian province. It performs equally well to ISS but is simpler to derive. In the future, PRESTO may serve to benchmark levels of in-hospital mortality within or across institutions over time across Canada.Level of evidence3     

Preoperative serum lactate cannot predict in-hospital mortality after decompressive craniectomy in traumatic brain injury

Purpose

Despite the utility of serum lactate for predicting clinical courses, little information is available on the topic after decompressive craniectomy. This study was conducted to determine the ability of perioperative serum lactate levels to predict in-hospital mortality in traumatic brain-injury patients who received emergency or urgent decompressive craniectomy.

Methods

The medical records of 586 consecutive patients who underwent emergency or urgent decompressive craniectomy due to traumatic brain injuries from January 2007 to December 2014 were retrospectively analyzed. Pre- and intraoperative serum lactate levels and base deficits were obtained from arterial blood gas analysis results.

Results

The overall mortality rate after decompressive craniectomy was 26.1 %. Mean preoperative serum lactate was significantly higher in the non-survivors (P = 0.034) than the survivors but had no significance for predicting in-hospital mortality in the multivariate regression analysis (P = 0.386). Rather, preoperative Glasgow Coma Score was a significant predictor for in-hospital mortality (hazard ratio 0.796, 95 % confidence interval 0.755–0.836, P < 0.001).

Conclusion

Preoperative lactate level is not an independent predictor of in-hospital mortality after decompressive craniectomy in traumatic brain-injury patients.

     

Predicting progressive hemorrhagic injury after traumatic brain injury: derivation and validation of a risk score based on admission characteristics

Abstract Previous studies have demonstrated that patients with traumatic brain injury (TBI) who also have progressive hemorrhagic injury (PHI), have a higher risk of clinical deterioration and worse outcomes than do TBI patients without PHI. Therefore, the early prediction of PHI occurrence is useful to evaluate the status of patients with TBI and to improve outcomes. The objective of this study was to develop and validate a prognostic model that uses information available at admission to determine the likelihood of PHI after TBI. Retrospectively collected data were used to develop a PHI prognostic model with a logistic regression analysis. The prediction model was validated in 114 patients from a separate hospital. Eight independent prognostic factors were identified: age ≥57 years (5 points), intra-axial bleeding/brain contusion (4 points), midline shift≥5?mm (6 points), platelet (PLT) count<100×10(9)/L (10 points), PLT count≥100 but <150×10(9)/L (4 points), prothrombin time>14?sec (7 points), D-dimer≥5?mg/L (12 points), and glucose≥10?mmol/L (10 points). Each patient was assigned a number of points proportional to the regression coefficient. We calculated risk scores for each patient and defined three risk groups: low risk (0-13 points), intermediate risk (14-22 points), and high risk (23-54 points). In the development cohort, the PHI rates after TBI for these three groups were 10.3%, 47.3%, and 85.2%, respectively. In the validation cohort, the corresponding PHI rates were 10.9%, 47.3%, and 86.9%. The C-statistic for the point system was 0.864 (p=0.509 by the Hosmer-Lemeshow test) in the development cohort, and 0.862 (p=0.589 by the Hosmer-Lemeshow test) in the validation cohort. In conclusion, a relatively simple risk score using admission predictors accurately predicted the risk for PHI after TBI.     

Predicting outcome after traumatic brain injury: development and validation of a prognostic score based on admission characteristics

The early prediction of outcome after traumatic brain injury (TBI) is important for several purposes, but no prognostic models have yet been developed with proven generalizability across different settings. The objective of this study was to develop and validate prognostic models that use information available at admission to estimate 6-month outcome after severe or moderate TBI. To this end, this study evaluated mortality and unfavorable outcome, that is, death, and vegetative or severe disability on the Glasgow Outcome Scale (GOS), at 6 months post-injury. Prospectively collected data on 2269 patients from two multi-center clinical trials were used to develop prognostic models for each outcome with logistic regression analysis. We included seven predictive characteristics-age, motor score, pupillary reactivity, hypoxia, hypotension, computed tomography classification, and traumatic subarachnoid hemorrhage. The models were validated internally with bootstrapping techniques. External validity was determined in prospectively collected data from two relatively unselected surveys in Europe (n = 796) and in North America (n = 746). We evaluated the discriminative ability, that is, the ability to distinguish patients with different outcomes, with the area under the receiver operating characteristic curve (AUC). Further, we determined calibration, that is, agreement between predicted and observed outcome, with the Hosmer-Lemeshow goodness-of-fit test. The models discriminated well in the development population (AUC 0.78-0.80). External validity was even better (AUC 0.83-0.89). Calibration was less satisfactory, with poor external validity in the North American survey (p < 0.001). Especially, observed risks were higher than predicted for poor prognosis patients. A score chart was derived from the regression models to facilitate clinical application. Relatively simple prognostic models using baseline characteristics can accurately predict 6-month outcome in patients with severe or moderate TBI. The high discriminative ability indicates the potential of this model for classifying patients according to prognostic risk.     

Mortality following combined burn and traumatic brain injuries: An analysis of the national trauma data bank of the American College of Surgeons

BackgroundSevere burn and traumatic brain injuries (TBI) lead to significant mortality, and combined burn-TBI injuries may predispose towards even worse outcomes. The purpose of this study was to investigate the mortality of patients with burn, burn with non-TBI trauma, and combined burn/TBI to determine if combined injury portends a worse outcome.MethodsWe obtained the National Trauma Data Bank from 2007 to 2012, identifying 32,334 patients with burn related injuries, dividing this cohort into three injury types: BURN ONLY, BURN with TRAUMA/NO TBI, and BURN with TBI. For each patient, demographic data was obtained, including age, gender, presence of trauma, TBI, or inhalation injury, burn total body surface area (TBSA), Glasgow Coma Scale, Injury Severity Score, and mortality. Multivariable logistic regression was performed.ResultsAge, gender, and TBSA were similar across the three injury groups, but the incidence of inhalation injury was doubled in the BURN with TRAUMA/NO TBI (15.4 %) and BURN with TBI (15.3 %) groups when compared to the BURN ONLY (7.2 %) group. Mortality differed across injury categories after adjusting for age, TBSA, and inhalation injury. Increased mortality was seen in BURN with TRAUMA/NO TBI versus BURN ONLY (OR = 1.27 [1.06, 1.53]) and was higher when comparing BURN with TBI versus BURN ONLY (OR = 4.22 [2.85, 6.18]). BURN with TBI also had higher mortality when compared to BURN with TRAUMA/NO TBI (OR = 3.33 [2.30, 4.82]). The logs odds of mortality also increased with increasing age, TBSA and presence of inhalation injury.DiscussionThis analysis of the NTDB suggests that mortality following burn-related injuries may be higher when burn injury is combined with TBI when compared to burns with other trauma, even after correcting for age, TBSA, and inhalation injury. Further clinical and laboratory research is needed to validate these findings and better understand how to optimize combined TBI and burn injury treatment.     

Predictors of in-hospital mortality and 6-month functional outcomes in older adults after moderate to severe traumatic brain injury

Introduction

Traumatic brain injury (TBI) is the single largest cause of death and disability following injury worldwide. While TBI in older adults is less common, it still contributes to significant morbidity and mortality in this group. Understanding the patient characteristics that result in good and poor outcome after TBI is important in the clinical management and prognosis of older adult TBI patients. This population-based study investigated predictors of mortality and longer term functional outcomes following serious TBI in older adults.

Methods

All older adults (aged > 64 years), isolated moderate to severe TBI cases from the population-based Victorian State Trauma Registry for the period July 2005 to June 2007 (inclusive) were extracted for analysis. Demographic, injury event, injury diagnosis, management and comorbid status information were obtained and the outcomes of interest were in-hospital mortality, and the Glasgow Outcome Scale-Extended (GOS-E) score at 6 months post-injury. Multivariate logistic regression analyses were used to identify independent predictors of in-hospital mortality and independent living (GOS-E > 4) status at 6 months.

Results

Of the 428 isolated, older adult TBI cases, the majority were the result of a fall (88%), male (55%), and aged > 74 years (76%). The in-hospital death rate was 28% and increasing age (p = 0.009), decreasing GCS (p < 0.001) and injury type (p = 0.002) were significant independent predictors of in-hospital mortality. Of the 310 patients who survived to discharge, 65% were successfully followed-up 6 months following injury. There was no difference between patients lost to follow-up and those successfully followed-up with respect to the key population indicators of age, gender, or head injury severity. Younger (<75 years) patients, and those with an SBP on arrival at hospital of 131-150 mmHg, were at increased odds of living independently at follow-up. No patients with a GCS < 9 had a good 6-month outcome, and most of them died. The survival rate for brainstem injury was also low (21%).

Conclusion

In this population-based study, we found that age, GCS, brainstem injury, and systolic blood pressure were the most important factors in predicting outcome in older adults with an isolated moderate to severe TBI.     

External validation of the traumatic aortic injury score

Aim

Traumatic aortic injury (TAI) is a serious complication of blunt chest trauma. The traumatic aortic injury score (TRAINS) is a clinical tool for risk determination, with patients scoring <4 considered low risk. The aim of the present study was to determine the sensitivity of TRAINS on a population of blunt trauma patients with TAI at an Australian major trauma centre.

Patients and Methods

Patients diagnosed with thoracic TAI between 2006 and 2014 were identified from an institutional registry. Radiological studies (chest X‐ray) were reviewed, while blood pressure on arrival to hospital was extracted from the registry. Using abbreviated injury scale codes, the presence of the five associated injuries included in the TRAINS model was determined.

Results

A TRAINS ≥4 was observed in 28 out of 63 cases, with complete data available (sensitivity = 44.4 per cent; 95 per cent confidence interval: 32.8–56.7), with minimum and maximum possible sensitivities of 42.4 per cent and 47 per cent, respectively, as determined by two‐way sensitivity analysis.

Conclusions

This independent external validation of the TRAINS concluded a poor sensitivity for excluding TAI in the blunt chest trauma population. In the absence of reliable predictive tools, a low threshold for thoracic computed tomography imaging and clinical gestalt remain essential tools for the early diagnosis of TAI.     

External validation of a predictive algorithm for in-hospital and 90-day mortality after spinal epidural abscess

BACKGROUND CONTEXTMortality in patients with spinal epidural abscess (SEA) remains high. Accurate prediction of patient-specific prognosis in SEA can improve patient counseling as well as guide management decisions. There are no externally validated studies predicting short-term mortality in patients with SEA.PURPOSEThe purpose of this study was to externally validate the Skeletal Oncology Research Group (SORG) stochastic gradient boosting algorithm for prediction of in-hospital and 90-day postdischarge mortality in SEA.STUDY DESIGN/SETTINGRetrospective, case-control study at a tertiary care academic medical center from 2003 to 2021.PATIENT SAMPLEAdult patients admitted for radiologically confirmed diagnosis of SEA who did not initiate treatment at an outside institution.OUTCOME MEASURESIn-hospital and 90-day postdischarge mortality.METHODSWe tested the SORG stochastic gradient boosting algorithm on an independent validation cohort. We assessed its performance with discrimination, calibration, decision curve analysis, and overall performance.RESULTSA total of 212 patients met inclusion criteria, with a short-term mortality rate of 10.4%. The area under the receiver operating characteristic curve (AUROC) of the SORG algorithm when tested on the full validation cohort was 0.82, the calibration intercept was -0.08, the calibration slope was 0.96, and the Brier score was 0.09.CONCLUSIONSWith a contemporaneous and geographically distinct independent cohort, we report successful external validation of a machine learning algorithm for prediction of in-hospital and 90-day postdischarge mortality in SEA.     

External validation of a modified trauma and injury severity score model in major trauma injury

BackgroundThe establishment of an accurate prognostic model in major trauma patients is important mainly because this group of patients will benefit the most. Clinical prediction models must be validated internally and externally on a regular basis to ensure the prediction is accurate and current. This study aims to externally validate two prediction models, the Trauma and Injury Severity Score model developed using the Major Trauma Outcome Study in North America (MTOS-TRISS model), and the NTrD-TRISS model, which is a refined MTOS-TRISS model with coefficients derived from the Malaysian National Trauma Database (NTrD), by regarding mortality as the outcome measurement.MethodThis retrospective study included patients with major trauma injuries reported to a trauma centre of Hospital Sultanah Aminah over a 6-year period from 2011 and 2017. Model validation was examined using the measures of discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The Hosmer-Lemeshow (H-L) goodness-of-fit test was used to examine calibration capabilities. The predictive validity of both MTOS-TRISS and NTrD-TRISS models were further evaluated by incorporating parameters such as the New Injury Severity Scale and the Injury Severity Score.ResultsTotal patients of 3788 (3434 blunt and 354 penetrating injuries) with average age of 37 years (standard deviation of 16 years) were included in this study. All MTOS-TRISS and NTrD-TRISS models examined in this study showed adequate discriminative ability with AUCs ranged from 0.86 to 0.89 for patients with blunt trauma mechanism and 0.89 to 0.99 for patients with penetrating trauma mechanism. The H-L goodness-of-fit test indicated the NTrD-TRISS model calibrated as good as the MTOS-TRISS model for patients with blunt trauma mechanism.ConclusionFor patients with blunt trauma mechanism, both the MTOS-TRISS and NTrD-TRISS models showed good discrimination and calibration performances. Discrimination performance for the NTrD-TRISS model was revealed to be as good as the MTOS-TRISS model specifically for patients with penetrating trauma mechanism. Overall, this validation study has ascertained the discrimination and calibration performances of the NTrD-TRISS model to be as good as the MTOS-TRISS model particularly for patients with blunt trauma mechanism.     

Association of antiplatelet or anticoagulant agents with in-hospital mortality among blunt torso trauma patients without severe traumatic brain injury: A retrospective analysis of the Japanese nationwide trauma registry

AimPatients with head trauma who take antiplatelet or anticoagulant (APAC) agents have a higher rate of mortality. However, the association between these agents and mortality among blunt torso trauma patients without severe traumatic brain injury remains unclear.MethodsUsing the Japanese nationwide trauma registry, we conducted a retrospective cohort study including adult patients with blunt torso trauma without severe head trauma between January 2019 and December 2020. Eligible patients were divided into two groups based on whether or not they took any APAC agents. The primary outcome was in-hospital mortality. To adjust for potential confounding factors, we conducted random effects logistic regression to account for patients clustering within the hospitals. The model was adjusted for potential confounders, including age, mechanism of injury, Charlson comorbidity index, systolic blood pressure, and injury severity scale on arrival as potentially confounding factors.ResultsDuring the study period, 16,201 patients were eligible for the analysis. A total of 832 patients (5.1%) were taking antiplatelet or anticoagulant agents. Overall in-hospital mortality was 774 patients (4.8%). APAC group had a higher risk of in-hospital mortality compared with the non-APAC group (6.9% vs. 4.7%; unadjusted OR, 1.51; 95% CI, 1.12–2.00; P < 0.01). After adjusting for potential confounder, there were no significant intergroup difference in a higher in-hospital mortality compared to with the non-APAC group (OR, 1.07; 95%CI, 0.65–1.77; P = 0.79).ConclusionThe use of APAC agents before the injury was not associated with higher in-hospital mortality among blunt torso trauma patients without severe traumatic brain injury.     

A standardized trauma care protocol decreased in-hospital mortality of patients with severe traumatic brain injury at a teaching hospital in a middle-income country

Introduction

Standardized trauma protocols (STP) have reduced morbidity and in-hospital mortality in mature trauma systems. Most hospitals in low- and middle-income countries (LMICs) have not implemented STPs, often because of financial and logistic limitations. We report the impact of an STP designed for the care of trauma patients in the emergency department (ED) at an LMIC hospital on patients with severe traumatic brain injury (STBI).

Methods

We developed an STP based on generally accepted best practices and damage control resuscitation for a level I trauma centre in Colombia. Without a pre-existing trauma registry, we adapted an administrative electronic database to capture clinical information of adult patients with TBI, a head abbreviated injury score (AIS) ≥3, and who presented ≤12 h from injury. Demographics, mechanisms of injury, and injury severity were compared. Primary outcome was in-hospital mortality. Secondary outcomes were Glasgow Coma Score (GCS), length of hospital and ICU stay, and prevalence of ED interventions recommended in the STP. Logistic regression was used to control for potential confounders.

Results

The pre-STP group was hospitalized between August 2010 and August 2011, the post-STP group between September 2011 and June 2012. There were 108 patients meeting inclusion criteria, 68 pre-STP implementation and 40 post-STP. The pre- and post-STP groups were similar in age (mean 37.1 vs. 38.6, p = 0.644), head AIS (median 4.5 vs. 4.0, p = 0.857), Injury Severity Scale (median 25 vs. 25, p = 0.757), and initial GCS (median 7 vs. 7, p = 0.384). Post-STP in-hospital mortality decreased (38% vs. 18%, p = 0.024), and discharge GCS increased (median 10 vs. 14, p = 0.034). After controlling for potential confounders, odds of in-hospital mortality post-STP compared to pre-STP were 0.248 (95%CI: 0.074–0.838, p = 0.025). Hospital and ICU stay did not significantly change. The use of many ED interventions increased post-STP, including bladder catheterization (49% vs. 73%, p = 0.015), hypertonic saline (38% vs. 63%, p = 0.014), arterial blood gas draws (25% vs. 43%, p = 0.059), and blood transfusions (3% vs. 18%, p = 0.008).

Conclusions

An STP in an LMIC decreased in-hospital mortality, increased discharge GCS, and increased use of vital ED interventions for patients with STBI. An STP in an LMIC can be implemented and measured without a pre-existing trauma registry.     

Pediatric firearm injury trends in the United States: A national trauma data bank (NTDB) analysis

Aim of studyFirearm injuries are the second leading cause of injury-related death in the USA in children less than 18. We compared overall and intent-specific firearm hospitalizations across age group and race to understand recent trends.MethodsA retrospective cohort of 20,083 children from the national trauma data bank involved in incidences of firearm discharges from 2013 to 2017 was divided by race, discharge intent, gun type, and four age groups: infants (ages 0–2), toddlers (3–6), children (7–12), and adolescents (13–18). Discharge intent and gun type were determined by ICD-9-CM and ICD-10-CM External Causes of Injury codes. Injury severity score (ISS), hospital discharge disposition, and length of stay (LOS) were used as predictors for morbidity and mortality.ResultsFrom 2013 to 2017, there were on an average 4016 firearm injuries reported to the NTDB (n = 20,083). 71% (n = 14,313) of the incidents were assaults. Victims who identified as African American and Caucasian made up 60% (n = 11,890) and 26% (n = 5162) of the total victims, respectively, and were predominantly male (86%, n = 17,202). Victims who identified as Caucasian made up 70% of suicides (n = 819), while African Americans made up 70% of assaults (n = 9733). 87% (n = 17,525) of the patient population were adolescents. The number of accidental firearm discharges compared to firearm assaults was greater for toddlers, while the opposite was true for infants, children, and adolescents. Average LOS by age group varied each year, though children had the highest total average LOS.  Average ISS was highest for infants and adolescents (11.5 and 10.4) and American Indians and Caucasians (10.1 and 11.1). Overall mortality rate was 6% (n = 1220) and had no significant differences between years. Mortality rate was highest for infants (7%, n = 25).ConclusionsAdolescent males and African Americans were disproportionately affected by overall and assault-specific firearm discharges. Firearm injury prevention legislation is critical to prevent these injures.     

Pre-hospital and in-hospital parameters and outcomes in patients with traumatic brain injury: A comparison between German and Australian trauma registries

Introduction

In Germany, physician-operated emergency medical services (EMS) manage most pre-hospital trauma care. Australia uses a different EMS system, deploying highly trained paramedics for road and air transport of trauma patients. The effect of these different systems on secondary insults to traumatic brain injury (TBI) patients is unclear. There is conflicting evidence regarding which system is preferable. To add to the body of evidence, we compared the profile of injury, pre-hospital management and outcomes of TBI patients from both populations.

Methods

Cases aged ≥16 years, with AIS head ≥3, AIS other body parts ≤3, recorded in the Victorian State Trauma Registry (VSTR) and Trauma Registry of the German Society of Trauma Surgery (TR-DGU) from 2002 to 2007 were compared.

Results

10,183 cases (5665 German, 4518 Australian) were included. No difference in sex or median age was observed. There were major between-registry differences in type of injury, trauma circumstance, intent and severity of injury. German cases sustained more serious injury and received more pre-hospital interventions. Mortality was significantly higher amongst German patients even when adjusted for demographics, injury severity and in- and pre-hospital parameters. German patients had a longer hospital and ICU stay.

Conclusion

There were clear differences in injury characteristics and outcomes in TBI patients between Germany and Australia. As differences in coding, data collection and patient selection are evident, firm conclusions regarding the contribution of variations in pre-hospital care are not possible. The differences in outcome deserve further exploration in prospective studies.     

External validation of the U-HIP prediction model for in-hospital mortality in geriatric hip fracture patients

IntroductionIdentification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery.Materials and methodsIn this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated.ResultsA total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72–0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71–0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibration-in-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75–0.81), with good calibration (calibration slope 0.934).Discussion and conclusionThe U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making.     

Glial fibrillary acidic protein in serum after traumatic brain injury and multiple trauma

BACKGROUND: This study aimed to determine whether glial fibrillary acidic protein (GFAP) is released after traumatic brain injury (TBI), whether GFAP is related to brain injury severity and outcome after TBI, and whether GFAP is released after multiple trauma without TBI. METHODS: This prospective study enrolled 114 patients who had TBI with or without multiple trauma (n = 101) or multiple trauma without TBI (n = 13), as verified by computerized tomography. Daily GFAP measurement began at admission (<12 hours after trauma) and continued for the duration of intensive care (1-22 days). Documentation included categorization of computerized tomography according to Marshall classification, based on daily highest intracranial pressure (ICP), lowest cerebral perfusion pressure (CPP), lowest mean arterial pressure (MAP), and 3-month Glasgow Outcome Score (GOS). RESULTS: The GFAP concentration was lower for diffuse injury 2 than for diffuse injury 4 (p < 0.0005) or nonevacuated mass lesions larger than than 25 mL (p < 0.005), lower for a ICP less than 25 mm Hg than for a ICP of 25 mm Hg or more, lower for a CPP of 60 mm Hg or more than for a CPP of 60 mm Hg or less, lower for a MAP of 60 mm Hg or more than for a MAP less than 60 mm Hg (all p < 0.0005), and lower for a GOS of 1 or 2 than for a GOS of 3, 4 (p < 0.05), or 5 (p < 0.0005). After TBI, GFAP was higher in nonsurvivors (n = 39) than in survivors (n = 62) (p < 0.005). After multiple trauma without TBI, GFAP remained normal. CONCLUSIONS: The findings showed that GFAP is released after TBI, that GFAP is related to brain injury severity and outcome after TBI, and that GFAP is not released after multiple trauma without brain injury.