Prospective independent validation of IMPACT modeling as a prognostic tool in severe traumatic brain injury

MRI and Clinical Variables for Prediction of Outcomes After Pediatric Severe Traumatic Brain Injury

Importance

Traumatic brain injury (TBI) is a leading cause of death and disability in children, and predicting functional outcome after TBI is challenging. Magnetic resonance imaging (MRI) is frequently conducted after severe TBI; however, the predictive value of MRI remains uncertain.

Objectives

To identify early MRI measures that predict long-term outcome after severe TBI in children and to assess the added predictive value of MRI measures over well-validated clinical predictors.

Design, Setting, and Participants

This preplanned prognostic study used data from the Approaches and Decisions in Acute Pediatric TBI (ADAPT) prospective observational comparative effectiveness study. The ADAPT study enrolled 1000 consecutive children (aged <18 years) with severe TBI between February 1, 2014, and September 30, 2017. Participants had a Glasgow Coma Scale (GCS) score of 8 or less and received intracranial pressure monitoring. Magnetic resonance imaging scans performed as part of standard clinical care within 30 days of injury were collected at 24 participating sites in the US, UK, and Australia. Summary imaging measures were correlated with the Glasgow Outcome Scale-Extended for Pediatrics (GOSE-Peds), and the predictive value of MRI measures was compared with the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) core clinical predictors. Data collection, image analysis, and data analyses were completed in July 2023.

Exposures

Pediatric severe TBI with an MRI scan performed as part of clinical care.

Main Outcomes and Measures

All measures were selected a priori. Magnetic resonance imaging measures included contusion, ischemia, diffuse axonal injury, intracerebral hemorrhage, and brainstem injury. Clinical predictors included the IMPACT core measures (GCS motor score and pupil reactivity). All models adjusted for age and sex. Outcome measures included the GOSE-Peds score obtained at 3, 6, and 12 months after injury.

Results

This study included 233 children with severe TBI who were enrolled at participating sites and had an MRI scan and preselected clinical predictors available. Their median age was 6.9 (IQR, 3.0-13.3) years, and more than half of participants (134 [57.5%]) were male. In a multivariable model including MRI measures and IMPACT core clinical variables, contusion volume (odds ratio [OR], 1.13; 95% CI, 1.02-1.26), brain ischemia (OR, 2.11; 95% CI, 1.58-2.81), brainstem lesions (OR, 5.40; 95% CI, 1.90-15.35), and pupil reactivity were each independently associated with GOSE-Peds score. Adding MRI measures to the IMPACT clinical predictors significantly improved model fit and discrimination between favorable and unfavorable outcomes compared with IMPACT predictors alone (area under the receiver operating characteristic curve, 0.77; 95% CI, 0.72-0.85 vs 0.67; 95% CI, 0.61-0.76 for GOSE-Peds score >3 at 6 months after injury).

Conclusions and Relevance

In this prognostic study of children with severe TBI, the addition of MRI measures significantly improved outcome prediction over well-established and validated clinical predictors. Magnetic resonance imaging should be considered in children with severe TBI to inform prognosis and may also promote stratification of patients in future clinical trials.

Use of magnetic source imaging to assess recovery after severe traumatic brain injury-an MEG pilot study

Rationale

Severe TBI (sTBI) is a devastating neurological injury that comprises a significant global trauma burden. Early comprehensive neurocritical care and rehabilitation improve outcomes for such patients, although better diagnostic and prognostic tools are necessary to guide personalized treatment plans.

Methods

In this study, we explored the feasibility of conducting resting state magnetoencephalography (MEG) in a case series of sTBI patients acutely after injury (~7 days), and then about 1.5 and 8 months after injury. Synthetic aperture magnetometry (SAM) was utilized to localize source power in the canonical frequency bands of delta, theta, alpha, beta, and gamma, as well as DC-80 Hz.

Results

At the first scan, SAM source maps revealed zones of hypofunction, islands of preserved activity, and hemispheric asymmetry across bandwidths, with markedly reduced power on the side of injury for each patient. GCS scores improved at scan 2 and by scan 3 the patients were ambulatory. The SAM maps for scans 2 and 3 varied, with most patients showing increasing power over time, especially in gamma, but a continued reduction in power in damaged areas and hemispheric asymmetry and/or relative diminishment in power at the site of injury. At the group level for scan 1, there was a large excess of neural generators operating within the delta band relative to control participants, while the number of neural generators for beta and gamma were significantly reduced. At scan 2 there was increased beta power relative to controls. At scan 3 there was increased group-wise delta power in comparison to controls.

Conclusion

In summary, this pilot study shows that MEG can be safely used to monitor and track the recovery of brain function in patients with severe TBI as well as to identify patient-specific regions of decreased or altered brain function. Such MEG maps of brain function may be used in the future to tailor patient-specific rehabilitation plans to target regions of altered spectral power with neurostimulation and other treatments.

Clinical Grading Scales and Neuroprognostication in Acute Brain Injury

Prediction of neurological clinical outcome after acute brain injury is critical because it helps guide discussions with patients and families and informs treatment plans and allocation of resources. Numerous clinical grading scales have been published that aim to support prognostication after acute brain injury. However, the development and validation of clinical scales lack a standardized approach. This in turn makes it difficult for clinicians to rely on prognostic grading scales and to integrate them into clinical practice. In this review, we discuss quality measures of score development and validation and summarize available scales to prognosticate outcomes after acute brain injury. These include scales developed for patients with coma, cardiac arrest, ischemic stroke, nontraumatic intracerebral hemorrhage, subarachnoid hemorrhage, and traumatic brain injury; for each scale, we discuss available validation studies.

PREDICT-TBI: Comparison of Physician Predictions with the IMPACT Model to Predict 6-Month Functional Outcome in Traumatic Brain Injury

BACKGROUND

Predicting functional outcome in critically ill patients with traumatic brain injury (TBI) strongly influences end-of-life decisions and information for surrogate decision makers. Despite well-validated prognostic models, clinicians most often rely on their subjective perception of prognosis. In this study, we aimed to compare physicians' predictions with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic model for predicting an unfavorable functional outcome at 6 months after moderate or severe TBI.

METHODS

PREDICT-TBI is a prospective study of patients with moderate to severe TBI. Patients were admitted to a neurocritical care unit and were excluded if they died or had withdrawal of life-sustaining treatments within the first 24 h. In a paired study design, we compared the accuracy of physician prediction on day 1 with the prediction of the IMPACT model as two diagnostic tests in predicting unfavorable outcome 6 months after TBI. Unfavorable outcome was assessed by the Glasgow Outcome Scale from 1 to 3 by using a structured telephone interview. The primary end point was the difference between the discrimination ability of the physician and the IMPACT model assessed by the area under the curve.

RESULTS

Of the 93 patients with inclusion and exclusion criteria, 80 patients reached the primary end point. At 6 months, 29 patients (36%) had unfavorable outcome. A total of 31 clinicians participated in the study. Physicians' predictions showed an area under the curve of 0.79 (95% confidence interval 0.68-0.89), against 0.80 (95% confidence interval 0.69-0.91) for the laboratory IMPACT model, with no statistical difference (p = 0.88). Both approaches were well calibrated. Agreement between physicians was moderate (κ = 0.56). Lack of experience was not associated with prediction accuracy (p = 0.58).

CONCLUSIONS

Predictions made by physicians for functional outcome were overall moderately accurate, and no statistical difference was found with the IMPACT models, possibly due to a lack of power. The significant variability between physician assessments suggests prediction could be improved through peer reviewing, with the support of the IMPACT models, to provide a realistic expectation of outcome to families and guide discussions about end-of-life decisions.

Predictive value of the Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory rate and Systolic blood pressure score (TRIAGES) and Revised Trauma Score (RTS) for the short-term mortality of patients with isolated traumatic brain injury: A retrospective study

BACKGROUND

Ensuring rapid and precise mortality prediction in patients with traumatic brain injury (TBI) at the emergency department (ED) is paramount in patient triage and enhancing their outcomes. We aimed to estimate and compare the predictive power of the Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory rate, and Systolic blood pressure score (TRIAGES) and Revised Trauma Score (RTS) for 24-h in-hospital mortality in patients with isolated TBI.

METHODS

We conducted a retrospective single-center study analyzing clinical data from 1156 patients with isolated acute TBI treated in the ED of the Affiliated Hospital of Nantong University from January 1, 2020, to December 31, 2020. We calculated each patient's TRIAGES and RTS scores and estimated their predictive value for short-term mortality using receiver operating characteristic (ROC) curves.

RESULTS

87 patients (7.53%) died within 24 h of admission. The non-survival group had higher TRIAGES and lower RTS than the survival group. Compared to non-survivors, survivors exhibited higher Glasgow Coma Scale scores (GCS) with a median score of 15 (12, 15) compared to a median score of 4.0 (3.0, 6.0). The crude and adjusted odds ratios (ORs) for TRIAGES were 1.79, 95% CI (1.62 to 1.98) and 1.79, 95% CI (1.60 to 2.00), respectively. The crude and adjusted ORs for RTS were 0.39, 95% CI (0.33 to 0.45) and 0.40, 95% CI (0.34 to 0.47), respectively. The area under the ROC (AUROC) curve of TRIAGES, RTS, and GCS was 0.865 (0.844 to 0.884), 0.863 (0.842 to 0.882), and 0.869 (0.830 to 0.909), respectively. The optimal cut-off values for predicting 24-h in-hospital mortality were 3 for TRIAGES, 6.08 for RTS, and 8 for GCS. The subgroup analysis showed a higher AUROC in TRIAGES (0.845) compared to GCS (0.836) and RTS (0.829) among patients aged 65 and above, although the difference was not statistically significant.

CONCLUSIONS

TRIAGES and RTS have shown promising efficacy in predicting 24-h in-hospital mortality in patients with isolated TBI, with comparable performance to GCS. However, improving the comprehensiveness of assessment does not necessarily translate into an overall increase in predictive ability.

Dynamic predictors of in-hospital and 3-year mortality after traumatic brain injury: A retrospective cohort study

BACKGROUND

Mortality risks after Traumatic Brain Injury (TBI) are understudied in critical illness. We sought to identify risks of mortality in critically ill patients with TBI using time-varying covariates.

METHODS

This single-center, six-year (2006-2012), retrospective cohort study measured demographics, injury characteristics, and daily data of acute TBI patients in the Intensive Care Unit (ICU). Time-varying Cox proportional hazards models assessed in-hospital and 3-year mortality.

RESULTS

Post-TBI ICU patients (n = 2664) experienced 20% in-hospital mortality (n = 529) and 27% (n = 706) 3-year mortality. Glasgow Coma Scale motor subscore (hazard ratio (HR) 0.58, p < 0.001), pupil reactivity (HR 3.17, p < 0.001), minimum glucose (HR 1.44, p < 0.001), mSOFA score (HR 1.81, p < 0.001), coma (HR 2.26, p < 0.001), and benzodiazepines (HR 1.38, p < 0.001) were associated with in-hospital mortality. At three years, public insurance (HR 1.78, p = 0.011) and discharge disposition (HR 4.48, p < 0.001) were associated with death.

CONCLUSIONS

Time-varying characteristics influenced in-hospital mortality post-TBI. Socioeconomic factors primarily affect three-year mortality.

Performance of CRASH and IMPACT Prognostic Models for Traumatic Brain Injury at 12 and 24 Months Post-Injury

The Corticoid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic models are the most reported prognostic models for traumatic brain injury (TBI) in the scientific literature. However, these models were developed and validated to predict 6-month unfavorable outcome and mortality, and growing evidence supports continuous improvements in functional outcome after severe TBI up to 2 years post-injury. The purpose of this study was to evaluate CRASH and IMPACT model performance beyond 6 months post-injury to include 12 and 24 months post-injury. Discriminative validity remained consistent over time and comparable to earlier recovery time points (area under the curve = 0.77-0.83). Both models had poor fit for unfavorable outcomes, explaining less than one quarter of the variation in outcomes for severe TBI patients. The CRASH model had significant values for the Hosmer-Lemeshow test at 12 and 24 months, indicating poor model fit past the previous validation point. There is concern in the scientific literature that TBI prognostic models are being used by neurotrauma clinicians to support clinical decision making despite the goal of the models' development being to support research study design. The results of this study indicate that the CRASH and IMPACT models should not be used in routine clinical practice because of poor model fit that worsens over time and the large, unexplained variance in outcomes.

Performance of the IMPACT and Helsinki models for predicting 6-month outcomes in a cohort of patients with traumatic brain injury undergoing cranial surgery

Background and aim

Prediction models for patients with traumatic brain injury (TBI) require generalizability and should apply to different settings. We aimed to validate the IMPACT and Helsinki prognostic models in patients with TBI who underwent cranial surgery in a Chinese center.

Methods

This validation study included 607 surgical patients with moderate to severe TBI (Glasgow Coma Scale [GCS] score ≤12) who were consecutively admitted to the Neurotrauma Center of People's Liberation Army (PLANC), China, between 2009 and 2021. The IMPACT models (core, extended and lab) and the Helsinki CT clinical model were used to estimate 6-month mortality and unfavorable outcomes. To assess performance, we studied discrimination and calibration.

Results

In the PLANC database, the observed 6-month mortality rate was 28%, and the 6-month unfavorable outcome was 52%. Significant differences in case mix existed between the PLANC cohort and the development populations for the IMPACT and, to a lesser extent, for the Helsinki models. Discrimination of the IMPACT and Helsinki models was excellent, with most AUC values ≥0.80. The highest values were found for the IMPACT lab model (AUC 0.87) and the Helsinki CT clinical model (AUC 0.86) for the prediction of unfavorable outcomes. Overestimation was found for all models, but the degree of miscalibration was lower in the Helsinki CT clinical model.

Conclusion

In our population of surgical TBI patients, the IMPACT and Helsinki CT clinical models demonstrated good performance, with excellent discrimination but suboptimal calibration. The good discrimination confirms the validity of the predictors, but the poorer calibration suggests a need to recalibrate the models to specific settings.

Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

This review aimed to analyze the results of investigations that performed external validation or that compared prognostic models to identify the models and their variations that showed the best performance in predicting mortality, survival, and unfavorable outcome after severe traumatic brain injury. Pubmed, Embase, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, TROVE, and Open Grey databases were searched. A total of 1616 studies were identified and screened, and 15 studies were subsequently included for analysis after applying the selection criteria. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models were the most externally validated among studies of severe traumatic brain injury. The results of the review showed that most publications encountered an area under the curve ≥ 0.70. The area under the curve meta-analysis showed similarity between the CRASH and IMPACT models and their variations for predicting mortality and unfavorable outcomes. Calibration results showed that the variations of CRASH and IMPACT models demonstrated adequate calibration in most studies for both outcomes, but without a clear indication of uncertainties in the evaluations of these models. Based on the results of this meta-analysis, the choice of prognostic models for clinical application may depend on the availability of predictors, characteristics of the population, and trauma care services.

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale–Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74–0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%– 60%) explanation of ordinal variation in 6-month GOSE (Somers’ D_xy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.

The early-stage triple-negative breast cancer landscape derives a novel prognostic signature and therapeutic target

PURPOSE

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Patients with early-stage TNBCs have distinct likelihood of distant recurrence. This study aimed to develop a prognostic signature of early-stage TNBC patients to improve risk stratification.

METHODS

Using RNA-sequencing data, we analyzed 189 pathologically confirmed pT1-2N0M0 TNBC patients and identified 21 mRNAs that were highly expressed in tumor and related to relapse-free survival. All-subset regression program was used for constructing a 7-mRNA signature in the training set (n = 159); the accuracy and prognostic value were then validated using an independent validation set (n = 158).

RESULTS

Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues. Early-stage TNBCs mainly consisted of basal-like immune-suppressed subtype and had higher homologous recombination deficiency scores. We developed a prognostic signature including seven mRNAs (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2). In both the training (n = 159) and validation set (n = 158), this signature could identify patients with relatively high recurrence risks and served as an independent prognostic factor. Time-dependent receiver operating curve showed that the signature had better prognostic value than traditional clinicopathological features in both sets. Functionally, we showed that TMEM101 promoted cell proliferation and migration in vitro, which represented a potential therapeutic target.

CONCLUSIONS

Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. This model may facilitate personalized therapy decision-making for early-stage TNBCs individuals.

Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models

Background

A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH).

Method

TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction.

Results

The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8.

Conclusions

The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Predicting in-hospital mortality after traumatic brain injury: External validation of CRASH-basic and IMPACT-core in the national trauma data bank

BACKGROUND

Traumatic brain injury (TBI) prognostic prediction models offer value to individualized treatment planning, systematic outcome assessments and clinical research design but require continuous external validation to ensure generalizability to different settings. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis on Clinical Trials in TBI (IMPACT) models are widely available but lack robust assessments of performance in a current national sample of patients. The purpose of this study is to assess the performance of the CRASH-Basic and IMPACT-Core models in predicting in-hospital mortality using a nationwide retrospective cohort from the National Trauma Data Bank (NTDB).

METHODS

The 2016 NTDB was used to analyze an adult cohort with moderate-severe TBI (Glasgow Coma Scale [GCS] ≤ 12, head Abbreviated Injury Scale of 2-6). Observed in-hospital mortality or discharge to hospice was compared to the CRASH-Basic and IMPACT-Core models' predicted probability of 14-day or 6-month mortality, respectively. Performance measures included discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plots and Brier scores). Further sensitivity analysis included patients with GCS ≤ 14 and considered patients discharged to hospice to be alive at 14-days.

RESULTS

A total of 26,228 patients were included in this study. Both models demonstrated good ability in differentiating between patients who died and those who survived, with IMPACT demonstrating a marginally greater AUC (0.863; 95% CI: 0.858 - 0.867) than CRASH (0.858; 0.854 - 0.863); p < 0.001. On calibration, IMPACT overpredicted at lower scores and underpredicted at higher scores but had good calibration-in-the-large (indicating no systemic over/underprediction), while CRASH consistently underpredicted mortality. Brier scores were similar (0.152 for IMPACT, 0.162 for CRASH; p < 0.001). Both models showed slight improvement in performance when including patients with GCS ≤ 14.

CONCLUSION

Both CRASH-Basic and IMPACT-Core accurately predict in-hospital mortality following moderate-severe TBI, and IMPACT-Core performs well beyond its original GCS cut-off of 12, indicating potential utility for mild TBI (GCS 13-15). By demonstrating validity in the NTDB, these models appear generalizable to new data and offer value to current practice in diverse settings as well as to large-scale research design.

Hospital-level intracranial pressure monitoring utilization and functional outcome in severe traumatic brain injury: a post hoc analysis of prospective multicenter observational study

Background

Several observational studies have shown that hospital-level intracranial pressure (ICP) monitoring utilization varies considerably in patients with severe traumatic brain injury (TBI). However, the relationship between hospital-level ICP monitoring utilization and clinical functional outcomes is unknown. This study examined whether patients with severe TBI treated at hospitals with high ICP monitoring utilization have better functional outcomes.

Methods

A post hoc analysis of the data from a prospective multicenter cohort study in Japan was undertaken, and included severe TBI patients (Glasgow Come Scale score ≤ 8). The primary exposure was hospital-level ICP monitoring utilization. Patients treated at hospitals with more than 80% ICP monitoring utilization were assigned to a high group and the others to a low group. The primary endpoint was a favorable functional outcome at 6 months after injury, defined as a Glasgow Outcome Scale score of good recovery or moderate disability. We conducted multiple logistic regression analyses adjusted for potential confounders.

Results

Of the 427 included patients, 60 were assigned to the high group and 367 to the low group. Multiple logistic regression analysis revealed that patients in the high group had significantly better functional outcome (adjusted odds ratio [OR]: 2.36; 95% confidence interval [CI]: 1.17–4.76; p = 0.016). Multiple logistic regression analysis adjusted for additional confounders supported this result (adjusted OR: 2.30; 95% CI: 1.07–4.92; p = 0.033).

Conclusion

Treatment at hospitals with high ICP monitoring utilization for severe TBI patients could be associated with better functional outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-020-00825-7.

IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort

OBJECTIVE

To develop a robust prognostic model, the more diverse the settings in which the system is tested and found to be accurate, the more likely it will be generalisable to untested settings. This study aimed to externally validate the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization after Significant Head Injury (CRASH) models for low-income and middle-income countries using a dataset of patients with severe traumatic brain injury (TBI) from the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure study and a simultaneously conducted observational study.

METHOD

A total of 550 patients with severe TBI were enrolled in the study, and 466 of those were included in the analysis. Patient admission characteristics were extracted to predict unfavourable outcome (Glasgow Outcome Scale: GOS<3) and mortality (GOS 1) at 14 days or 6 months.

RESULTS

There were 48% of the participants who had unfavourable outcome at 6 months and these included 38% who had died. The area under the receiver operating characteristic curve (AUC) values were 0.683-0.775 and 0.640-0.731 for the IMPACT and CRASH models respectively. The IMPACT CT model had the highest AUC for predicting unfavourable outcomes, and the IMPACT Lab model had the best discrimination for predicting 6-month mortality. The discrimination for both the IMPACT and CRASH models improved with increasing complexity of the models. Calibration revealed that there were disagreement between observed and predicted outcomes in the IMPACT and CRASH models.

CONCLUSION

The overall performance of all IMPACT and CRASH models was adequate when used to predict outcomes in the dataset. However, some disagreement in calibration suggests the necessity for updating prognostic models to maintain currency and generalisability.

External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan

In Japan, a range of patients with traumatic brain injury (TBI) has been recorded in a nationwide database (Japan Neurotrauma Data Bank; JNTDB). This study aimed to externally validate three international prediction models using JNTDB data: Trauma and Injury Severity Score (TRISS), Corticosteroid Randomization After Significant Head Injury (CRASH), and International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT). We also aimed to validate the applicability of these models in the Japanese population. Of 1,091 patients registered in the JNTDB from July 2009 to June 2011, we analyzed data for 635 patients. We examined factors associated with mortality in-hospital and unfavorable outcomes 6 months after TBI by applying the TRISS, CRASH, and IMPACT models. We also conducted an external validation of these models based on these data. The patients’ mean age was 60.1 ±21.1 years, and 342 were alive at the time of discharge (53.9%). Univariate analysis revealed eight major risk factors for mortality in-hospital: age, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), systolic blood pressure, heart rate, mydriasis, acute epidural hematoma (AEDH), and traumatic subarachnoid hemorrhage. A similar analysis identified five risk factors for unfavorable outcomes at 6 months: age, GCS, ISS, mydriasis, and AEDH. For mortality in-hospital, the TRISS had a satisfactory area under the curve value (0.75). For unfavorable outcomes at 6 months, the CRASH (basic and computed tomography) and IMPACT (core and core extended) models had satisfactory area under the curve values (0.86, 0.86, 0.81, and 0.85, respectively). The TRISS, CRASH, and IMPACT models were suitable for application to the JNTDB population, indicating these models had high value in Japanese patients with neurotrauma.

Prognosis in Moderate and Severe Traumatic Brain Injury: A Systematic Review of Contemporary Models and Validation Studies

Outcome prognostication in traumatic brain injury (TBI) is important but challenging due to heterogeneity of the disease. The aim of this systematic review is to present the current state-of-the-art on prognostic models for outcome after moderate and severe TBI and evidence on their validity. We searched for studies reporting on the development, validation or extension of prognostic models for functional outcome after TBI with Glasgow Coma Scale (GCS) ≤12 published between 2006-2018. Studies with patients age ≥14 years and evaluating a multi-variable prognostic model based on admission characteristics were included. Model discrimination was expressed with the area under the receiver operating characteristic curve (AUC), and model calibration with calibration slope and intercept. We included 58 studies describing 67 different prognostic models, comprising the development of 42 models, 149 external validations of 31 models, and 12 model extensions. The most common predictors were GCS (motor) score (n = 55), age (n = 54), and pupillary reactivity (n = 48). Model discrimination varied substantially between studies. The International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) models were developed on the largest cohorts (8509 and 10,008 patients, respectively) and were most often externally validated (n = 91), yielding AUCs ranging between 0.65-0.90 and 0.66-1.00, respectively. Model calibration was reported with a calibration intercept and slope for seven models in 53 validations, and was highly variable. In conclusion, the discriminatory validity of the IMPACT and CRASH prognostic models is supported across a range of settings. The variation in calibration, reflecting heterogeneity in reliability of predictions, motivates continuous validation and updating if clinical implementation is pursued.

Effect of the Hybrid Emergency Room System on Functional Outcome in Patients with Severe Traumatic Brain Injury

OBJECTIVE

The timely treatment of severe traumatic brain injury (TBI) is essential for limiting the effects of damage; however, there is no consensus regarding an effective method for early intervention. In August 2011, our hospital launched a novel trauma workflow using the hybrid emergency room (ER), consisting of an interventional radiology-computed tomography (CT) unit installed in the trauma resuscitation room to facilitate early interventions. The aim of this study was to evaluate effects of the hybrid ER system on functional outcomes in patients with severe TBI.

METHODS

We conducted a retrospective historical control study of patients with severe TBI (Glasgow Coma Scale score ≤8) who received conventional treatment (August 2007-July 2011) or treatment in the hybrid ER (August 2011-July 2015). The primary end point was unfavorable outcome at 6 months after injury (death, vegetative state, or lower severe disability) as evaluated by the Glasgow Outcome Scale-Extended. Secondary end points included time from arrival to the start of CT examination and emergency intracranial operation. Potential confounders were adjusted with multivariable logistic regressions.

RESULTS

Among 158 included patients, 88 were in the conventional group and 70 were in the hybrid ER group. After model adjustment, the hybrid ER group was significantly associated with a reduction in unfavorable outcomes. Times to CT examination and intracranial operation were significantly shorter in the hybrid ER group than that in the conventional group.

CONCLUSIONS

The hybrid ER system is useful for realizing immediate CT examination and emergency surgery and improving functional outcomes in patients with severe TBI.

The Application of the CRASH-CT Prognostic Model for Older Adults With Traumatic Brain Injury: A Population-Based Observational Cohort Study

OBJECTIVE

To examine the performance of the Corticosteroid Randomization After Significant Head injury (CRASH) trial prognostic model in older patients with traumatic brain injury.

SETTING

The National Study on Costs and Outcomes of Trauma cohort, established at 69 hospitals in the United States in 2001 and 2002.

PARTICIPANTS

Adults with traumatic brain injury and an initial Glasgow Coma Scale score of 14 or less.

DESIGN

The CRASH-CT model predicting death within 14 days was deployed in all patients. Model performance in older patients (aged 65-84 years) was compared with that in younger patients (aged 18-64 years).

MAIN MEASURES

Model discrimination (as defined by the c-statistic) and calibration (as defined by the Hosmer-Lemeshow P value).

RESULTS

CRASH-CT model discrimination was not significantly different between the older (n = 356; weighted n = 524) and younger patients (n = 981; weighted n = 2602) and was generally adequate (c-statistic 0.83 vs 0.87, respectively; P = .11). CRASH-CT model calibration was adequate for the older patients and inadequate for younger patients (Hosmer-Lemeshow P values .12 and .001, respectively), possibly reflecting differences in sample size. Calibration-in-the-large showed no systematic under- or overprediction in either stratum.

CONCLUSION

The CRASH-CT model may be valid for use in a geriatric population.

Predictor of Isolated Trauma in Head: A New Simple Predictor for Survival of Isolated Traumatic Brain Injury

BACKGROUND

Mortality prediction in patients with brain trauma during initial management in the emergency department (ED) is essential for creating the foundation for a better prognosis.

OBJECTIVE

This study aimed to create a simple and useful survival predictive model for patients with isolated blunt traumatic brain injury that is easily available in the ED.

METHODS

This is a retrospective study based on the trauma registry data of an academic teaching hospital. The inclusion criteria were age ≥ 15 years, blunt and not penetrating mechanism of injury, and Abbreviated Injury Scale (AIS) scores between 1 and 6 for head and 0 for all other body parts. The primary outcome was 30-day survival probability. Internal and external validation was performed.

RESULTS

After univariate logistic regression analysis based on the derivation cohort, the final Predictor of Isolated Trauma in Head (PITH) model for survival prediction of isolated traumatic brain injury included Glasgow Coma Scale (GCS), age, and coded AIS of the head. In the validation cohort, the area under the curve of the PITH score was 0.970 (p < 0.0001; 95% confidence interval 0.960-0.978). Sensitivity and specificity were 95% and 81.7% at the cutoff value of 0.9 (probability of survival 90%), respectively.

CONCLUSIONS

The PITH model performed better than the GCS; Revised Trauma Score; and mechanism of injury, GCS, age, and arterial pressure. It will be a useful triage method for isolated traumatic brain injury in the early phase of management.

Management of severe traumatic brain injury (first 24hours)

The latest French Guidelines for the management in the first 24hours of patients with severe traumatic brain injury (TBI) were published in 1998. Due to recent changes (intracerebral monitoring, cerebral perfusion pressure management, treatment of raised intracranial pressure), an update was required. Our objective has been to specify the significant developments since 1998. These guidelines were conducted by a group of experts for the French Society of Anesthesia and Intensive Care Medicine (Société francaise d'anesthésie et de réanimation [SFAR]) in partnership with the Association de neuro-anesthésie-réanimation de langue française (ANARLF), The French Society of Emergency Medicine (Société française de médecine d'urgence (SFMU), the Société française de neurochirurgie (SFN), the Groupe francophone de réanimation et d'urgences pédiatriques (GFRUP) and the Association des anesthésistes-réanimateurs pédiatriques d'expression française (ADARPEF). The method used to elaborate these guidelines was the Grade^® method. After two Delphi rounds, 32 recommendations were formally developed by the experts focusing on the evaluation the initial severity of traumatic brain injury, the modalities of prehospital management, imaging strategies, indications for neurosurgical interventions, sedation and analgesia, indications and modalities of cerebral monitoring, medical management of raised intracranial pressure, management of multiple trauma with severe traumatic brain injury, detection and prevention of post-traumatic epilepsia, biological homeostasis (osmolarity, glycaemia, adrenal axis) and paediatric specificities.

A systematic review of cerebral microdialysis and outcomes in TBI: relationships to patient functional outcome, neurophysiologic measures, and tissue outcome

OBJECTIVE

To perform a systematic review on commonly measured cerebral microdialysis (CMD) analytes and their association to: (A) patient functional outcome, (B) neurophysiologic measures, and (C) tissue outcome; after moderate/severe TBI. The aim was to provide a foundation for next-generation CMD studies and build on existing pragmatic expert guidelines for CMD.

METHODS

We searched MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library (inception to October 2016). Strength of evidence was adjudicated using GRADE.

RESULTS

(A) Functional Outcome: 55 articles were included, assessing outcome as mortality or Glasgow Outcome Scale (GOS) at 3-6 months post-injury. Overall, there is GRADE C evidence to support an association between CMD glucose, glutamate, glycerol, lactate, and LPR to patient outcome at 3-6 months. (B) Neurophysiologic Measures: 59 articles were included. Overall, there currently exists GRADE C level of evidence supporting an association between elevated CMD measured mean LPR, glutamate and glycerol with elevated ICP and/or decreased CPP. In addition, there currently exists GRADE C evidence to support an association between elevated mean lactate:pyruvate ratio (LPR) and low PbtO2. Remaining CMD measures and physiologic outcomes displayed GRADE D or no evidence to support a relationship. (C) Tissue Outcome: four studies were included. Given the conflicting literature, the only conclusion that can be drawn is acute/subacute phase elevation of CMD measured LPR is associated with frontal lobe atrophy at 6 months.

CONCLUSIONS

This systematic review replicates previously documented relationships between CMD and various outcome, which have driven clinical application of the technique. Evidence assessments do not address the application of CMD for exploring pathophysiology or titrating therapy in individual patients, and do not account for the modulatory effect of therapy on outcome, triggered at different CMD thresholds in individual centers. Our findings support clinical application of CMD and refinement of existing guidelines.

Beta-blockers and Traumatic Brain Injury: A Systematic Review, Meta-analysis, and Eastern Association for the Surgery of Trauma Guideline

OBJECTIVE

To determine if beta-(β)-blockers improve outcomes after acute traumatic brain injury (TBI).

BACKGROUND

There have been no new inpatient pharmacologic therapies to improve TBI outcomes in a half-century. Treatment of TBI patients with β-blockers offers a potentially beneficial approach.

METHODS

Using MEDLINE, EMBASE, and CENTRAL databases, eligible articles for our systematic review and meta-analysis (PROSPERO CRD42016048547) included adult (age ≥ 16 years) blunt trauma patients admitted with TBI. The exposure of interest was β-blocker administration initiated during the hospitalization. Outcomes were mortality, functional measures, quality of life, cardiopulmonary morbidity (e.g., hypotension, bradycardia, bronchospasm, and/or congestive heart failure). Data were analyzed using a random-effects model, and represented by pooled odds ratio (OR) with 95% confidence intervals (CI) and statistical heterogeneity (I).

RESULTS

Data were extracted from 9 included studies encompassing 2005 unique TBI patients with β-blocker treatment and 6240 unique controls. Exposure to β-blockers after TBI was associated with a reduction of in-hospital mortality (pooled OR 0.39, 95% CI: 0.27-0.56; I = 65%, P < 0.00001). None of the included studies examined functional outcome or quality of life measures, and cardiopulmonary adverse events were rarely reported. No clear evidence of reporting bias was identified.

CONCLUSIONS

In adults with acute TBI, observational studies reveal a significant mortality advantage with β-blockers; however, quality of evidence is very low. We conditionally recommend the use of in-hospital β-blockers. However, we recommend further high-quality trials to answer questions about the mechanisms of action, effectiveness on subgroups, dose-response, length of therapy, functional outcome, and quality of life after β-blocker use for TBI.

The aggressiveness of neurotrauma practitioners and the influence of the IMPACT prognostic calculator

Published guidelines have helped to standardize the care of patients with traumatic brain injury; however, there remains substantial variation in the decision to pursue or withhold aggressive care. The International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic calculator offers the opportunity to study and decrease variability in physician aggressiveness. The authors wish to understand how IMPACT’s prognostic calculations currently influence patient care and to better understand physician aggressiveness. The authors conducted an anonymous international, multidisciplinary survey of practitioners who provide care to patients with traumatic brain injury. Questions were designed to determine current use rates of the IMPACT prognostic calculator and thresholds of age and risk for death or poor outcome that might cause practitioners to consider withholding aggressive care. Correlations between physician aggressiveness, putative predictors of aggressiveness, and demographics were examined. One hundred fifty-four responses were received, half of which were from physicians who were familiar with the IMPACT calculator. The most frequent use of the calculator was to improve communication with patients and their families. On average, respondents indicated that in patients older than 76 years or those with a >85% chance of death or poor outcome it might be reasonable to pursue non-aggressive care. These thresholds were robust and were not influenced by provider or institutional characteristics. This study demonstrates the need to educate physicians about the IMPACT prognostic calculator. The consensus values for age and prognosis identified in our study may be explored in future studies aimed at reducing variability in physician aggressiveness and should not serve as a basis for withdrawing care.

[Magnetic resonance in traumatic brain injury: A comparative study of the different conventional magnetic resonance imaging sequences and their diagnostic value in diffuse axonal injury]

OBJECTIVE

To compare the identification capability of traumatic axonal injury (TAI) by different sequences on conventional magnetic resonance (MR) studies in traumatic brain injury (TBI) patients.

MATERIAL AND METHODS

We retropectevely analyzed 264 TBI patients to whom a MR had been performed in the first 60 days after trauma. All clinical variables related to prognosis were registered, as well as the data from the initial computed tomography. The MR imaging protocol consisted of a 3-plane localizer sequence T1-weighted and T2-weighted fast spin-echo, FLAIR and gradient-echo images (GRET2*). TAI lesions were classified according to Gentry and Firsching classifications. We calculated weighted kappa coefficients and the area under the ROC curve for each MR sequence. A multivariable analyses was performed to correlate MR findings in each sequence with the final outcome of the patients.

RESULTS

TAI lesions were adequately visualized on T2, FLAIR and GRET2* sequences in more than 80% of the studies. Subcortical TAI lesions were well on FLAIR and GRET2* sequences visualized hemorrhagic TAI lesions. We saw that these MR sequences had a high inter-rater agreement for TAI diagnosis (0.8). T2 sequence presented the highest value on ROC curve in Gentry (0.68, 95%CI: 0.61-0.76, p<0.001, Nagerlkerke-R² 0.26) and Firsching classifications (0.64, 95%CI 0.57-0.72, p<0.001, Nagerlkerke-R² 0.19), followed by FLAIR and GRET2* sequences. Both classifications determined by each of these sequences were associated with poor outcome after performing a multivariable analyses adjusted for prognostic factors (p<0.02).

CONCLUSIONS

We recommend to perform conventional MR study in subacute phase including T2, FLAIR and GRET2* sequences for visualize TAI lesions. These MR findings added prognostic information in TBI patients.

Missing in action: Species competition is a neglected predictor variable in species distribution modelling

The central role of species competition in shaping community structure in ecosystems is well appreciated amongst ecologists. However species competition is a consistently missing variable in Species Distribution Modelling (SDM). This study presents results of our attempt to incorporate species competition in SDMs. We used a suit of predictor variables including Soil Adjusted Vegetation Index (SAVI), as well as distance from roads, settlements and water, fire frequency and distance from the nearest herbivore sighting (of selected herbivores) to model individual habitat preferences of five grazer species (buffalo, warthog, waterbuck, wildebeest and zebra) with the Ensemble SDM algorithm for Gonarezhou National Park, Zimbabwe. Our results showed that distance from the nearest animal sighting (a proxy for competition among grazers) was the best predictor of the potential distribution of buffalo, wildebeest and zebra but the second best predictor for warthog and waterbuck. Our findings provide evidence to that competition is an important predictor of grazer species' potential distribution. These findings suggest that species distribution modelling that neglects species competition may be inadequate in explaining the potential distribution of species. Therefore our findings encourage the inclusion of competition in SDM as well as potentially igniting discussions that may lead to improving the predictive power of future SDM efforts.

Volumetric analysis of day of injury computed tomography is associated with rehabilitation outcomes after traumatic brain injury

BACKGROUND

Day-of-injury (DOI) brain lesion volumes in traumatic brain injury (TBI) patients are rarely used to predict long-term outcomes in the acute setting. The purpose of this study was to investigate the relationship between acute brain injury lesion volume and rehabilitation outcomes in patients with TBI at a level one trauma center.

METHODS

Patients with TBI who were admitted to our rehabilitation unit after the acute care trauma service from February 2009-July 2011 were eligible for the study. Demographic data and outcome variables including cognitive and motor Functional Independence Measure (FIM) scores, length of stay (LOS) in the rehabilitation unit, and ability to return to home were obtained. The DOI quantitative injury lesion volumes and degree of midline shift were obtained from DOI brain computed tomography scans. A multiple stepwise regression model including 13 independent variables was created. This model was used to predict postrehabilitation outcomes, including FIM scores and ability to return to home. A p value less than 0.05 was considered significant.

RESULTS

Ninety-six patients were enrolled in the study. Mean age was 43 ± 21 years, admission Glasgow Coma Score was 8.4 ± 4.8, Injury Severity Score was 24.7 ± 9.9, and head Abbreviated Injury Scale score was 3.73 ± 0.97. Acute hospital LOS was 12.3 ± 8.9 days, and rehabilitation LOS was 15.9 ± 9.3 days. Day-of-injury TBI lesion volumes were inversely associated with cognitive FIM scores at rehabilitation admission (p = 0.004) and discharge (p = 0.004) and inversely associated with ability to be discharged to home after rehabilitation (p = 0.006).

CONCLUSION

In a cohort of patients with moderate to severe TBI requiring a rehabilitation unit stay after the acute care hospital stay, DOI brain injury lesion volumes are associated with worse cognitive FIM scores at the time of rehabilitation admission and discharge. Smaller-injury volumes were associated with eventual discharge to home. Volumetric neuroimaging in the acute injury phase may improve surgeons' ultimate outcome predictions in TBI patients.

LEVEL OF EVIDENCE

Prognostic/epidemiologic study, level V.

Mortality Risk Stratification After Traumatic Brain Injury and Hazard of Death With Titrated Hypothermia in the Eurotherm3235Trial

Supplemental Digital Content is available in the text.

Objectives:

Hypothermia reduces intracranial hypertension in patients with traumatic brain injury but was associated with harm in the Eurotherm3235Trial. We stratified trial patients by International Mission for Prognosis and Analysis of Clinical Trials in [Traumatic Brain Injury] (IMPACT) extended model sum scores to determine where the balance of risks lay with the intervention.

Design:

The Eurotherm3235Trial was a randomized controlled trial, with standardized and blinded outcome assessment. Patients in the trial were split into risk tertiles by IMPACT extended model sum scores. A proportional hazard analysis for death between randomization and 6 months was performed by intervention and IMPACT extended model sum scores tertiles in both the intention-to-treat and the per-protocol populations of the Eurotherm3235Trial.

Setting:

Forty-seven neurologic critical care units in 18 countries.

Patients:

Adult traumatic brain injury patients admitted to intensive care who had suffered a primary, closed traumatic brain injury; increased intracranial pressure; an initial head injury less than 10 days earlier; a core temperature at least 36°C; and an abnormal brain CT.

Intervention:

Titrated Hypothermia in the range 32-35°C as the primary intervention to reduce raised intracranial pressure.

Measurements and Main Results:

Three hundred eighty-six patients were available for analysis in the intention-to-treat and 257 in the per-protocol population. The proportional hazard analysis (intention-to-treat and per-protocol populations) showed that the treatment effect behaves similarly across all risk stratums. However, there is a trend that indicates that patients in the low-risk group could be at greater risk of suffering harm due to hypothermia.

Conclusions:

Hypothermia as a first line measure to reduce intracranial pressure to less than 20 mm Hg is harmful in patients with a lower severity of injury and no clear benefit exists in patients with more severe injuries.

Seizures and the Role of Anticonvulsants After Traumatic Brain Injury

Posttraumatic seizures are a common complication of traumatic brain injury. Posttraumatic epilepsy accounts for 20% of symptomatic epilepsy in the general population and 5% of all epilepsy. Early posttraumatic seizures occur in more than 20% of patients in the intensive care unit and are associated with secondary brain injury and worse patient outcomes. Most posttraumatic seizures are nonconvulsive and therefore continuous electroencephalography monitoring should be the standard of care for patients with moderate or severe brain injury. The literature shows that posttraumatic seizures result in secondary brain injury caused by increased intracranial pressure, cerebral edema and metabolic crisis.

Prognostic value of corpus callosum injuries in severe head trauma

BACKGROUND

This study was performed to investigate the relationship between corpus callosum (CC) injury and prognosis in traumatic axonal injury (TAI).

METHOD

We retrospectively reviewed 264 patients with severe head trauma who underwent a conventional MR imaging in the first 60 days after injury. They were selected from a prospectively collected database of 1048 patients with severe head trauma admitted in our hospital. TAI lesions were defined as areas of increased signal intensity on T2 and FLAIR or areas of decreased signal on gradient-echo T2. We attempted to determine whether any MR imaging findings of TAI lesions at CC could be related to prognosis. Neurological impairment was assessed at 1 year after injury by means of GOS-E (good outcome being GOS-E 4/5 and bad outcome being GOS-E <4). We adjusted the multivariable analysis for the prognostic factors according to the IMPACT studies: the Core model (age, motor score at admission, and pupillary reactivity) and the Extended model (including CT information and second insults).

RESULTS

We found 97 patients (37 %) with TAI at CC and 167 patients (63 %) without CC lesions at MR. A total of 62 % of the patients with CC lesions had poor outcome, whereas 38 % showed good prognosis. The presence of TAI lesions at the corpus callosum was associated with poor outcome 1 year after brain trauma (p < 0.001, OR 3.8, 95 % CI: 2.04-7.06). The volume of CC lesions measured on T2 and FLAIR sequences was negatively correlated with the GOS-E after adjustment for independent prognostic factors (p = 0.01, OR 2.23, 95 % CI:1.17-4.26). Also the presence of lesions at splenium was statistically related to worse prognosis (p = 0.002, OR 8.1, 95 % CI: 2.2-29.82). We did not find statistical significance in outcome between hemorrhagic and non-hemorrhagic CC lesions.

CONCLUSIONS

The presence of CC is associated with a poor outcome. The total volume of the CC lesion is an independent prognostic factor for poor outcome in severe head trauma.

Predicting long-term neurological outcomes after severe traumatic brain injury requiring decompressive craniectomy: A comparison of the CRASH and IMPACT prognostic models

BACKGROUND

Predicting long-term neurological outcomes after severe traumatic brain (TBI) is important, but which prognostic model in the context of decompressive craniectomy has the best performance remains uncertain.

METHODS

This prospective observational cohort study included all patients who had severe TBI requiring decompressive craniectomy between 2004 and 2014, in the two neurosurgical centres in Perth, Western Australia. Severe disability, vegetative state, or death were defined as unfavourable neurological outcomes. Area under the receiver-operating-characteristic curve (AUROC) and slope and intercept of the calibration curve were used to assess discrimination and calibration of the CRASH (Corticosteroid-Randomisation-After-Significant-Head injury) and IMPACT (International-Mission-For-Prognosis-And-Clinical-Trial) models, respectively.

RESULTS

Of the 319 patients included in the study, 119 (37%) had unfavourable neurological outcomes at 18-month after decompressive craniectomy for severe TBI. Both CRASH (AUROC 0.86, 95% confidence interval 0.81-0.90) and IMPACT full-model (AUROC 0.85, 95% CI 0.80-0.89) were similar in discriminating between favourable and unfavourable neurological outcome at 18-month after surgery (p=0.690 for the difference in AUROC derived from the two models). Although both models tended to over-predict the risks of long-term unfavourable outcome, the IMPACT model had a slightly better calibration than the CRASH model (intercept of the calibration curve=-4.1 vs. -5.7, and log likelihoods -159 vs. -360, respectively), especially when the predicted risks of unfavourable outcome were <80%.

CONCLUSIONS

Both CRASH and IMPACT prognostic models were good in discriminating between favourable and unfavourable long-term neurological outcome for patients with severe TBI requiring decompressive craniectomy, but the calibration of the IMPACT full-model was better than the CRASH model.

Past, Present, and Future of Traumatic Brain Injury Research

Traumatic brain injury (TBI) is the greatest cause of death and severe disability in young adults; its incidence is increasing in the elderly and in the developing world. Outcome from severe TBI has improved dramatically as a result of advancements in trauma systems and supportive critical care, however we remain without a therapeutic which acts directly to attenuate brain injury. Recognition of secondary injury and its molecular mediators has raised hopes for such targeted treatments. Unfortunately, over 30 late-phase clinical trials investigating promising agents have failed to translate a therapeutic for clinical use. Numerous explanations for this failure have been postulated and are reviewed here. With this historical context we review ongoing research and anticipated future trends which are armed with lessons from past trials, new scientific advances, as well as improved research infrastructure and funding. There is great hope that these new efforts will finally lead to an effective therapeutic for TBI as well as better clinical management strategies.

Clinical Results and Outcome Improvement Over Time in Traumatic Brain Injury

Prognostic models for traumatic brain injury (TBI) are important tools both in clinical practice and research if properly validated, preferably by external validation. Prognostic models also offer the possibility of monitoring performance by comparing predicted outcomes with observed outcomes. In this study, we applied the prognostic models developed by the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) in an Italian multi-center database (Neurolink) with two aims: to compare observed with predicted outcomes and to check for a possible improvement of clinical outcome over the 11 years of patient inclusion in Neurolink. We applied the IMPACT models to patients included in Neurolink between 1997 and 2007. Performance of the models was assessed by determining calibration (with calibration plots) and discrimination (by the area under the receiver operating characteristic curve [AUC]). Logistic regression analysis was used to analyze a possible trend in outcomes over time, adjusted for predicted outcomes. A total of 1401 patients were studied. Patients had a median age of 40 years and 51% had a Glasgow Coma Scale motor score of 5 or 6. The models showed good discrimination, with AUCs of 0.86 (according to the Core Model) and 0.88 (Extended Model), and adequate calibration, with the overall observed risk of unfavorable outcome and mortality being less than predicted. Outcomes significantly improved over time. This study shows that the IMPACT models performed reasonably well in the Neurolink data and can be used for monitoring performance. After adjustment for predicted outcomes with the prognostic models, we observed a substantial improvement of patient outcomes over time in the three Neurolink centers.

External Validation of the International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury: Prognostic Models for Traumatic Brain Injury on the Study of the Neuroprotective Activity of Progesterone in Severe Traumatic Brain Injuries Trial

Prediction models for patients with traumatic brain injury (TBI) are important for multiple reasons, including case-mix adjustment, trial design, and benchmarking for quality-of-care evaluation. Models should be generalizable and therefore require regular external validation. We aimed to validate the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic models for moderate and severe TBI in a recent randomized controlled trial. We studied 1124 patients enrolled in the multi-center randomized placebo-controlled Study of the Neuroprotective Activity of Progesterone in Severe Traumatic Brain Injuries (SyNAPSe) trial that evaluated the efficacy of progesterone in TBI. Treatment and placebo groups were combined for analysis. We evaluated the predictive performance of the three prognostic models (core, extended, and lab) from the IMPACT study with regard to discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (comparison of observed to predicted risks). Substantial differences were found in case-mix and outcome distribution between IMPACT and SyNAPSe. In line with the more homogeneous case-mix of a clinical trial, the discriminative performance was reasonable. For the core model, an AUC of 0.677 and 0.684 was obtained for 6-month mortality and unfavorable outcome, respectively. Performance was slightly better for the extended model (0.693 and 0.705) and for the lab model (0.689 and 0.711, respectively). For calibration, we found overestimation of mortality, especially at higher risk predictions, and underestimation of unfavorable outcome, especially at lower risk predictions. This pattern of miscalibration was consistent across all three models. In a contemporary trial setting, the IMPACT models have reasonable discrimination if enrollment restrictions apply. Observed changes in outcome distribution necessitate updating of previously developed prognostic models.

Predicting the outcome for individual patients with traumatic brain injury: a case-based review

BACKGROUND

Traumatic brain injuries result in significant morbidity and mortality. Accurate prediction of prognosis is desirable to inform treatment decisions and counsel family members. Objective To review the currently available prognostic tools for use in traumatic brain injury (TBI), to analyse their value in individual patient management and to appraise ongoing research on prognostic modelling.

METHODS AND RESULTS

We present two patients who sustained a TBI in 2011-2012 and evaluate whether prognostic models could accurately predict their outcome. The methodology and validity of current prognostic models are analysed and current research that might contribute to improved individual patient prognostication is evaluated.

CONCLUSION

Predicting prognosis in the acute phase after TBI is complex and existing prognostic models are not suitable for use at the individual patient level. Data derived from these models should only be used as an adjunct to clinical judgement and should not be used to set limits for acute care interventions. Information from neuroimaging, physiological monitoring and analysis of biomarkers or genetic polymorphisms may be used in the future to improve accuracy of individual patient prognostication. Clinicians should consider offering full supportive treatment to patients in the early phase after injury whilst the outcome is unclear.

Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score)

BACKGROUND

Early computerized tomography (CT) abnormalities are important predictors of outcome after traumatic brain injury (TBI).

OBJECTIVE

To develop a novel CT scoring system (Helsinki CT score) and to compare it with the Marshall CT classification and the Rotterdam CT score in predicting long-term outcome of patients with TBI.

METHODS

Eight hundred sixty-nine consecutive TBI patients were included in this open-cohort, retrospective, single-center study. Logistic regression was used to develop the Helsinki CT score. The scores from the Marshall, Rotterdam, and Helsinki CT scoring methods were added to a clinical model based on age, motor score, and pupils to evaluate their value in predicting outcome. Internal validity was assessed by a bootstrap technique and expressed as area under the curve (AUC). Outcome was 6-month unfavorable neurological outcome and mortality.

RESULTS

Variables included in the Helsinki CT score were bleeding type and size, intraventricular hemorrhage, and suprasellar cisterns. In the present data set, the performance of the Helsinki CT score was superior to that of the Marshall CT and Rotterdam CT scores (AUC, 0.74-0.75 vs 0.63-0.70; P < .001). Addition of the Helsinki CT score modestly increased prognostic performance of the clinical model (AUC neurological outcome +0.02 [P = .002]; AUC mortality, +0.01 [P = .21]). In contrast, the Marshall and Rotterdam CT scores were of no additional predictive value to the clinical model (P > .05).

CONCLUSION

Use of the novel Helsinki CT score improved outcome prediction accuracy, and the Helsinki CT score is a feasible alternative to the Rotterdam and Marshall CT systems. External validation of the Helsinki CT score is advocated to show generalizability.

External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom

This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Scale score of less than 15 were recruited. The primary outcomes were mortality and unfavorable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3626 critical care unit admissions, 2975 were analyzed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavorable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven and colleagues, the Medical Research Council (MRC) Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) group. The model with the best discrimination was the IMPACT "Lab" model (C index, 0.779 for mortality and 0.713 for unfavorable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavorable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavorable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research.