Time to Follow Commands in Severe Traumatic Brain Injury Survivors With Favorable Recovery at 2 Years

Performance of the IMPACT and CRASH prognostic models for traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study

OBJECTIVE

The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) prognostic models for mortality and outcome after traumatic brain injury (TBI) were developed using data from 1984 to 2004. This study examined IMPACT and CRASH model performances in a contemporary cohort of US patients.

METHODS

The prospective 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years 2014-2018) enrolled subjects aged ≥ 17 years who presented to level I trauma centers and received head CT within 24 hours of TBI. Data were extracted from the subjects who met the model criteria (for IMPACT, Glasgow Coma Scale [GCS] score 3-12 with 6-month Glasgow Outcome Scale-Extended [GOSE] data [n = 441]; for CRASH, GCS score 3-14 with 2-week mortality data and 6-month GOSE data [n = 831]). Analyses were conducted in the overall cohort and stratified on the basis of TBI severity (severe/moderate/mild TBI defined as GCS score 3-8/9-12/13-14), age (17-64 years or ≥ 65 years), and the 5 top enrolling sites. Unfavorable outcome was defined as GOSE score 1-4. Original IMPACT and CRASH model coefficients were applied, and model performances were assessed by calibration (intercept [< 0 indicated overprediction; > 0 indicated underprediction] and slope) and discrimination (c-statistic).

RESULTS

Overall, the IMPACT models overpredicted mortality (intercept -0.79 [95% CI -1.05 to -0.53], slope 1.37 [1.05-1.69]) and acceptably predicted unfavorable outcome (intercept 0.07 [-0.14 to 0.29], slope 1.19 [0.96-1.42]), with good discrimination (c-statistics 0.84 and 0.83, respectively). The CRASH models overpredicted mortality (intercept -1.06 [-1.36 to -0.75], slope 0.96 [0.79-1.14]) and unfavorable outcome (intercept -0.60 [-0.78 to -0.41], slope 1.20 [1.03-1.37]), with good discrimination (c-statistics 0.92 and 0.88, respectively). IMPACT overpredicted mortality and acceptably predicted unfavorable outcome in the severe and moderate TBI subgroups, with good discrimination (c-statistic ≥ 0.81). CRASH overpredicted mortality in the severe and moderate TBI subgroups and acceptably predicted mortality in the mild TBI subgroup, with good discrimination (c-statistic ≥ 0.86); unfavorable outcome was overpredicted in the severe and mild TBI subgroups with adequate discrimination (c-statistic ≥ 0.78), whereas calibration was nonlinear in the moderate TBI subgroup. In subjects ≥ 65 years of age, the models performed variably (IMPACT-mortality, intercept 0.28, slope 0.68, and c-statistic 0.68; CRASH-unfavorable outcome, intercept -0.97, slope 1.32, and c-statistic 0.88; nonlinear calibration for IMPACT-unfavorable outcome and CRASH-mortality). Model performance differences were observed across the top enrolling sites for mortality and unfavorable outcome.

CONCLUSIONS

The IMPACT and CRASH models adequately discriminated mortality and unfavorable outcome. Observed overestimations of mortality and unfavorable outcome underscore the need to update prognostic models to incorporate contemporary changes in TBI management and case-mix. Investigations to elucidate the relationships between increased survival, outcome, treatment intensity, and site-specific practices will be relevant to improve models in specific TBI subpopulations (e.g., older adults), which may benefit from the inclusion of blood-based biomarkers, neuroimaging features, and treatment data.

Influence of health insurance on withdrawal of life sustaining treatment for patients with isolated traumatic brain injury: a retrospective multi-center observational cohort study

BACKGROUND

Healthcare inequities for patients with traumatic brain injury (TBI) represent a major priority area for trauma quality improvement. We hypothesized a relationship between health insurance status and timing of withdrawal of life sustaining treatment (WLST) for adults with severe TBI.

METHODS

This multicenter retrospective observational cohort study utilized data collected between 2017 and 2020. We identified adult (age ≥ 16) patients with isolated severe TBI admitted participating Trauma Quality Improvement Program centers. We determined the relationship between insurance status (public, private, and uninsured) and the timing of WLST using a competing risk survival analysis framework adjusting for baseline, clinical, injury and trauma center characteristics. Multivariable cause-specific Cox regressions were used to compute adjusted hazard ratios (HR) reflecting timing of WLST, accounting for mortality events. We also quantified the between-center residual variability in WLST using the median odds ratio (MOR) and measured insurance status association with access to rehabilitation at discharge.

RESULTS

We identified 42,111 adults with isolated severe TBI treated across 509 trauma centers across North America. There were 10,771 (25.6%) WLST events in the cohort and a higher unadjusted incidence of WLST events was evident in public insurance patients compared to private or uninsured groups. After adjustment, WLST occurred earlier for publicly insured (HR 1.07, 95% CI 1.02-1.12) and uninsured patients (HR 1.29, 95% CI 1.18-1.41) compared to privately insured patients. Access to rehabilitation was lower for both publicly insured and uninsured patients compared to patients with private insurance. Accounting for case-mix, the MOR was 1.49 (95% CI 1.43-1.55), reflecting significant residual between-center variation in WLST decision-making.

CONCLUSIONS

Our findings highlight the presence of disparate WLST practices independently associated with health insurance status. Additionally, these results emphasize between-center variability in WLST, persisting despite adjustments for measurable patient and trauma center characteristics.

Predicting Functional Dependency in Patients with Disorders of Consciousness: A TBI-Model Systems and TRACK-TBI Study

OBJECTIVE

It is not currently possible to predict long-term functional dependency in patients with disorders of consciousness (DoC) after traumatic brain injury (TBI). Our objective was to fit and externally validate a prediction model for 1-year dependency in patients with DoC ≥ 2 weeks after TBI.

METHODS

We included adults with TBI enrolled in TBI Model Systems (TBI-MS) or Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) studies who were not following commands at rehabilitation admission or 2 weeks post-injury, respectively. We fit a logistic regression model in TBI-MS and validated it in TRACK-TBI. The primary outcome was death or dependency at 1 year post-injury, defined using the Disability Rating Scale.

RESULTS

In the TBI-MS Discovery Sample, 1,960 participants (mean age 40 [18] years, 76% male, 68% white) met inclusion criteria, and 406 (27%) were dependent 1 year post-injury. In a TBI-MS held out cohort, the dependency prediction model's area under the receiver operating characteristic curve was 0.79 (95% CI 0.74-0.85), positive predictive value was 53% and negative predictive value was 86%. In the TRACK-TBI external validation (n = 124, age 40 [16] years, 77% male, 81% white), the area under the receiver operating characteristic curve was 0.66 (0.53, 0.79), equivalent to the standard IMPACTcore + CT score (p = 0.8).

INTERPRETATION

We developed a 1-year dependency prediction model using the largest existing cohort of patients with DoC after TBI. The sensitivity and negative predictive values were greater than specificity and positive predictive values. Accuracy was diminished in an external sample, but equivalent to the IMPACT model. Further research is needed to improve dependency prediction in patients with DoC after TBI. ANN NEUROL 2023;94:1008-1023.

Early Shared Decision-Making for Older Adults with Traumatic Brain Injury: Using Time-Limited Trials and Understanding Their Limitations

Older adults account for a disproportionate share of the morbidity and mortality after traumatic brain injury (TBI). Predicting functional and cognitive outcomes for individual older adults after TBI is challenging in the acute phase of injury. Given that neurologic recovery is possible and uncertain, life-sustaining therapy may be pursued initially, even if for some, there is a risk of survival to an undesired level of disability or dependence. Experts recommend early conversations about goals of care after TBI, but evidence-based guidelines for these discussions or for the optimal method for communicating prognosis are limited. The time-limited trial (TLT) model may be an effective strategy for managing prognostic uncertainty after TBI. TLTs can provide a framework for early management: specific treatments or procedures are used for a defined period of time while monitoring for an agreed-upon outcome. Outcome measures, including signs of worsening and improvement, are defined at the outset of the trial. In this Viewpoint article, we discuss the use of TLTs for older adults with TBI, their potential benefits, and current challenges to their application. Three main barriers limit the implementation of TLTs in these scenarios: inadequate models for prognostication; cognitive biases faced by clinicians and surrogate decision-makers, which may contribute to prognostic discordance; and ambiguity regarding appropriate endpoints for the TLT. Further study is needed to understand clinician behaviors and surrogate preferences for prognostic communication and how to optimally integrate TLTs into the care of older adults with TBI.

Traumatic Brain Injury: Contemporary Challenges and the Path to Progress

Traumatic brain injury (TBI) remains a leading cause of death and disability worldwide, and its incidence is increasing [...].

Predicting Functional Dependency in Patients with Disorders of Consciousness: A TBI-Model Systems and TRACK-TBI Study

Importance

There are currently no models that predict long-term functional dependency in patients with disorders of consciousness (DoC) after traumatic brain injury (TBI).

Objective

Fit, test, and externally validate a prediction model for 1-year dependency in patients with DoC 2 or more weeks after TBI.

Design

Secondary analysis of patients enrolled in TBI Model Systems (TBI-MS, 1988-2020, Discovery Sample) or Transforming Research and Clinical Knowledge in TBI (TRACK-TBI, 2013-2018, Validation Sample) and followed 1-year post-injury.

Setting

Multi-center study at USA rehabilitation hospitals (TBI-MS) and acute care hospitals (TRACK-TBI).

Participants

Adults with TBI who were not following commands at rehabilitation admission (TBI-MS; days post-injury vary) or 2-weeks post-injury (TRACK-TBI).

Exposures

In the TBI-MS database (model fitting and testing), we screened demographic, radiological, clinical variables, and Disability Rating Scale (DRS) item scores for association with the primary outcome.

Main Outcome

The primary outcome was death or complete functional dependency at 1-year post-injury, defined using a DRS-based binary measure (DRS Depend ), indicating need for assistance with all activities and concomitant cognitive impairment.

Results

In the TBI-MS Discovery Sample, 1,960 subjects (mean age 40 [18] years, 76% male, 68% white) met inclusion criteria and 406 (27%) were dependent at 1-year post-injury. A dependency prediction model had an area under the receiver operating characteristic curve (AUROC) of 0.79 [0.74, 0.85], positive predictive value of 53%, and negative predictive value of 86% for dependency in a held-out TBI-MS Testing cohort. Within the TRACK-TBI external validation sample (N=124, age 40 [16], 77% male, 81% white), a model modified to remove variables not collected in TRACK-TBI, had an AUROC of 0.66 [0.53, 0.79], equivalent to the gold-standard IMPACT core+CT score (0.68; 95% AUROC difference CI: -0.2 to 0.2, p=0.8).

Conclusions and Relevance

We used the largest existing cohort of patients with DoC after TBI to develop, test and externally validate a prediction model of 1-year dependency. The model’s sensitivity and negative predictive value were greater than specificity and positive predictive value. Accuracy was diminished in an external sample, but equivalent to the best-available models. Further research is needed to improve dependency prediction in patients with DoC after TBI.

Prognostic Models for Traumatic Brain Injury Have Good Discrimination but Poor Overall Model Performance for Predicting Mortality and Unfavorable Outcomes

BACKGROUND

The most extensively validated prognostic models for traumatic brain injury (TBI) are the Corticoid Randomization after Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis of Clinical Trials (IMPACT). Model characteristics outside of area under the curve (AUC) are rarely reported.

OBJECTIVE

To report the discriminative validity and overall model performance of the CRASH and IMPACT models for prognosticating death at 14 days (CRASH) and 6 months (IMPACT) and unfavorable outcomes at 6 months after TBI.

METHODS

This retrospective cohort study included prospectively collected patients with severe TBI treated at a single level I trauma center (n = 467). CRASH and IMPACT percent risk values for the given outcome were computed. Unfavorable outcome was defined as a Glasgow Outcome Scale-Extended score of 1 to 4 at 6 months. Binary logistic regressions and receiver operating characteristic analyses were used to differentiate patients from the CRASH and IMPACT prognostic models.

RESULTS

All models had low R 2 values (0.17-0.23) with AUC values from 0.77 to 0.81 and overall accuracies ranging from 72.4% to 78.3%. Sensitivity (35.3-50.0) and positive predictive values (66.7-69.2) were poor in the CRASH models, while specificity (52.3-53.1) and negative predictive values (58.1-63.6) were poor in IMPACT models. All models had unacceptable false positive rates (20.8%-33.3%).

CONCLUSION

Our results were consistent with previous literature regarding discriminative validity (AUC = 0.77-0.81). However, accuracy and false positive rates of both the CRASH and IMPACT models were poor.

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.