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

A Model Predicting the 6-Month Disability of Patients With Traumatic Brain Injury to Assess the Quality of Care in Intensive Care Units: Results from the CREACTIVE Study

Assessing quality of care is essential for improving the management of patients experiencing traumatic brain injury (TBI). This study aimed at devising a rigorous framework to evaluate the quality of TBI care provided by intensive care units (ICUs) and applying it to the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) consortium, which involved 83 ICUs from seven countries. The performance of the centers was assessed in terms of patients' outcomes, as measured by the 6-month Glasgow Outcome Scale-Extended (GOS-E). To account for the between-center differences in the characteristics of the admitted patients, we developed a multinomial logistic regression model estimating the probability of a four-level categorization of the GOS-E: good recovery (GR), moderate disability (MD), severe disability (SD), and death or vegetative state (D/VS). A total of 5928 patients admitted to the participating ICUs between March 2014 and March 2019 were analyzed. The model included 11 predictors and demonstrated good discrimination (area under the receiver operating characteristic [ROC] curve in the validation set for GR: 0.836, MD: 0.802, SD: 0.706, D/VS: 0.890) and calibration, both overall (Hosmer-Lemeshow test p value: 0.87) and in several subgroups, defined by prognostically relevant variables. The model was used as a benchmark for assessing quality of care by comparing the observed number of patients experiencing GR, MD, SD, and D/VS to the corresponding numbers expected in each category by the model, computing observed/expected (O/E) ratios. The four center-specific ratios were assembled with polar representations and used to provide a multidimensional assessment of the ICUs, overcoming the loss of information consequent to the traditional dichotomizations of the outcome in TBI research. The proposed framework can help in identifying strengths and weaknesses of current TBI care, triggering the changes that are necessary to improve patient outcomes.

Multivariate modeling and prediction of cerebral physiology in acute traumatic neural injury: A scoping review

Traumatic brain injury (TBI) poses a significant global public health challenge necessitating a profound understanding of cerebral physiology. The dynamic nature of TBI demands sophisticated methodologies for modeling and predicting cerebral signals to unravel intricate pathophysiology and predict secondary injury mechanisms prior to their occurrence. In this comprehensive scoping review, we focus specifically on multivariate cerebral physiologic signal analysis in the context of multi-modal monitoring (MMM) in TBI, exploring a range of techniques including multivariate statistical time-series models and machine learning algorithms. Conducting a comprehensive search across databases yielded 7 studies for evaluation, encompassing diverse cerebral physiologic signals and parameters from TBI patients. Among these, five studies concentrated on modeling cerebral physiologic signals using statistical time-series models, while the remaining two studies primarily delved into intracranial pressure (ICP) prediction through machine learning models. Autoregressive models were predominantly utilized in the modeling studies. In the context of prediction studies, logistic regression and Gaussian processes (GP) emerged as the predominant choice in both research endeavors, with their performance being evaluated against each other in one study and other models such as random forest, and decision tree in the other study. Notably among these models, random forest model, an ensemble learning approach, demonstrated superior performance across various metrics. Additionally, a notable gap was identified concerning the absence of studies focusing on prediction for multivariate outcomes. This review addresses existing knowledge gaps and sets the stage for future research in advancing cerebral physiologic signal analysis for neurocritical care improvement.

Assessing outcomes in traumatic brain injury: Helsinki score versus Glasgow coma scale

BACKGROUND

The precision of assessment and prognosis in traumatic brain injury (TBI) is paramount for effective triage and informed therapeutic strategies. While the Glasgow Coma Scale (GCS) remains the cornerstone for TBI evaluation, it overlooks critical primary imaging findings. The Helsinki Score (HS), a novel tool designed to incorporate radiological data, offers a promising approach to predicting TBI outcomes. This study aims to evaluate the prognostic efficacy of HS in comparison to GCS across a substantial TBI patient cohort.

METHODS

This retrospective study encompassed TBI patients treated at our institution between 2008 and 2019, specifically those with an admission GCS of 14 or lower. We assessed both the initial GCS and the HS derived from primary CT scans. Key outcome metrics included the Glasgow Outcome Scale (GOS) and mortality rates at hospital discharge and at 6 and 12-month intervals post-discharge. Predictive performances of GCS and HS were analyzed through Receiver Operating Characteristic (ROC) curves and Kendall tau-b correlation coefficients against each outcome.

RESULTS

The study included 544 patients, with an average age of 62.2 ± 21.5 years, median initial GCS of 14, and a median HS of 3. The mortality rate at discharge stood at 8.6%, with a median GOS of 4. Both GCS and HS demonstrated significant correlations with mortality and GOS outcomes (p < 0.05). Notably, HS showed a markedly superior correlation with mortality (τb = 0.36) compared to GCS (τb = -0.11) and with GOS outcomes (τb = -0.40 for HS vs. τb = 0.33 for GCS). ROC analyses affirmed HS's enhanced predictive accuracy over GCS for both mortality (AUC of 0.79 for HS vs. 0.62 for GCS) and overall outcomes (AUC of 0.77 for HS vs. 0.71 for GCS).

CONCLUSION

The findings validate the HS in a large German cohort and suggest that radiological assessments alone, as exemplified by HS, can surpass the traditional GCS in predicting TBI outcomes. However, the HS, despite its efficacy, lacks the integration of clinical evaluation, a vital component in TBI management. This underscores the necessity for a holistic approach that amalgamates both radiological and clinical insights for a more comprehensive and accurate prognostication in TBI care.

PaCO2 Association with Outcomes of Patients with Traumatic Brain Injury at High Altitude: A Prospective Single-Center Cohort Study

BACKGROUND

Partial pressure of carbon dioxide (PaCO2) is generally known to influence outcome in patients with traumatic brain injury (TBI) at normal altitudes. Less is known about specific relationships of PaCO2 levels and clinical outcomes at high altitudes.

METHODS

This is a prospective single-center cohort of consecutive patients with TBI admitted to a trauma center located at 2600 m above sea level. An unfavorable outcome was defined as a Glasgow Outcome Scale-Extended (GOSE) score < 4 at the 6-month follow-up.

RESULTS

We had a total of 81 patients with complete data, 80% (65/81) were men, and the median (interquartile range) age was 36 (25-50) years. Median Glasgow Coma Scale (GCS) score on admission was 9 (6-14); 49% (40/81) of patients had severe TBI (GCS 3-8), 32% (26/81) had moderate TBI (GCS 12-9), and 18% (15/81) had mild TBI (GCS 13-15). The median (interquartile range) Abbreviated Injury Score of the head (AISh) was 3 (2-4). The frequency of an unfavorable outcome (GOSE < 4) was 30% (25/81), the median GOSE was 4 (2-5), and the median 6-month mortality rate was 24% (20/81). Comparison between patients with favorable and unfavorable outcomes revealed that those with unfavorable outcome were older, (median age 49 [30-72] vs. 29 [22-41] years, P < 0.01), had lower admission GCS scores (6 [4-8] vs. 13 [8-15], P < 0.01), had higher AISh scores (4 [4-4] vs. 3 [2-4], P < 0.01), had higher Acute Physiology and Chronic Health disease Classification System II scores (17 [15-23] vs. 10 [6-14], P < 0.01), had higher Charlson scores (0 [0-2] vs. 0 [0-0], P < 0.01), and had higher PaCO2 levels (mean 35 ± 8 vs. 32 ± 6 mm Hg, P < 0.01). In a multivariate analysis, age (odds ratio [OR] 1.14, 95% confidence interval [CI] 1.1-1.30, P < 0.01), AISh (OR 4.7, 95% CI 1.55-21.0, P < 0.05), and PaCO2 levels (OR 1.23, 95% CI 1.10-1.53, P < 0.05) were significantly associated with the unfavorable outcomes. When applying the same analysis to the subgroup on mechanical ventilation, AISh (OR 5.4, 95% CI 1.61-28.5, P = 0.017) and PaCO2 levels (OR 1.36, 95% CI 1.13-1.78, P = 0.015) remained significantly associated with the unfavorable outcome.

CONCLUSIONS

Higher PaCO2 levels are associated with an unfavorable outcome in ventilated patients with TBI. These results underscore the importance of PaCO2 levels in patients with TBI and whether it should be adjusted for populations living at higher altitudes.

Hsa_circ_0018401 and miR-127-5p Expressions Are Diagnostic and Prognostic Markers for Traumatic Brain Injury (TBI) in Trauma Patients

This study investigated the potentials of hsa_circ_0018401 and miR-127-5p in traumatic brain injury (TBI) diagnosis, stratification and outcome prediction. A retrospective analysis of clinical data and blood samples of n = 109 TBI patients was performed. Expression levels of hsa_circ_0018401 and miR-127-5p were measured using Real-time PCR. The diagnostic values, as well as the values in TBI stratification, of hsa_circ_0018401 and miR-127-5p were assessed by receiver operating characteristic analyses. The prognostic impacts were investigated for one-year endpoint events using multivariable Cox regression analyses and receiver operating characteristic analysis. The target genes for miR-127-5p were predicted. An upregulation of hsa_circ_0018401 and a downregulation of miR-127-5p expression was detected in patients with TBI, and the highest or lowest levels were found in moderate/severe TBI. A negative correlation between miR-423-3p level and Dual luciferase reporter assay verified the binding relationship between hsa_circ_0018401 and miR-127-5p. Hsa_circ_0018401 and miR-127-5p, used alone or combinedly, showed clinical values for TBI diagnosis and stratification, as well as outcome prediction. The proteins for target genes covered TBI-related functions and pathways. Therefore, hsa_circ_0018401 and miR-127-5p could represent promising new biomarkers to identify TBI from healthy, moderate/severe TBI from mild TBI, as well as to predict the TBI outcome.

PaCO2 Association with Traumatic Brain Injury Patients Outcomes at High Altitude: A Prospective Single-Center Cohort Study

Background

partial pressure of carbon dioxide (PaCO2) is generally known to influence outcome in patients with traumatic brain injury (TBI) at normal altitudes. Less is known about specific relationships of PaCO2 levels and clinical outcomes at high altitudes.

Methods

This is a prospective single-center cohort of consecutive TBI patients admitted to a trauma center located at 2600 meter above sea level. An unfavorable outcome was defined as the Glasgow Outcome Scale-Extended (GOSE) < 4 at 6-month follow-up.

Results

81 patients with complete data, 80% (65/81) were men, and median (IQR) age was 36 (25-50) years). Median Glasgow Coma Scale (GCS) on admission was 9 (6-14), 49% (40/81) were severe (GCS: 3-8), 32% (26/81) moderate (GCS 12 - 9), and 18% (15/81) mild (GCS 13-15) TBI. The median (IQR) Abbreviated Injury Score of the Head (AISh) was 3 (2-4). Frequency of an unfavorable outcome (GOSE < 4) was 30% (25/81), median GOSE was 4 (2-5), and 6-month mortality was 24% (20/81). Comparison between patients with favorable and unfavorable outcomes revealed that those with unfavorable outcome were older, median [49 (30-72) vs. 29 (22-41), P < 0.01], had lower admission GCS [6 (4-8) vs. 13 (8-15), P < 0.01], higher AIS head [4 (4-4) vs. 3(2-4), p < 0.01], higher APACHE II score [17(15-23) vs 10 (6-14), < 0.01), higher Charlson score [0(0-2) vs. 0 (0-0), P < 0.01] and higher PaCO2 (mmHg), mean ± SD, 39 ± 9 vs. 32 ± 6, P < 0.01. In a multivariate analysis, age (OR 1.14 95% CI 1.1-1.30, P < 0.01), AISh (OR 4.7 95% CI 1.55-21.0, P < 0.05), and PaCO2 (OR 1.23 95% CI: 1.10-1.53, P < 0.05) were significantly associated with the unfavorable outcomes. When applying the same analysis to the subgroup on mechanical ventilation, AISh (OR 5.4 95% CI: 1.61-28.5, P = 0.017) and PaCO2 (OR 1.36 95% CI: 1.13-1.78, P = 0.015) remained significantly associated with the unfavorable outcome.

Conclusion

Higher PaCO2 levels are associated with an unfavorable outcome in ventilated TBI patients. These results underscore the importance of PaCO2 level in TBI patients and whether it should be adjusted for populations living at higher altitudes.

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical "life-or-death" decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools.

An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study

Background

Severe traumatic brain injury (sTBI) is extremely disabling and associated with high mortality. Early detection of patients at risk of short-term (≤14 days after injury) death and provision of timely treatment is critical. This study aimed to establish and independently validate a nomogram to estimate individualised short-term mortality for sTBI based on large-scale data from China.

Methods

The data were from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry (between Dec 22, 2014, and Aug 1, 2017; registered at ClinicalTrials.gov, NCT02210221). This analysis included information of eligible patients with diagnosed sTBI from 52 centres (2631 cases). 1808 cases from 36 centres were enrolled in the training group (used to construct the nomogram) and 823 cases from 16 centres were enrolled in the validation group. Multivariate logistic regression was used to identify independent predictors of short-term mortality and establish the nomogram. The discrimination of the nomogram was evaluated using area under the receiver operating characteristic curves (AUC) and concordance indexes (C-index), the calibration was evaluated using calibration curves and Hosmer-Lemeshow tests (H-L tests). Decision curve analysis (DCA) was used to evaluate the net benefit of the model for patients.

Findings

In the training group, multivariate logistic regression demonstrated that age (odds ratio [OR] 1.013, 95% confidence interval [CI] 1.003-1.022), Glasgow Coma Scale score (OR 33.997, 95% CI 14.657-78.856), Injury Severity Score (OR 1.020, 95% CI 1.009-1.032), abnormal pupil status (OR 1.738, 95% CI 1.178-2.565), midline shift (OR 2.266, 95% CI 1.378-3.727), and pre-hospital intubation (OR 2.059, 95% CI 1.472-2.879) were independent predictors for short-term death in patients with sTBI. A nomogram was built using the logistic regression prediction model. The AUC and C-index were 0.859 (95% CI 0.837-0.880). The calibration curve of the nomogram was close to the ideal reference line, and the H-L test p value was 0.504. DCA curve demonstrated significantly better net benefit with the model. Application of the nomogram in external validation group still showed good discrimination (AUC and C-index were 0.856, 95% CI 0.827-0.886), calibration, and clinical usefulness.

Interpretation

A nomogram was developed for predicting the occurrence of short-term (≤14 days after injury) death in patients with sTBI. This can provide clinicians with an effective and accurate tool for the early prediction and timely management of sTBI, as well as support clinical decision-making around the withdrawal of life-sustaining therapy. This nomogram is based on Chinese large-scale data and is especially relevant to low- and middle-income countries.

Funding

Shanghai Academic Research Leader (21XD1422400), Shanghai Medical and Health Development Foundation (20224Z0012).