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

Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review

Traumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI's role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI's implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI's transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients.

Machine Learning Approaches to Prognostication in Traumatic Brain Injury

PURPOSE OF REVIEW

This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook.

RECENT FINDINGS

ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes.

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Applying the Sliding Scale Approach to Quantifying Functional Outcomes Up to Two Years After Severe Traumatic Brain Injury

Outcomes after severe traumatic brain injury (TBI) can be represented by a sliding score that compares actual functional recovery to that predicted by illness severity models. This approach has been applied in clinical trials because of its statistical efficiency and interpretability but has not been used to describe change in functional recovery over time. The objective of this study was to use a sliding scoring system to describe the magnitude of change in Glasgow Outcome Scale Extended (GOSE) score at 6, 12, and 24 months after severe TBI and to compare patients who improved after 6 months to those who did not. This study included consecutive severe TBI patients (Glasgow Coma Scale ≤8; n = 482) from a single center. We grouped patients into four strata based on probability of unfavorable outcome (GOSE = 1-4) using the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) model, selected a dichotomous GOSE threshold within each stratum, and compared each patient's GOSE to this threshold to calculate a score (GOSE-Sliding Scale [SS]) from -5 to +4 at 6, 12, and 24 months. We compared GOSE-SS at 6 months with GOSE-SS at 12 and 24 months and also compared characteristics of participants who improved after 6 months with characteristics of those who did not using χ2 and t tests. Compared with at 6 months, 40% of patients (n = 74) had improved GOSE-SS at 12 months, and 53% had improved GOSE-SS by 24 months (n = 72). Among those who improved at 12 months, the average magnitude of improvement was 1.7 ± 0.9 and among those who improved at 24 months, the average magnitude of improvement was 1.9 ± 1.0. Those who improved their GOSE-SS score from 6 to 24 months had longer hospital stays (mean-difference = 8.6 days; p = 0.03), longer intensive care unit (ICU) stays (mean-difference = 5.5 days; p = 0.02), and longer ventilator time (mean-difference = 5 days; p = 0.02) than those who worsened. These results support an optimistic long-term outlook for severe TBI patients and emphasize the importance of long-term follow-up in severe TBI survivors.

Insights into epileptogenesis from post-traumatic epilepsy

Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.

Comparative analysis of CRASH and IMPACT in predicting the outcome of 340 patients with traumatic brain injury

Background

Both the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) and the Corticosteroid randomization after significant head injury (CRASH) models are globally acknowledged prognostic algorithms for assessing traumatic brain injury (TBI) outcomes. The aim of this study is to externalize the validation process and juxtapose the prognostic accuracy of the CRASH and IMPACT models in moderate-to-severe TBI patients in the Chinese population.

Methods

We conducted a retrospective study encompassing a cohort of 340 adult TBI patients (aged > 18 years), presenting with Glasgow Coma Scale (GCS) scores ranging from 3 to 12. The data were accrued over 2 years (2020-2022). The primary endpoints were 14-day mortality rates and 6-month Glasgow Outcome Scale (GOS) scores. Analytical metrics, including the area under the receiver operating characteristic curve for discrimination and the Brier score for predictive precision were employed to quantitatively evaluate the model performance.

Results

Mortality rates at the 14-day and 6-month intervals, as well as the 6-month unfavorable GOS outcomes, were established to be 22.06, 40.29, and 65.59%, respectively. The IMPACT models had area under the curves (AUCs) of 0.873, 0.912, and 0.927 for the 6-month unfavorable GOS outcomes, with respective Brier scores of 0.14, 0.12, and 0.11. On the other hand, the AUCs associated with the six-month mortality were 0.883, 0.909, and 0.912, and the corresponding Brier scores were 0.15, 0.14, and 0.13, respectively. The CRASH models exhibited AUCs of 0.862 and 0.878 for the 6-month adverse outcomes, with uniform Brier scores of 0.18. The 14-day mortality rates had AUCs of 0.867 and 0.87, and corresponding Brier scores of 0.21 and 0.22, respectively.

Conclusion

Both the CRASH and IMPACT algorithms offer reliable prognostic estimations for patients suffering from craniocerebral injuries. However, compared to the CRASH model, the IMPACT model has superior predictive accuracy, albeit at the cost of increased computational intricacy.

Computational Prognostic Modeling in Traumatic Brain Injury

Traumatic brain injury is the leading cause of death and disability worldwide. Despite this large impact, no predictive models are in widespread use due to tedious data collection requirements, lack of provider trust, and poor performance. Furthermore, these models use simple, often binary, data elements that fail to capture the complex heterogeneity of traumatic brain injury. Recent advances in computational modeling efforts have demonstrated promising results for capturing imaging, clinical, electroencephalographic, and other biomarkers for powerful predictive models. In this review, we provide an overview of efforts in computational modeling in neurotrauma and provide insights into future directions.

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.