Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review

A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis

OBJECTIVE

The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI).

METHODS

This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms.

RESULTS

This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%.

CONCLUSION

The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies.

SIGNIFICANCE

The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.

A Comprehensive Perspective on Intracranial Pressure Monitoring and Individualized Management in Neurocritical Care: Results of a Survey with Global Experts

BACKGROUND

Numerous trials have addressed intracranial pressure (ICP) management in neurocritical care. However, identifying its harmful thresholds and controlling ICP remain challenging in terms of improving outcomes. Evidence suggests that an individualized approach is necessary for establishing tolerance limits for ICP, incorporating factors such as ICP waveform (ICPW) or pulse morphology along with additional data provided by other invasive (e.g., brain oximetry) and noninvasive monitoring (NIM) methods (e.g., transcranial Doppler, optic nerve sheath diameter ultrasound, and pupillometry). This study aims to assess current ICP monitoring practices among experienced clinicians and explore whether guidelines should incorporate ancillary parameters from NIM and ICPW in future updates.

METHODS

We conducted a survey among experienced professionals involved in researching and managing patients with severe injury across low-middle-income countries (LMICs) and high-income countries (HICs). We sought their insights on ICP monitoring, particularly focusing on the impact of NIM and ICPW in various clinical scenarios.

RESULTS

From October to December 2023, 109 professionals from the Americas and Europe participated in the survey, evenly distributed between LMIC and HIC. When ICP ranged from 22 to 25 mm Hg, 62.3% of respondents were open to considering additional information, such as ICPW and other monitoring techniques, before adjusting therapy intensity levels. Moreover, 77% of respondents were inclined to reassess patients with ICP in the 18-22 mm Hg range, potentially escalating therapy intensity levels with the support of ICPW and NIM. Differences emerged between LMIC and HIC participants, with more LMIC respondents preferring arterial blood pressure transducer leveling at the heart and endorsing the use of NIM techniques and ICPW as ancillary information.

CONCLUSIONS

Experienced clinicians tend to personalize ICP management, emphasizing the importance of considering various monitoring techniques. ICPW and noninvasive techniques, particularly in LMIC settings, warrant further exploration and could potentially enhance individualized patient care. The study suggests updating guidelines to include these additional components for a more personalized approach to ICP management.

A Nomogram to Predict Intracranial Hypertension in Moderate Traumatic Brain Injury Patients

OBJECTIVE

Patients with moderate traumatic brain injury (mTBI) are under the threat of intracranial hypertension (IHT). However, it is unclear which mTBI patient will develop IHT and should receive intracranial pressure (ICP)-lowering treatment or invasive ICP monitoring after admission. The purpose of the present study was to develop and validate a prediction model that estimates the risk of IHT in mTBI patients.

METHODS

Baseline data collected on admission of 296 mTBI patients with Glasgow Coma Scale (GCS) score of 9-11 was collected and analyzed. Multivariable logistic regression modeling with backward stepwise elimination was used to develop a prediction model for IHT. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Finally, the prediction model was validated in a separate cohort of 122 patients from 3 hospitals.

RESULTS

Four independent prognostic factors for IHT were identified: GCS score, Marshall head computed tomography score, injury severity score, and location of contusion. The C-statistic of the prediction model in internal validation was 84.30% (95% CI: 0.794-0.892). The area under the curve for the prediction model in external validation was 82.80% (95% CI: 0.747-0.909).

CONCLUSIONS

A prediction model based on baseline parameters was found to be highly sensitive in distinguishing mTBI patients with GCS score of 9-11 who would suffer IHT. The high discriminative ability of the prediction model supports its use in identifying mTBI patients with GCS score of 9-11 who need ICP-lowering therapy or invasive ICP monitoring.

Intracranial pressure monitoring in adult patients with traumatic brain injury: challenges and innovations

Intracranial pressure monitoring enables the detection and treatment of intracranial hypertension, a potentially lethal insult after traumatic brain injury. Despite its widespread use, robust evidence supporting intracranial pressure monitoring and treatment remains sparse. International studies have shown large variations between centres regarding the indications for intracranial pressure monitoring and treatment of intracranial hypertension. Experts have reviewed these two aspects and, by consensus, provided practical approaches for monitoring and treatment. Advances have occurred in methods for non-invasive estimation of intracranial pressure although, for now, a reliable way to non-invasively and continuously measure intracranial pressure remains aspirational. Analysis of the intracranial pressure signal can provide information on brain compliance (ie, the ability of the cranium to tolerate volume changes) and on cerebral autoregulation (ie, the ability of cerebral blood vessels to react to changes in blood pressure). The information derived from the intracranial pressure signal might allow for more individualised patient management. Machine learning and artificial intelligence approaches are being increasingly applied to intracranial pressure monitoring, but many obstacles need to be overcome before their use in clinical practice could be attempted. Robust clinical trials are needed to support indications for intracranial pressure monitoring and treatment. Progress in non-invasive assessment of intracranial pressure and in signal analysis (for targeted treatment) will also be crucial.

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating "real-time model testing" stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review

BACKGROUND

Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI.

METHODS

The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies.

RESULTS

A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles.

CONCLUSIONS

AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury

BACKGROUND

Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.

METHODS

We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.

RESULTS

The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.

CONCLUSIONS

The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary

Introduction

Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI).

Research question

We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models.

Material and methods

We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing.

Results

ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required.

Conclusions

Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.

PRECISION-TBI: a study protocol for a vanguard prospective cohort study to enhance understanding and management of moderate to severe traumatic brain injury in Australia

INTRODUCTION

Traumatic brain injury (TBI) is a heterogeneous condition in terms of pathophysiology and clinical course. Outcomes from moderate to severe TBI (msTBI) remain poor despite concerted research efforts. The heterogeneity of clinical management represents a barrier to progress in this area. PRECISION-TBI is a prospective, observational, cohort study that will establish a clinical research network across major neurotrauma centres in Australia. This network will enable the ongoing collection of injury and clinical management data from patients with msTBI, to quantify variations in processes of care between sites. It will also pilot high-frequency data collection and analysis techniques, novel clinical interventions, and comparative effectiveness methodology.

METHODS AND ANALYSIS

PRECISION-TBI will initially enrol 300 patients with msTBI with Glasgow Coma Scale (GCS) <13 requiring intensive care unit (ICU) admission for invasive neuromonitoring from 10 Australian neurotrauma centres. Demographic data and process of care data (eg, prehospital, emergency and surgical intervention variables) will be collected. Clinical data will include prehospital and emergency department vital signs, and ICU physiological variables in the form of high frequency neuromonitoring data. ICU treatment data will also be collected for specific aspects of msTBI care. Six-month extended Glasgow Outcome Scores (GOSE) will be collected as the key outcome. Statistical analysis will focus on measures of between and within-site variation. Reports documenting performance on selected key quality indicators will be provided to participating sites.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from The Alfred Human Research Ethics Committee (Alfred Health, Melbourne, Australia). All eligible participants will be included in the study under a waiver of consent (hospital data collection) and opt-out (6 months follow-up). Brochures explaining the rationale of the study will be provided to all participants and/or an appropriate medical treatment decision-maker, who can act on the patient's behalf if they lack capacity. Study findings will be disseminated by peer-review publications.

TRIAL REGISTRATION NUMBER

NCT05855252.

Management Strategies Based on Multi-Modality Neuromonitoring in Severe Traumatic Brain Injury

Secondary brain injury after neurotrauma is comprised of a host of distinct, potentially concurrent and interacting mechanisms that may exacerbate primary brain insult. Multimodality neuromonitoring is a method of measuring multiple aspects of the brain in order to understand the signatures of these different pathomechanisms and to detect, treat, or prevent potentially reversible secondary brain injuries. The most studied invasive parameters include intracranial pressure (ICP), cerebral perfusion pressure (CPP), autoregulatory indices, brain tissue partial oxygen tension, and tissue energy and metabolism measures such as the lactate pyruvate ratio. Understanding the local metabolic state of brain tissue in order to infer pathology and develop appropriate management strategies is an area of active investigation. Several clinical trials are underway to define the role of brain tissue oxygenation monitoring and electrocorticography in conjunction with other multimodal neuromonitoring information, including ICP and CPP monitoring. Identifying an optimal CPP to guide individualized management of blood pressure and ICP has been shown to be feasible, but definitive clinical trial evidence is still needed. Future work is still needed to define and clinically correlate patterns that emerge from integrated measurements of metabolism, pressure, flow, oxygenation, and electrophysiology. Pathophysiologic targets and precise critical care management strategies to address their underlying causes promise to mitigate secondary injuries and hold the potential to improve patient outcome. Advancements in clinical trial design are poised to establish new standards for the use of multimodality neuromonitoring to guide individualized clinical care.

Apelin-Overexpressing Neural Stem Cells in Conjunction with a Silk Fibroin Nanofiber Scaffold for the Treatment of Traumatic Brain Injury

Traumatic brain injury (TBI), especially moderate or severe TBI, is one of the most devastating injuries to the nervous system, as the existing therapies for neurological defect repair have difficulty achieving satisfactory results. Neural stem cells (NSCs) therapy is a potentially effective treatment option, especially after specific genetic modifications and when used in combination with biomimetic biological scaffolds. In this study, tussah silk fibroin (TSF) scaffolds with interconnected nanofibrous structures were fabricated using a top-down method. We constructed the apelin-overexpressing NSCs that were cocultured with a TSF nanofiber scaffold (TSFNS) that simulated the extracellular matrix in vitro. To verify the therapeutic efficacy of engineered NSCs in vivo, we constructed TBI models and randomized the C57BL/6 mice into three groups: a control group, an NSC-ctrl group (transplantation of NSCs integrated on TSFNS), and an NSC-apelin group (transplantation of apelin-overexpressing NSCs integrated on TSFNS). The neurological functions of the model mice were evaluated in stages. Specimens were obtained 24 days after transplantation for immunohistochemistry, immunofluorescence, and western blot experiments, and statistical analysis was performed. The results showed that the combination of the TSFNS and apelin overexpression guided extension and elevated the proliferation and differentiation of NSCs both in vivo and in vitro. Moreover, the transplantation of TSFNS-NSCs-Apelin reduced lesion volume, enhanced angiogenesis, inhibited neuronal apoptosis, reduced blood-brain barrier damage, and mitigated neuroinflammation. In summary, TSFNS-NSC-Apelin therapy could build a microenvironment that is more conducive to neural repair to promote the recovery of injured neurological function.