Challenges and Opportunities in Multimodal Monitoring and Data Analytics in Traumatic Brain Injury

Adapting segment anything model for hematoma segmentation in traumatic brain injury

Hematoma segmentation in traumatic brain injury (TBI) is critical for accurate diagnosis and effective treatment planning. In this study, we evaluate various automated segmentation models, including stat-of-the-art architecture as benchmarks, and compare their performance with our proposed SAM-Adapter method for segmenting hematomas in brain CT scans. By incorporating the adapter into the vanilla SAM model, we address the challenges in medical imaging, which has very limited annotated datasets, enhancing model performance efficiency. We also find that domain-specific pre-processing, such as contrast adjustment, reduces the need for extensive pretraining, making the model more streamlined. And the model performance benefited with optimization and hyperparameter tuning. Our results demonstrate that the SAM-Adapter model achieved strong performance and reliability in identifying hematomas with Dice (72.34%), IoU (59.78%), 95% HD (5.57), sensitivity (75.39%) and specificity (99.73%). Inter-observer variability was assessed, revealing that the model's performance Dice (67.20%) was closely aligned with human expert agreement Dice (63.79%), suggesting its potential clinical utility. The external validation on the HemSeg-200 dataset, which contains 222 scans, demonstrates the robustness of our approach across diverse cases. These advancements in automatic segmentation hold promise for improving the accuracy and efficiency of TBI diagnosis, supporting clinical decision-making, and enhancing patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44352-025-00011-4.

Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort

Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. This study uses the CAnadian High-Resolution TBI (CAHR-TBI) dataset to examine the impact of temporal resolution changes (1 min to 24 h) on autoregressive integrated moving average (ARIMA) modeling for raw and derived cerebral physiologic signals. Stationarity tests indicated that the majority of the signals required first-order differencing to address persistent trends. A grid search identified optimal ARIMA parameters (p,d,q) for each signal and resolution. Subgroup analyses revealed population-specific differences in temporal structure, and small-scale forecasting using optimal parameters confirmed model adequacy. Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. Ignoring these features risks compromising the significance of models developed from such data. This underscores the need for careful resolution considerations in temporal modeling for TBI care.

Caring for Coma after Severe Brain Injury: Clinical Practices and Challenges to Improve Outcomes: An Initiative by the Curing Coma Campaign

Severe brain injury can result in disorders of consciousness (DoC), including coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. Improved emergency and trauma medicine response, in addition to expanding efforts to prevent premature withdrawal of life-sustaining treatment, has led to an increased number of patients with prolonged DoC. High-quality bedside care of patients with DoC is key to improving long-term functional outcomes. However, there is a paucity of DoC-specific evidence guiding clinicians on efficacious bedside care that can promote medical stability and recovery of consciousness. This Viewpoint describes the state of current DoC bedside care and identifies knowledge and practice gaps related to patient care with DoC collated by the Care of the Patient in Coma scientific workgroup as part of the Neurocritical Care Society's Curing Coma Campaign. The gap analysis identified and organized domains of bedside care that could affect patient outcomes: clinical expertise, assessment and monitoring, timing of intervention, technology, family engagement, cultural considerations, systems of care, and transition to the post-acute continuum. Finally, this Viewpoint recommends future research and education initiatives to address and improve the care of patients with DoC.

An open source autoregulation-based neuromonitoring algorithm shows PRx and optimal CPP association with pediatric traumatic brain injury

This study aimed to develop an open-source algorithm for the pressure-reactivity index (PRx) to monitor cerebral autoregulation (CA) in pediatric severe traumatic brain injury (sTBI) and compared derived optimal cerebral perfusion pressure (CPPopt) with real-time CPP in relation to long-term outcome. Retrospective study in children (< 18 years) with sTBI admitted to the pediatric intensive care unit (PICU) for intracranial pressure (ICP) monitoring between 2016 and 2023. ICP was analyzed on an insult basis and correlated with outcome. PRx was calculated as Pearson correlation coefficient between ICP and mean arterial pressure. CPPopt was derived as weighted average of CPP-PRx over time. Outcome was determined via Pediatric Cerebral Performance Category (PCPC) scale at one year post-injury. Logistic regression and mixed effect models were developed to associate PRx and CPPopt with outcome. In total 50 children were included, 35 with favorable (PCPC 1-3) and 15 with unfavorable outcome (PCPC 4-6). ICP insults correlated with unfavorable outcome at 20 mmHg for 7 min duration. Mean CPPopt yield was 75.4% of monitoring time. Mean and median PRx and CPPopt yield associated with unfavorable outcome, with odds ratio (OR) 2.49 (1.38-4.50), 1.38 (1.08-1.76) and 0.95 (0.92-0.97) (p < 0.001). PRx thresholds 0.0, 0.20, 0.25 and 0.30 resulted in OR 1.01 (1.00-1.02) (p < 0.006). CPP in optimal range associated with unfavorable outcome on day one (0.018, p = 0.029) and four (-0.026, p = 0.025). Our algorithm can obtain optimal targets for pediatric neuromonitoring that showed association with long-term outcome, and is now available open source.

Unlocking the potential of high-resolution multimodality neuromonitoring for traumatic brain injury management: lessons and insights from cases, events, and patterns

Multimodality neuromonitoring represents a crucial cornerstone for patient management after acute brain injury. Despite the potential of multimodality neuromonitoring (particularly high-resolution neuromonitoring data) to transform care, its full benefits are not yet universally realized. There remains a critical need to integrate the interpretation of complex patterns and indices into the real-time clinical decision-making processes. This requires a multidisciplinary approach, to evaluate and discuss the implications of observed patterns in a timely manner, ideally in close temporal proximity to their occurrence. Such a collaborative effort could enable clinicians to harness the full potential of multimodal data. In this educational case-based scoping review, we aim to provide clinicians, researchers, and healthcare professionals with detailed, compelling examples of potential applications of multimodality neuromonitoring, focused on high-resolution modalities within the field of traumatic brain injury. This case series showcases how neuromonitoring modalities such as intracranial pressure, brain tissue oxygenation, near-infrared spectroscopy, and transcranial Doppler can be integrated with cerebral microdialysis, neuroimaging and systemic physiology monitoring. The aim is to demonstrate the value of a multimodal approach based on high-resolution data and derived indices integrated in one monitoring tool, allowing for the improvement of diagnosis, monitoring, and treatment of patients with traumatic brain injury. For this purpose, key concepts are covered, and various cases have been described to illustrate how to make the most of this advanced monitoring technology.

Clinical Impact of Standardized Interpretation and Reporting of Multimodality Neuromonitoring Data

OBJECTIVE

Evaluate the consistency and clinical impact of standardized multimodality neuromonitoring (MNM) interpretation and reporting within a system of care for patients with severe traumatic brain injury (sTBI).

DESIGN

Retrospective, observational historical case-control study.

SETTING

Single-center academic level I trauma center.

INTERVENTIONS

Standardized interpretation of MNM data summarized within daily reports.

MEASUREMENTS MAIN RESULTS

Consecutive patients with sTBI undergoing MNM were included. Historical controls were patients monitored before implementation of standardized MNM interpretation; cases were defined as patients with available MNM interpretative reports. Patient characteristics, physiologic data, and clinical outcomes were recorded, and clinical MNM reporting elements were abstracted. The primary outcome was the Glasgow Outcome Scale score 3-6 months postinjury. One hundred twenty-nine patients were included (age 42 ± 18 yr, 82% men); 45 (35%) patients were monitored before standardized MNM interpretation and reporting, and 84 (65%) patients were monitored after that. Patients undergoing standardized interpretative reporting received fewer hyperosmotic agents (3 [1-6] vs. 6 [1-8]; p = 0.04) and spent less time above an intracranial threshold of 22 mm Hg (22% ± 26% vs. 28% ± 24%; p = 0.05). The MNM interpretation cohort had a lower proportion of anesthetic days (48% [24-70%] vs. 67% [33-91%]; p = 0.02) and higher average end-tidal carbon dioxide during monitoring (34 ± 6 mm Hg vs. 32 ± 6 mm Hg; p < 0.01; d = 0.36). After controlling for injury severity, patients undergoing standardized MNM interpretation and reporting had an odds of 1.5 (95% CI, 1.37-1.59) for better outcomes.

CONCLUSIONS

Standardized interpretation and reporting of MNM data are a novel approach to provide clinical insight and to guide individualized critical care. In patients with sTBI, independent MNM interpretation and communication to bedside clinical care teams may result in improved intracranial pressure control, fewer medical interventions, and changes in ventilatory management. In this study, the implementation of a system for management, including standardized MNM interpretation, was associated with a significant improvement in outcome.

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.

Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis

IMPORTANCE

Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used for these predictive tools since differences between external ventricular drain (EVD) and intraparenchymal monitor (IPM)-derived waveforms have not been well accounted for.

OBJECTIVES

We sought to develop a proof-of-concept ML model differentiating ICP waveforms originating from an EVD or IPM.

DESIGN, SETTING, AND PARTICIPANTS

We examined raw ICP waveform data from the ICU physiology cohort within the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury multicenter study.

MAIN OUTCOMES AND MEASURES

Nested patient-wise five-fold cross-validation and group analysis with bagged decision trees (BDT) and linear discriminant analysis were used for feature selection and fair evaluation. Nine patients were kept as unseen hold-outs for further evaluation.

RESULTS

ICP waveform data totaling 14,110 hours were included from 82 patients (EVD, 47; IPM, 26; both, 9). Mean age, Glasgow Coma Scale (GCS) total, and GCS motor score upon admission, as well as the presence and amount of midline shift, were similar between groups. The model mean area under the receiver operating characteristic curve (AU-ROC) exceeded 0.874 across all folds. In additional rigorous cluster-based subgroup analysis, targeted at testing the resilience of models to cross-validation with smaller subsets constructed to develop models in one confounder set and test them in another subset, AU-ROC exceeded 0.811. In a similar analysis using propensity score-based rather than cluster-based subgroup analysis, the mean AU-ROC exceeded 0.827. Of 842 extracted ICP features, 62 were invariant within every analysis, representing the most accurate and robust differences between ICP monitor types. For the nine patient hold-outs, an AU-ROC of 0.826 was obtained using BDT.

CONCLUSIONS AND RELEVANCE

The developed proof-of-concept ML model identified differences in EVD- and IPM-derived ICP signals, which can provide missing contextual data for large-scale retrospective datasets, prevent bias in computational models that ingest ICP data indiscriminately, and control for confounding using our model's output as a propensity score by to adjust for the monitoring method that was clinically indicated. Furthermore, the invariant features may be leveraged as ICP features for anomaly detection.

Electrical impedance tomography provides information of brain injury during total aortic arch replacement through its correlation with relative difference of neurological biomarkers

Postoperative neurological dysfunction (PND) is one of the most common complications after a total aortic arch replacement (TAAR). Electrical impedance tomography (EIT) monitoring of cerebral hypoxia injury during TAAR is a promising technique for preventing the occurrence of PND. This study aimed to explore the feasibility of electrical impedance tomography (EIT) for warning of potential brain injury during total aortic arch replacement (TAAR) through building the correlation between EIT extracted parameters and variation of neurological biomarkers in serum. Patients with Stanford type A aortic dissection and requiring TAAR who were admitted between December 2021 to March 2022 were included. A 16-electrode EIT system was adopted to monitor each patient's cerebral impedance intraoperatively. Five parameters of EIT signals regarding to the hypothermic circulatory arrest (HCA) period were extracted. Meanwhile, concentration of four neurological biomarkers in serum were measured regarding to time before and right after surgery, 12 h, 24 h and 48 h after surgery. The correlation between EIT parameters and variation of serum biomarkers were analyzed. A total of 57 TAAR patients were recruited. The correlation between EIT parameters and variation of biomarkers were stronger for patients with postoperative neurological dysfunction (PND(+)) than those without postoperative neurological dysfunction (PND(-)) in general. Particularly, variation of S100B after surgery had significantly moderate correlation with two parameters regarding to the difference of impedance between left and right brain which were MRAIabs and TRAIabs (0.500 and 0.485 with p < 0.05, respectively). In addition, significantly strong correlations were seen between variation of S100B at 24 h and the difference of average resistivity value before and after HCA phase (ΔARVHCA), the slope of electrical impedance during HCA (kHCA) and MRAIabs (0.758, 0.758 and 0.743 with p < 0.05, respectively) for patients with abnormal S100B level before surgery. Strong correlations were seen between variation of TAU after surgery and ΔARVHCA, kHCA and the time integral of electrical impedance for half flow of perfusion (TARVHP) (0.770, 0.794 and 0.818 with p < 0.01, respectively) for patients with abnormal TAU level before surgery. Another two significantly moderate correlations were found between TRAIabs and variation of GFAP at 12 h and 24 h (0.521 and 0.521 with p < 0.05, respectively) for patients with a normal GFAP serum level before surgery. The correlations between EIT parameters and serum level of neurological biomarkers were significant in patients with PND, especially for MRAIabs and TRAIabs, indicating that EIT may become a powerful assistant for providing a real-time warning of brain injury during TAAR from physiological perspective and useful guidance for intensive care units.

Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

The State of the Field of Pediatric Multimodality Neuromonitoring

BACKGROUND

The use of multimodal neuromonitoring in pediatrics is in its infancy relative to adult neurocritical care. Multimodal neuromonitoring encompasses the amalgamation of information from multiple individual neuromonitoring devices to gain a more comprehensive understanding of the condition of the brain. It allows for adaptation to the changing state of the brain throughout various stages of injury with potential to individualize and optimize therapies.

METHODS

Here we provide an overview of multimodal neuromonitoring in pediatric neurocritical care and its potential application in the future.

RESULTS

Multimodal neuromonitoring devices are key to the process of multimodal neuromonitoring, allowing for visualization of data trends over time and ideally improving the ability of clinicians to identify patterns and find meaning in the immense volume of data now encountered in the care of critically ill patients at the bedside. Clinical use in pediatrics requires more study to determine best practices and impact on patient outcomes. Potential uses include guidance for targets of physiological parameters in the setting of acute brain injury, neuroprotection for patients at high risk for brain injury, and neuroprognostication. Implementing multimodal neuromonitoring in pediatric patients involves interprofessional collaboration with the development of a simultaneous comprehensive program to support the use of multimodal neuromonitoring while maintaining the fundamental principles of the delivery of neurocritical care at the bedside.

CONCLUSIONS

The possible benefits of multimodal neuromonitoring are immense and have great potential to advance the field of pediatric neurocritical care and the health of critically ill children.

The pressure reactivity index as a measure of cerebral autoregulation and its application in traumatic brain injury management

Severe traumatic brain injury (TBI) is a major cause of morbidity and mortality globally. The Brain Trauma Foundation guidelines advocate for the maintenance of a cerebral perfusion pressure (CPP) between 60 and 70 mmHg following severe TBI. However, such a uniform goal does not account for changes in cerebral autoregulation (CA). CA refers to the complex homeostatic mechanisms by which cerebral blood flow is maintained, despite variations in mean arterial pressure and intracranial pressure. Disruption to CA has become increasingly recognised as a key mediator of secondary brain injury following severe TBI. The pressure reactivity index is calculated as the degree of statistical correlation between the slow wave components of mean arterial pressure and intracranial pressure signals and is a validated dynamic marker of CA status following brain injury. The widespread acceptance of pressure reactivity index has precipitated the consideration of individualised CPP targets or an optimal cerebral perfusion pressure (CPPopt). CPPopt represents an alternative target for cerebral haemodynamic optimisation following severe TBI, and early observational data suggest improved neurological outcomes in patients whose CPP is more proximate to CPPopt. The recent publication of a prospective randomised feasibility study of CPPopt guided therapy in TBI, suggests clinicians caring for such patients should be increasingly familiar with these concepts. In this paper, we present a narrative review of the key landmarks in the development of CPPopt and offer a summary of the evidence for CPPopt-based therapy in comparison to current standards of care.

Practice Standards for the Use of Multimodality Neuromonitoring: A Delphi Consensus Process

OBJECTIVES

To address areas in which there is no consensus for the technologies, effort, and training necessary to integrate and interpret information from multimodality neuromonitoring (MNM).

DESIGN

A three-round Delphi consensus process.

SETTING

Electronic surveys and virtual meeting.

SUBJECTS

Participants with broad MNM expertise from adult and pediatric intensive care backgrounds.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two rounds of surveys were completed followed by a virtual meeting to resolve areas without consensus and a final survey to conclude the Delphi process. With 35 participants consensus was achieved on 49% statements concerning MNM. Neurologic impairment and the potential for MNM to guide management were important clinical considerations. Experts reached consensus for the use of MNM-both invasive and noninvasive-for patients in coma with traumatic brain injury, aneurysmal subarachnoid hemorrhage, and intracranial hemorrhage. There was consensus that effort to integrate and interpret MNM requires time independent of daily clinical duties, along with specific skills and expertise. Consensus was reached that training and educational platforms are necessary to develop this expertise and to provide clinical correlation.

CONCLUSIONS

We provide expert consensus in the clinical considerations, minimum necessary technologies, implementation, and training/education to provide practice standards for the use of MNM to individualize clinical care.

Why Intracranial Compliance Is Not Utilized as a Common Practical Tool in Clinical Practice

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders.

Are We Ready for Clinical Therapy based on Cerebral Autoregulation? A Pro-con Debate

Cerebral autoregulation (CA) is a physiological mechanism that maintains constant cerebral blood flow regardless of changes in cerebral perfusion pressure and prevents brain damage caused by hypoperfusion or hyperperfusion. In recent decades, researchers have investigated the range of systemic blood pressures and clinical management strategies over which cerebral vasculature modifies intracranial hemodynamics to maintain cerebral perfusion. However, proposed clinical interventions to optimize autoregulation status have not demonstrated clear clinical benefit. As future trials are designed, it is crucial to comprehend the underlying cause of our inability to produce robust clinical evidence supporting the concept of CA-targeted management. This article examines the technological advances in monitoring techniques and the accuracy of continuous assessment of autoregulation techniques used in intraoperative and intensive care settings today. It also examines how increasing knowledge of CA from recent clinical trials contributes to a greater understanding of secondary brain injury in many disease processes, despite the fact that the lack of robust evidence influencing outcomes has prevented the translation of CA-guided algorithms into clinical practice.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Traumatic Brain Injury: Intraoperative Management and Intensive Care Unit Multimodality Monitoring

Traumatic brain injury is a devastating event associated with substantial morbidity. Pathophysiology involves the initial trauma, subsequent inflammatory response, and secondary insults, which worsen brain injury severity. Management entails cardiopulmonary stabilization and diagnostic imaging with targeted interventions, such as decompressive hemicraniectomy, intracranial monitors or drains, and pharmacological agents to reduce intracranial pressure. Anesthesia and intensive care requires control of multiple physiologic variables and evidence-based practices to reduce secondary brain injury. Advances in biomedical engineering have enhanced assessments of cerebral oxygenation, pressure, metabolism, blood flow, and autoregulation. Many centers employ multimodality neuromonitoring for targeted therapies with the hope to improve recovery.

Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach

BACKGROUND

Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications.

METHODS

The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features.

RESULTS

PRECICECAP is currently enrolling and will be completed in late 2025.

CONCLUSIONS

Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

Current state of high-fidelity multimodal monitoring in traumatic brain injury

INTRODUCTION

Multimodality monitoring of patients with severe traumatic brain injury (TBI) is primarily performed in neuro-critical care units to prevent secondary harmful brain insults and facilitate patient recovery. Several metrics are commonly monitored using both invasive and non-invasive techniques. The latest Brain Trauma Foundation guidelines from 2016 provide recommendations and thresholds for some of these. Still, high-level evidence for several metrics and thresholds is lacking.

METHODS

Regarding invasive brain monitoring, intracranial pressure (ICP) forms the cornerstone, and pressures above 22 mmHg should be avoided. From ICP, cerebral perfusion pressure (CPP) (mean arterial pressure (MAP)-ICP) and pressure reactivity index (PRx) (a correlation between slow waves MAP and ICP as a surrogate for cerebrovascular reactivity) may be derived. In terms of regional monitoring, partial brain tissue oxygen pressure (PbtO₂) is commonly used, and phase 3 studies are currently ongoing to determine its added effect to outcome together with ICP monitoring. Cerebral microdialysis (CMD) is another regional invasive modality to measure substances in the brain extracellular fluid. International consortiums have suggested thresholds and management strategies, in spite of lacking high-level evidence. Although invasive monitoring is generally safe, iatrogenic hemorrhages are reported in about 10% of cases, but these probably do not significantly affect long-term outcome. Non-invasive monitoring is relatively recent in the field of TBI care, and research is usually from single-center retrospective experiences. Near-infrared spectrometry (NIRS) measuring regional tissue saturation has been shown to be associated with outcome. Transcranial doppler (TCD) has several tentative utilities in TBI like measuring ICP and detecting vasospasm. Furthermore, serial sampling of biomarkers of brain injury in the blood can be used to detect secondary brain injury development.

CONCLUSIONS

In multimodal monitoring, the most important aspect is data interpretation, which requires knowledge of each metric's strengths and limitations. Combinations of several modalities might make it possible to discern specific pathologic states suitable for treatment. However, the cost-benefit should be considered as the incremental benefit of adding several metrics has a low level of evidence, thus warranting additional research.