Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW: We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. RECENT FINDINGS: Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. SUMMARY: The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

Brain-Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap.

BACKGROUND: We developed a gap analysis that examines the role of brain-computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment. METHODS: The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question. RESULTS: We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed. CONCLUSIONS: We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.

Incidence and Outcomes of Acute Respiratory Distress Syndrome in Brain-Injured Patients Receiving Invasive Ventilation: A Secondary Analysis of the ENIO Study.

Background: Acute respiratory distress syndrome (ARDS) is an important pulmonary complication in brain-injured patients receiving invasive mechanical ventilation (IMV). We aimed to evaluate the incidence and association between ARDS and clinical outcomes in patients with different forms of acute brain injury requiring IMV in the intensive care unit (ICU). Methods: This was a preplanned secondary analysis of a prospective, multicenter, international cohort study (NCT03400904). We included brain-injured patients receiving IMV for ≥ 24 h. ARDS was the main exposure of interest and was identified during index ICU admission using the Berlin definition. We examined the incidence and adjusted association of ARDS with ICU mortality, ICU length of stay, duration of IMV, and extubation failure. Outcomes were evaluated using mixed-effect logistic regression and cause-specific Cox proportional hazards models. Results: 1492 patients from 67 hospitals and 16 countries were included in the analysis, of whom 137 individuals developed ARDS (9.2% of overall cohort). Across countries, the median ARDS incidence was 5.1% (interquartile range [IQR] 0-10; range 0-27.3). ARDS was associated with increased ICU mortality (adjusted odds ratio (OR) 2.66; 95% confidence interval [CI], 1.29-5.48), longer ICU length of stay (adjusted hazard ratio [HR] 0.59; 95% CI, 0.48-0.73), and longer duration of IMV (adjusted HR 0.54; 95% CI, 0.44-0.67). The association between ARDS and extubation failure approached statistical significance (adjusted HR 1.48; 95% CI 0.99-2.21). Higher ARDS severity was associated with incrementally longer ICU length of stay and longer cumulative duration of IMV. Findings remained robust in a sensitivity analysis evaluating the magnitude of unmeasured confounding. Conclusions: In this cohort of acutely brain-injured patients, the incidence of ARDS was similar to that reported in other mixed cohorts of critically ill patients. Development of ARDS was associated with worse outcomes.

Cognitive Motor Dissociation: Gap Analysis and Future Directions.

BACKGROUND: Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance. METHODS: The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs. RESULTS: The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces. CONCLUSIONS: To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.

Optimal Design of Clinical Trials Involving Persons with Disorders of Consciousness.

BACKGROUND: Limited data exist regarding the optimal clinical trial design for studies involving persons with disorders of consciousness (DoC), and only a few therapies have been tested in high-quality clinical trials. To address this, the Curing Coma Campaign Clinical Trial Working Group performed a gap analysis on the current state of clinical trials in DoC to identify the optimal clinical design for studies involving persons with DoC. METHODS: The Curing Coma Campaign Clinical Trial Working Group was divided into three subgroups to (1) review clinical trials involving persons with DoC, (2) identify unique challenges in the design of clinical trials involving persons with DoC, and (3) recommend optimal clinical trial designs for DoC. RESULTS: There were 3055 studies screened, and 66 were included in this review. Several knowledge gaps and unique challenges were identified. There is a lack of high-quality clinical trials, and most data regarding patients with DoC are based on observational studies focusing on patients with traumatic brain injury and cardiac arrest. There is a lack of a structured long-term outcome assessment with significant heterogeneity in the methodology, definitions of outcomes, and conduct of studies, especially for long-term follow-up. Another major barrier to conducting clinical trials is the lack of resources, especially in low-income countries. Based on the available data, we recommend incorporating trial designs that use master protocols, sequential multiple assessment randomized trials, and comparative effectiveness research. Adaptive platform trials using a multiarm, multistage approach offer substantial advantages and should make use of biomarkers to assess treatment responses to increase trial efficiency. Finally, sound infrastructure and international collaboration are essential to facilitate the conduct of trials in patients with DoC. CONCLUSIONS: Conduct of trials in patients with DoC should make use of master protocols and adaptive design and establish international registries incorporating standardized assessment tools. This will allow the establishment of evidence-based practice recommendations and decrease variations in care.

Development of a core outcome set for ventilation trials in neurocritical care patients with acute brain injury: protocol for a Delphi consensus study of international stakeholders.

INTRODUCTION: There is little consensus and high heterogeneity on the optimal set of relevant clinical outcomes in research studies regarding extubation in neurocritical care patients with brain injury undergoing mechanical ventilation. The aims of this study are to: (1) develop a core outcome set (COS) and (2) reach consensus on a hierarchical composite endpoint for such studies. METHODS AND ANALYSIS: The study will include a broadly representative, international panel of stakeholders with research and clinical expertise in this field and will involve four stages: (1) a scoping review to generate an initial list of outcomes represented in the literature, (2) an investigator meeting to review the outcomes for inclusion in the Delphi surveys, (3) four rounds of online Delphi consensus-building surveys and (4) online consensus meetings to finalise the COS and hierarchical composite endpoint. ETHICS AND DISSEMINATION: This study received ethical approval from the French Society of Anesthesia and Critical Care Medicine Institutional Review Board (SFAR CERAR-IRB 00010254-2023-029). The study results will be disseminated through communication to stakeholders, publication in a peer-reviewed journal, and presentations at conferences. TRIAL REGISTRATION NUMBER: This study is registered with the Core Outcome Measures in Effectiveness Trials (COMET) Initiative.

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury.

Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50-54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90-91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4-6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging.

BACKGROUND: Over the past 5 decades, advances in neuroimaging have yielded insights into the pathophysiologic mechanisms that cause disorders of consciousness (DoC) in patients with severe brain injuries. Structural, functional, metabolic, and perfusion imaging studies have revealed specific neuroanatomic regions, such as the brainstem tegmentum, thalamus, posterior cingulate cortex, medial prefrontal cortex, and occipital cortex, where lesions correlate with the current or future state of consciousness. Advanced imaging modalities, such as diffusion tensor imaging, resting-state functional magnetic resonance imaging (fMRI), and task-based fMRI, have been used to improve the accuracy of diagnosis and long-term prognosis, culminating in the endorsement of fMRI for the clinical evaluation of patients with DoC in the 2018 US (task-based fMRI) and 2020 European (task-based and resting-state fMRI) guidelines. As diverse neuroimaging techniques are increasingly used for patients with DoC in research and clinical settings, the need for a standardized approach to reporting results is clear. The success of future multicenter collaborations and international trials fundamentally depends on the implementation of a shared nomenclature and infrastructure. METHODS: To address this need, the Neurocritical Care Society's Curing Coma Campaign convened an international panel of DoC neuroimaging experts to propose common data elements (CDEs) for data collection and reporting in this field. RESULTS: We report the recommendations of this CDE development panel and disseminate CDEs to be used in neuroimaging studies of patients with DoC. CONCLUSIONS: These CDEs will support progress in the field of DoC neuroimaging and facilitate international collaboration.

Association of Noninvasive Respiratory Support with Extubation Outcomes in Brain-injured Patients Receiving Mechanical Ventilation: A Secondary Analysis of the ENIO Prospective Observational Study.

Rationale: Noninvasive respiratory support using a high-flow nasal cannula (HFNC) or noninvasive positive pressure ventilation (NIPPV) can decrease the risk of reintubation in patients being liberated from mechanical ventilation, but effects in patients with acute brain injury (ABI) are unknown. Objectives: To evaluate the association between postextubation noninvasive respiratory support and reintubation in patients with ABI being liberated from mechanical ventilation. Methods: This was a secondary analysis of a prospective, observational study of mechanically ventilated patients with ABI (clinicaltrials.gov identifier NCT03400904). The primary endpoint was reintubation during ICU admission. We used mixed-effects logistic regression models with patient-level covariates and random intercepts for hospital and country to evaluate the association between prophylactic (i.e., planned) HFNC or NIPPV and reintubation. Measurements and Main Results: 1,115 patients were included from 62 hospitals and 19 countries, of whom 267 received HFNC or NIPPV following extubation (23.9%). Compared with conventional oxygen therapy, neither prophylactic HFNC nor NIPPV was associated with decreased odds of reintubation (respectively, odds ratios of 0.97 [95% confidence interval, 0.54-1.73] and 0.63 [0.30-1.32]). Findings remained consistent in sensitivity analyses accounting for alternate adjustment procedures, missing data, shorter time frames of the primary endpoint, and competing risks precluding reintubation. In a Bayesian analysis using skeptical and data-driven priors, the probabilities of reduced reintubation ranged from 17% to 34% for HFNC and from 46% to 74% for NIPPV. Conclusions: In a large cohort of brain-injured patients undergoing liberation from mechanical ventilation, prophylactic use of HFNC and NIPPV were not associated with reintubation. Prospective trials are needed to confirm treatment effects in this population. Primary study registered with www.clinicaltrials.gov (NCT03400904).

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Utilization of mechanical power and associations with clinical outcomes in brain injured patients: a secondary analysis of the extubation strategies in neuro-intensive care unit patients and associations with outcome (ENIO) trial.

BACKGROUND: There is insufficient evidence to guide ventilatory targets in acute brain injury (ABI). Recent studies have shown associations between mechanical power (MP) and mortality in critical care populations. We aimed to describe MP in ventilated patients with ABI, and evaluate associations between MP and clinical outcomes. METHODS: In this preplanned, secondary analysis of a prospective, multi-center, observational cohort study (ENIO, NCT03400904), we included adult patients with ABI (Glasgow Coma Scale ≤ 12 before intubation) who required mechanical ventilation (MV) ≥ 24 h. Using multivariable log binomial regressions, we separately assessed associations between MP on hospital day (HD)1, HD3, HD7 and clinical outcomes: hospital mortality, need for reintubation, tracheostomy placement, and development of acute respiratory distress syndrome (ARDS). RESULTS: We included 1217 patients (mean age 51.2 years [SD 18.1], 66% male, mean body mass index [BMI] 26.3 [SD 5.18]) hospitalized at 62 intensive care units in 18 countries. Hospital mortality was 11% (n = 139), 44% (n = 536) were extubated by HD7 of which 20% (107/536) required reintubation, 28% (n = 340) underwent tracheostomy placement, and 9% (n = 114) developed ARDS. The median MP on HD1, HD3, and HD7 was 11.9 J/min [IQR 9.2-15.1], 13 J/min [IQR 10-17], and 14 J/min [IQR 11-20], respectively. MP was overall higher in patients with ARDS, especially those with higher ARDS severity. After controlling for same-day pressure of arterial oxygen/fraction of inspired oxygen (P/F ratio), BMI, and neurological severity, MP at HD1, HD3, and HD7 was independently associated with hospital mortality, reintubation and tracheostomy placement. The adjusted relative risk (aRR) was greater at higher MP, and strongest for: mortality on HD1 (compared to the HD1 median MP 11.9 J/min, aRR at 17 J/min was 1.22, 95% CI 1.14-1.30) and HD3 (1.38, 95% CI 1.23-1.53), reintubation on HD1 (1.64; 95% CI 1.57-1.72), and tracheostomy on HD7 (1.53; 95%CI 1.18-1.99). MP was associated with the development of moderate-severe ARDS on HD1 (2.07; 95% CI 1.56-2.78) and HD3 (1.76; 95% CI 1.41-2.22). CONCLUSIONS: Exposure to high MP during the first week of MV is associated with poor clinical outcomes in ABI, independent of P/F ratio and neurological severity. Potential benefits of optimizing ventilator settings to limit MP warrant further investigation.

A Focus on Subtle Signs and Motor Behavior to Unveil Awareness in Unresponsive Brain-Impaired Patients: The Importance of Being Clinical.

Brain-injured patients in a state of cognitive motor dissociation (CMD) exhibit a lack of command following using conventional neurobehavioral examination tools but a high level of awareness and language processing when assessed using advanced imaging and electrophysiology techniques. Because of their behavioral unresponsiveness, patients with CMD may seem clinically indistinguishable from those with a true disorder of consciousness that affects awareness on a substantial level (coma, vegetative state/unresponsive wakefulness state, or minimally conscious state minus). Yet, by expanding the range of motor testing across limb, facial, and ocular motricity, we may detect subtle, purposeful movements even in the subset of patients classified as vegetative state/unresponsive wakefulness state. We propose the term of clinical CMD to describe patients showing these slight but determined motor responses and exhibiting a characteristic akinetic motor behavior as opposed to a pyramidal motor system behavior. These patients may harbor hidden cognitive capabilities and significant potential for a good long-term outcome. Indeed, we envision CMD as ranging from complete (no motor response) to partial (subtle clinical motor response) forms, falling within a spectrum of progressively better motor output in patients with considerable cognitive capabilities. In addition to providing a decisional flowchart, we present this novel approach to classification as a graphical model that illustrates the range of clinical manifestations and recovery trajectories fundamentally differentiating true disorders of consciousness from the spectrum of CMD.

Determinants of Professional Fulfillment and Burnout Among Intensivists: A National Survey by the Society of Critical Care Anesthesiologists in 2022.

BACKGROUND: Increased burnout and decreased professional fulfillment among intensive care physicians is partly due to intensive care unit (ICU) workload. Although the SARS-CoV-2 (COVID-19) pandemic increased ICU workload, it also may have increased feelings of personal fulfillment due to positive public perceptions of physicians caring for COVID patients. We surveyed critical care anesthesiologists to identify the effect of provider demographics, ICU workload, and COVID-19-related workload, on professional fulfillment and burnout. METHODS: We performed an exploratory survey of 606 members of the Society of Critical Care Anesthesiologists (SOCCA) in January and February 2022. We used the Stanford Professional Fulfillment Index (PFI) to grade levels of professional fulfillment and markers of burnout (ie, work exhaustion and disengagement). Univariable and multivariable models were used to identify associations between provider demographics and practice characteristics and professional fulfillment and work exhaustion. RESULTS: One hundred and seventy-five intensivists (29%) responded. A total of 65% were male and 49% were between 36 and 45 years old. The overall median PFI score-0 (none) to 24 (most professional fulfillment)-was 17 (IQR, 1-24), with a wide distribution of responses. In multivariable analysis, factors associated with higher professional fulfillment included age >45 years ( P =.004), ≤15 weeks full-time ICU coverage in 2020 ( P =.02), role as medical director ( P =.01), and nighttime home call with supervision of in-house ICU fellows ( P =.01). CONCLUSIONS: Professional fulfillment and work exhaustion in this cross-sectional survey were associated with several demographic and practice characteristics but not COVID-19-related workload, suggesting that COVID-19 workload may not have either positive or negative perceptions on professional fulfillment.

Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.

BACKGROUND: Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records. METHODS: Two prediction models were trained and tested using a multicenter database (years of data collection 2014 to 2015), and externally validated on two single-center databases (2001 to 2012 and 2008 to 2019). The primary outcome variable was delirium defined as a positive Confusion Assessment Method for the ICU screen, or an Intensive Care Delirium Screening Checklist of 4 or greater. The first model, named "24-hour model," used data from the 24 h after ICU admission to predict delirium any time afterward. The second model designated "dynamic model," predicted the onset of delirium up to 12 h in advance. Model performance was compared with a widely cited reference model. RESULTS: For the 24-h model, delirium was identified in 2,536 of 18,305 (13.9%), 768 of 5,299 (14.5%), and 5,955 of 36,194 (11.9%) of patient stays, respectively, in the development sample and two validation samples. For the 12-h lead time dynamic model, delirium was identified in 3,791 of 22,234 (17.0%), 994 of 6,166 (16.1%), and 5,955 of 28,440 (20.9%) patient stays, respectively. Mean area under the receiver operating characteristics curve (AUC) (95% CI) for the first 24-h model was 0.785 (0.769 to 0.801), significantly higher than the modified reference model with AUC of 0.730 (0.704 to 0.757). The dynamic model had a mean AUC of 0.845 (0.831 to 0.859) when predicting delirium 12 h in advance. Calibration was similar in both models (mean Brier Score [95% CI] 0.102 [0.097 to 0.108] and 0.111 [0.106 to 0.116]). Model discrimination and calibration were maintained when tested on the validation datasets. CONCLUSIONS: Machine learning models trained with routinely collected electronic health record data accurately predict ICU delirium, supporting dynamic time-sensitive forecasting.

Nationwide Clinical Practice Patterns of Anesthesiology Critical Care Physicians: A Survey to Members of the Society of Critical Care Anesthesiologists.

BACKGROUND: Despite the growing contributions of critical care anesthesiologists to clinical practice, research, and administrative leadership of intensive care units (ICUs), relatively little is known about the subspecialty-specific clinical practice environment. An understanding of contemporary clinical practice is essential to recognize the opportunities and challenges facing critical care anesthesia, optimize staffing patterns, assess sustainability and satisfaction, and strategically plan for future activity, scope, and training. This study surveyed intensivists who are members of the Society of Critical Care Anesthesiologists (SOCCA) to evaluate practice patterns of critical care anesthesiologists, including compensation, types of ICUs covered, models of overnight ICU coverage, and relationships between these factors. We hypothesized that variability in compensation and practice patterns would be observed between individuals. METHODS: Board-certified critical care anesthesiologists practicing in the United States were identified using the SOCCA membership distribution list and invited to take a voluntary online survey between May and June 2021. Multiple-choice questions with both single- and multiple-select options were used for answers with categorical data, and adaptive questioning was used to clarify stem-based responses. Respondents were asked to describe practice patterns at their respective institutions and provide information about their demographics, salaries, effort in ICUs, as well as other activities. RESULTS: A total of 490 participants were invited to take this survey, and 157 (response rate 32%) surveys were completed and analyzed. The majority of respondents were White (73%), male (69%), and younger than 50 years of age (82%). The cardiothoracic/cardiovascular ICU was the most common practice setting, with 69.5% of respondents reporting time working in this unit. Significant variability was observed in ICU practice patterns. Respondents reported spending an equal proportion of their time in clinical practice in the operating rooms and ICUs (median, 40%; interquartile range [IQR], 20%-50%), whereas a smaller proportion-primarily those who completed their training before 2009-reported administrative or research activities. Female respondents reported salaries that were $36,739 less than male respondents; however, this difference was not statistically different, and after adjusting for age and practice type, these differences were less pronounced (-$27,479.79; 95% confidence interval [CI], -$57,232.61 to $2273.03; P = .07). CONCLUSIONS: These survey data provide a current snapshot of anesthesiology critical care clinical practice patterns in the United States. Our findings may inform decision-making around the initiation and expansion of critical care services and optimal staffing patterns, as well as provide a basis for further work that focuses on intensivist satisfaction and burnout.