Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study

An Update on Curing Coma Campaign

The Curing Coma Campaign (CCC) and its contributing collaborators identified multiple key areas of knowledge and research gaps in coma and disorders of consciousness (DoC). This step was a crucial effort and essential to prioritize future educational and research efforts. These key areas include defining categories of DoC, assessing DoC using multimodal approach (e.g., behavioral assessment tools, advanced neuroimaging studies), discussing optimal clinical trials' design and exploring computational models to conduct clinical trials in patients with DoC, and establishing common data elements to standardize data collection. Other key areas focused on creating coma care registry and educating clinicians and patients and promoting awareness of DoC to improve care in patients with DoC. The ongoing efforts in these key areas are discussed.

Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Evolution of brain injury and neurological dysfunction after cardiac arrest in the rat - A multimodal and comprehensive model

Cardiac arrest (CA) is one of the leading causes of death worldwide. Due to hypoxic ischemic brain injury, CA survivors may experience variable degrees of neurological dysfunction. This study, for the first time, describes the progression of CA-induced neuropathology in the rat. CA rats displayed neurological and exploratory deficits. Brain MRI revealed cortical and striatal edema at 3 days (d), white matter (WM) damage in corpus callosum (CC), external capsule (EC), internal capsule (IC) at d7 and d14. At d3 a brain edema significantly correlated with neurological score. Parallel neuropathological studies showed neurodegeneration, reduced neuronal density in CA1 and hilus of hippocampus at d7 and d14, with cells dying at d3 in hilus. Microgliosis increased in cortex (Cx), caudate putamen (Cpu), CA1, CC, and EC up to d14. Astrogliosis increased earlier (d3 to d7) in Cx, Cpu, CC and EC compared to CA1 (d7 to d14). Plasma levels of neurofilament light (NfL) increased at d3 and remained elevated up to d14. NfL levels at d7 correlated with WM damage. The study shows the consequences up to 14d after CA in rats, introducing clinically relevant parameters such as advanced neuroimaging and blood biomarker useful to test therapeutic interventions in this model.

Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Coma Prognostication After Acute Brain Injury: A Review

Importance

Among the most impactful neurologic assessments is that of neuroprognostication, defined here as the prediction of neurologic recovery from disorders of consciousness caused by severe, acute brain injury. Across a range of brain injury etiologies, these determinations often dictate whether life-sustaining treatment is continued or withdrawn; thus, they have major implications for morbidity, mortality, and health care costs. Neuroprognostication relies on a diverse array of tests, including behavioral, radiologic, physiological, and serologic markers, that evaluate the brain's functional and structural integrity.

Observations

Prognostic markers, such as the neurologic examination, electroencephalography, and conventional computed tomography and magnetic resonance imaging (MRI), have been foundational in assessing a patient's current level of consciousness and capacity for recovery. Emerging techniques, such as functional MRI, diffusion MRI, and advanced forms of electroencephalography, provide new ways of evaluating the brain, leading to evolving schemes for characterizing neurologic function and novel methods for predicting recovery.

Conclusions and Relevance

Neuroprognostic markers are rapidly evolving as new ways of assessing the brain's structural and functional integrity after brain injury are discovered. Many of these techniques remain in development, and further research is needed to optimize their prognostic utility. However, even as such efforts are underway, a series of promising findings coupled with the imperfect predictive value of conventional prognostic markers and the high stakes of these assessments have prompted clinical guidelines to endorse emerging techniques for neuroprognostication. Thus, clinicians have been thrust into an uncertain predicament in which emerging techniques are not yet perfected but too promising to ignore. This review illustrates the current, and likely future, landscapes of prognostic markers. No matter how much prognostic markers evolve and improve, these assessments must be approached with humility and individualized to reflect each patient's values.

MRI volumetry and diffusion tensor imaging for diagnosis and follow-up of late post-traumatic injuries

BACKGROUND

Traumatic Brain Injury (TBI) is a major cause of acquired disability and can cause devastating and progressive post-traumatic encephalopathy. TBI is a dynamic condition that continues to evolve over time. A better understanding of the pathophysiology of these late lesions is important for the development of new therapeutic strategies.

OBJECTIVES

The primary objective was to compare the ability of fluid-attenuated reversion recovery (FLAIR) and diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) markers to identify participants with a Glasgow outcome scale extended (GOS-E) score of 7-8, up to 10 years after their original TBI. The secondary objective was to study the brain regionalization of DTI markers. Finally, we analyzed the evolution of late-developing brain lesions using repeated MRI images, also taken up to 10 years after the TBI.

METHODS

In this retrospective study, participants were included from a cohort of people hospitalized following a severe TBI. Following their discharge, they were followed-up and clinically assessed, including a DTI-MRI scan, between 2012 and 2016. We performed a cross-sectional analysis on 97 participants at a median (IQR) of 5 years (3-6) post-TBI, and a further post-TBI longitudinal analysis over 10 years on a subpopulation (n = 17) of the cohort.

RESULTS

Although the area under the curve (AUC) of FLAIR, fractional anisotropy (FA), and mean diffusivity (MD) were not significantly different, only the AUC of FA was statistically greater than 0.5. In addition, only the FA was correlated with clinical outcomes as assessed by GOS-E score (P<10-4). On the cross-sectional analysis, DTI markers allowed study post-TBI white matter lesions by region. In the longitudinal subpopulation analysis, the observed number of brain lesions increased for the first 5 years post-TBI, before stabilizing over the next 5 years.

CONCLUSIONS

This study has shown for the first time that post-TBI lesions can present in a two-phase evolution. These results must be confirmed in larger studies. French Data Protection Agency (Commission nationale de l'informatique et des libertés; CNIL) study registration no: 1934708v0.

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Evaluation and Management of Disorders of Consciousness in the Acute Care Setting

Acute disorders of consciousness (DOC) are impairments in arousal and awareness that occur within 28 days of an initial injury and can result from a variety of insults. These states range from coma, unresponsive wakefulness, covert consciousness, minimal consciousness, to confusional state. It is important to perform thorough, serial examinations with particular emphasis on the level of consciousness, brainstem reflexes, and motor responses. Evaluation of acute DOC includes laboratory tests, imaging, and electrophysiology testing. Prognostication in the acute phase of DOC must be done cautiously, using open, frequent communication with families, and by acknowledging significant multidimensional uncertainty.

Neuroimaging in Disorders of Consciousness and Recovery

There is a clinical need for more accurate diagnosis and prognostication in patients with disorders of consciousness (DoC). There are several neuroimaging modalities that enable detailed, quantitative assessment of structural and functional brain injury, with demonstrated diagnostic and prognostic value. Additionally, longitudinal neuroimaging studies have hinted at quantifiable structural and functional neuroimaging biomarkers of recovery, with potential implications for the management of DoC.

Mastering the brain in critical conditions: an update

Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course.

Covert consciousness

Covert consciousness is a state of residual awareness following severe brain injury or neurological disorder that evades routine bedside behavioral detection. Patients with covert consciousness have preserved awareness but are incapable of self-expression through ordinary means of behavior or communication. Growing recognition of the limitations of bedside neurobehavioral examination in reliably detecting consciousness, along with advances in neurotechnologies capable of detecting brain states or subtle signs indicative of consciousness not discernible by routine examination, carry promise to transform approaches to classifying, diagnosing, prognosticating and treating disorders of consciousness. Here we describe and critically evaluate the evolving clinical category of covert consciousness, including approaches to its diagnosis through neuroimaging, electrophysiology, and novel behavioral tools, its prognostic relevance, and open questions pertaining to optimal clinical management of patients with covert consciousness recovering from severe brain injury.

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.

Indoleamine 2,3-Dioxygenase Deletion to Modulate Kynurenine Pathway and to Prevent Brain Injury after Cardiac Arrest in Mice

BACKGROUND

The catabolism of the essential amino acid tryptophan to kynurenine is emerging as a potential key pathway involved in post-cardiac arrest brain injury. The aim of this study was to evaluate the effects of the modulation of kynurenine pathway on cardiac arrest outcome through genetic deletion of the rate-limiting enzyme of the pathway, indoleamine 2,3-dioxygenase.

METHODS

Wild-type and indoleamine 2,3-dioxygenase-deleted (IDO-/-) mice were subjected to 8-min cardiac arrest. Survival, neurologic outcome, and locomotor activity were evaluated after resuscitation. Brain magnetic resonance imaging with diffusion tensor and diffusion-weighted imaging sequences was performed, together with microglia and macrophage activation and neurofilament light chain measurements.

RESULTS

IDO-/- mice showed higher survival compared to wild-type mice (IDO-/- 11 of 16, wild-type 6 of 16, log-rank P = 0.036). Neurologic function was higher in IDO-/- mice than in wild-type mice after cardiac arrest (IDO-/- 9 ± 1, wild-type 7 ± 1, P = 0.012, n = 16). Indoleamine 2,3-dioxygenase deletion preserved locomotor function while maintaining physiologic circadian rhythm after cardiac arrest. Brain magnetic resonance imaging with diffusion tensor imaging showed an increase in mean fractional anisotropy in the corpus callosum (IDO-/- 0.68 ± 0.01, wild-type 0.65 ± 0.01, P = 0.010, n = 4 to 5) and in the external capsule (IDO-/- 0.47 ± 0.01, wild-type 0.45 ± 0.01, P = 0.006, n = 4 to 5) in IDO-/- mice compared with wild-type ones. Increased release of neurofilament light chain was observed in wild-type mice compared to IDO-/- (median concentrations [interquartile range], pg/mL: wild-type 1,138 [678 to 1,384]; IDO-/- 267 [157 to 550]; P < 0.001, n = 3 to 4). Brain magnetic resonance imaging with diffusion-weighted imaging revealed restriction of water diffusivity 24 h after cardiac arrest in wild-type mice; indoleamine 2,3-dioxygenase deletion prevented water diffusion abnormalities, which was reverted in IDO-/- mice receiving l-kynurenine (apparent diffusion coefficient, μm2/ms: wild-type, 0.48 ± 0.07; IDO-/-, 0.59 ± 0.02; IDO-/- and l-kynurenine, 0.47 ± 0.08; P = 0.007, n = 6).

CONCLUSIONS

The kynurenine pathway represents a novel target to prevent post-cardiac arrest brain injury. The neuroprotective effects of indoleamine 2,3-dioxygenase deletion were associated with preservation of brain white matter microintegrity and with reduction of cerebral cytotoxic edema.

EDITOR’S PERSPECTIVE

Neuroprognostication after cardiac arrest: what the cardiologist should know

Two aspects are a key to mastering prognostication of comatose cardiac arrest survivors: a detailed knowledge about the clinical trajectories of consciousness recovery (or lack thereof) and the ability to correctly interpret the results of multimodal investigations, which include clinical examination, electroencephalography, neuroimaging, evoked potentials, and blood biomarkers. While the very good and the very poor ends of the clinical spectrum typically do not pose diagnostic challenges, the intermediate 'grey zone' of post-cardiac arrest encephalopathy requires cautious interpretation of the available information and sufficiently long clinical observation. Late recovery of coma patients with initially ambiguous diagnostic results is increasingly reported, as are unresponsive patients with various forms of residual consciousness, including so-called cognitive motor dissociation, rendering prognostication of post-anoxic coma highly complex. The aim of this paper is to provide busy clinicians with a high-yield, concise overview of neuroprognostication after cardiac arrest, emphasizing notable developments in the field since 2020.

State of the art post-cardiac arrest care: evolution and future of post cardiac arrest care

Out-of-hospital cardiac arrest is a leading cause of mortality. In the pre-hospital setting, bystander response with cardiopulmonary resuscitation and the use of publicly available automated external defibrillators have been associated with improved survival. Early in-hospital treatment still focuses on emergency coronary angiography for selected patients. For patients remaining comatose, temperature control to avoid fever is still recommended, but former hypothermic targets have been abandoned. For patients without spontaneous awakening, the use of a multimodal prognostication model is key. After discharge, follow-up with screening for cognitive and emotional disabilities is recommended. There has been an incredible evolution of research on cardiac arrest. Two decades ago, the largest trials include a few hundred patients. Today, undergoing studies are planning to include 10-20 times as many patients, with improved methodology. This article describes the evolution and perspectives for the future in post-cardiac arrest care.

Predicting poor neurological outcomes following out-of-hospital cardiac arrest using neuron-specific enolase and neurofilament light chain in patients with and without haemolysis

Aims

Hypoxic-ischaemic brain injury following out-of-hospital cardiac arrest (OHCA) is a common complication and a major cause of death. Neuron-specific enolase (NSE) and neurofilament light chain (NfL) are released after brain injury and elevated concentrations of both are associated with poor neurological outcome. We explored the influence of haemolysis on the prognostic performance of NSE and NfL.

Methods and results

The study is based on post hoc analyses of a randomized, single-centre, double-blinded, controlled trial (IMICA), where comatose OHCA patients of presumed cardiac cause were included. Free-haemoglobin was measured at admission to quantify haemolysis. NSE and NfL were measured after 48 h to estimate the extent of brain injury. Montreal Cognitive Assessment score (MoCA) was assessed to evaluate neurocognitive impairments. Seventy-three patients were included and divided into two groups by the median free-haemoglobin at admission. No group differences in mortality or poor neurological outcome were observed. The high-admission free-haemoglobin group had a significantly higher concentration of NSE compared to the low-admission free-haemoglobin group (27.4 µmol/L vs. 19.6 µmol/L, P = 0.03), but no differences in NfL. The performance of NSE and NfL in predicting poor neurological outcome were high for both, but NfL was numerically higher [area under the ROC (AUROC) 0.90 vs. 0.96, P = 0.09]. Furthermore, NfL, but not NSE, was inversely correlated with MoCA score, R2 = 0.21, P = 0.006.

Conclusion

High free-haemoglobin at admission was associated with higher NSE concentration after 48 h, but, the performance of NSE and NfL in predicting poor neurological outcome among OHCA patients were good regardless of early haemolysis. Only elevated NfL concentrations were associated with cognitive impairments.

Towards modern post-coma care based on neuroscientific evidence

Background

Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients.

Method

This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years.

Results

Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics.

Conclusions

Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside.

Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest

BACKGROUND

Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors.

METHODS

A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format.

RESULTS

Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors.

CONCLUSIONS

These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.

Long-Term Outcomes of Patients with Stroke Predicted by Clinicians to have no Chance of Meaningful Recovery: A Japanese Cohort Study

BACKGROUND

Little is known about the natural history of comatose patients with brain injury, as in many countries most of these patients die in the context of withdrawal of life-sustaining therapies (WLSTs). The accuracy of predicting recovery that is used to guide goals-of-care decisions is uncertain. We examined long-term outcomes of patients with ischemic or hemorrhagic stroke predicted by experienced clinicians to have no chance of meaningful recovery in Japan, where WLST in patients with isolated neurological disease is uncommon.

METHODS

We retrospectively reviewed the medical records of all patients admitted with acute ischemic stroke, intracerebral hemorrhage, or nontraumatic subarachnoid hemorrhage between January 2018 and December 2020 to a neurocritical care unit at Toda Medical Group Asaka Medical Center in Saitama, Japan. We screened for patients who were predicted by the attending physician on postinjury day 1-4 to have no chance of meaningful recovery. Primary outcome measures were disposition at hospital discharge and the ability to follow commands and functional outcomes measured by the Glasgow Outcome Scale-Extended (GOS-E), which was assessed 6 months after injury.

RESULTS

From 860 screened patients, we identified 40 patients (14 with acute ischemic stroke, 19 with intracerebral hemorrhage, and 7 with subarachnoid hemorrhage) who were predicted to have no chance of meaningful recovery. Median age was 77 years (interquartile range 64-85), 53% (n = 21) were women, and 80% (n = 32) had no functional deficits prior to hospitalization. Six months after injury, 17 patients were dead, 14 lived in a long-term care hospital, 3 lived at home, 2 lived in a rehabilitation center, and 2 lived in a nursing home. Three patients reliably followed commands, two were in a vegetative state (GOS-E 2), four fully depended on others and required constant assistance (GOS-E 3), one could be left alone independently for 8 h per day but remained dependent (GOS-E 4), and one was independent and able to return to work-like activities (GOS-E 5).

CONCLUSIONS

In the absence of WLST, almost half of the patients predicted shortly after the injury to have no chance of meaningful recovery were dead 6 months after the injury. A small minority of patients had good functional recovery, highlighting the need for more accurate neurological prognostication.

Prognosis of consciousness disorders in the intensive care unit

Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.

Discrepancies in the late auditory potentials of post-anoxic patients: watch out for focal brain lesions, a pilot retrospective study

AIMS

Late auditory evoked potentials, and notably mismatch negativity (MMN) and P3 responses, can be used as part of the multimodal prognostic evaluation in post-anoxic disorders of consciousness (DOC). MMN response preferentially stems from the temporal cortex and the arcuate fasciculus. Situations with discrepant evaluations, for example MMN absent but P3 present, are frequent and difficult to interpret. We hypothesize that discrepant MMN-/P3+ results could reflect a higher prevalence of lesions in MMN generating regions. This study presents correlations between neurophysiological and neuroradiological results.

METHODS

This retrospective study was conducted on 38 post-anoxic DOC patients. Brain lesions were analyzed on 3T MRI both anatomically and through computation of the local arcuate fasciculus fractional anisotropy values on Diffusion Tensor Imaging sequences. Neurophysiological data and outcome were also analyzed.

RESULTS

Our cohort included 8 MMN-/P3+, 7 MMN+/P3+, 21 MMN-/P3- and 2 MMN-/P3+ patients, assessed at a median delay of 20.5 days since cardiac arrest. Our results show that MMN-/P3+ patients tended to have fewer temporal and basal ganglia lesions than MMN-/P3- patients, and more than MMN+/P3+ patients (p-values for trend: p=0.02 for temporal and p=0.02 for basal ganglia lesions). There was a statistical difference across groups for mean fractional anisotropy values in the arcuate fasciculus (p=0.008). The percentage of patients regaining consciousness at three months in MMN-/P3+ patients was higher than in MMN-/P3- patients and lower than in MMN+/P3+ patients.

CONCLUSION

This study suggests that discrepancies in late auditory evoked potentials may be linked to focal post-anoxic brain lesions, visible on brain MRI.

Association between the extent of diffusion restriction on brain diffusion-weighted imaging and neurological outcomes after an out-of-hospital cardiac arrest

BACKGROUND

This study evaluated the association between the extent of diffusion restriction on brain diffusion-weighted imaging (DWI) and neurological outcomes in patients who underwent targeted temperature management (TTM) after an out-of-hospital cardiac arrest (OHCA).

METHODS

Patients who underwent brain magnetic resonance imaging within 10 days of OHCA between 2012 and 2021 were analysed. The extent of diffusion restriction was described according to the modified DWI Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS). The 35 predefined brain regions were assigned a score if diffuse signal changes were concordantly present in DWI scans and apparent diffusion coefficient maps. The primary outcome was an unfavourable neurological outcome at 6 months. The sensitivity, specificity, and receiver operating characteristic (ROC) curves for the measured parameters were analysed. Cut-off values were determined to predict the primary outcome. The predictive cut-off DWI-ASPECTS was internally validated using five-fold cross-validation.

RESULTS

Of the 301 patients, 108 (35.9%) had 6-month favourable neurological outcomes. Patients with unfavourable outcomes had higher whole-brain DWI-ASPECTS (median, 31 [26-33] vs. 0 [0-1], P < 0.001) than those with favourable outcomes. The area under the ROC curve (AUROC) of whole-brain DWI-ASPECTS was 0.957 (95% confidence interval [CI] 0.928-0.977). A cut-off value of ≥8 for unfavourable neurological outcomes had specificity and sensitivity of 100% (95% CI 96.6-100) and 89.6% (95% CI 84.4-93.6), respectively. The mean AUROC was 0.956.

CONCLUSION

More extensive diffusion restriction on DWI-ASPECTS in patients with OHCA who underwent TTM was associated with 6-month unfavourable neurological outcomes. Running title: Diffusion restriction and neurological outcomes after cardiac arrest.

Editorial Comment: Free water corrected diffusion tensor imaging discriminates between good and poor outcome of comatose patients after cardiac arrest

KEY POINTS

• Prediction of neurological outcome after cardiac arrest is a major ethical challenge. • Diffusion tensor imaging (DTI) is a promising prognostic tool in comatose patients assessing brain microstructural damages. • The combination of clinical data with whole-brain fractional anisotropy and mean diffusivity values appears more accurate to predict poor outcome.

Development of PPARγ Agonists for the Treatment of Neuroinflammatory and Neurodegenerative Diseases: Leriglitazone as a Promising Candidate

Increasing evidence suggests that the peroxisome proliferator-activated receptor γ (PPARγ), a member of the nuclear receptor superfamily, plays an important role in physiological processes in the central nervous system (CNS) and is involved in cellular metabolism and repair. Cellular damage caused by acute brain injury and long-term neurodegenerative disorders is associated with alterations of these metabolic processes leading to mitochondrial dysfunction, oxidative stress, and neuroinflammation. PPARγ agonists have demonstrated the potential to be effective treatments for CNS diseases in preclinical models, but to date, most drugs have failed to show efficacy in clinical trials of neurodegenerative diseases including amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease. The most likely explanation for this lack of efficacy is the insufficient brain exposure of these PPARγ agonists. Leriglitazone is a novel, blood-brain barrier (BBB)-penetrant PPARγ agonist that is being developed to treat CNS diseases. Here, we review the main roles of PPARγ in physiology and pathophysiology in the CNS, describe the mechanism of action of PPARγ agonists, and discuss the evidence supporting the use of leriglitazone to treat CNS diseases.

Comparison of the prognostic value of early-phase proton magnetic resonance spectroscopy and diffusion tensor imaging with serum neuron-specific enolase at 72 h in comatose survivors of out-of-hospital cardiac arrest-a substudy of the XeHypotheca trial

PURPOSE

We compared the predictive accuracy of early-phase brain diffusion tensor imaging (DTI), proton magnetic resonance spectroscopy (1H-MRS), and serum neuron-specific enolase (NSE) against the motor score and epileptic seizures (ES) for poor neurological outcome after out-of-hospital cardiac arrest (OHCA).

METHODS

The predictive accuracy of DTI, 1H-MRS, and NSE along with motor score at 72 h and ES for the poor neurological outcome (modified Rankin Scale, mRS, 3 - 6) in 92 comatose OHCA patients at 6 months was assessed by area under the receiver operating characteristic curve (AUROC). Combined models of the variables were included as exploratory.

RESULTS

The predictive accuracy of fractional anisotropy (FA) of DTI (AUROC 0.73, 95% CI 0.62-0.84), total N-acetyl aspartate/total creatine (tNAA/tCr) of 1H-MRS (0.78 (0.68 - 0.88)), or NSE at 72 h (0.85 (0.76 - 0.93)) was not significantly better than motor score at 72 h (0.88 (95% CI 0.80-0.96)). The addition of FA and tNAA/tCr to a combination of NSE, motor score, and ES provided a small but statistically significant improvement in predictive accuracy (AUROC 0.92 (0.85-0.98) vs 0.98 (0.96-1.00), p = 0.037).

CONCLUSION

None of the variables (FA, tNAA/tCr, ES, NSE at 72 h, and motor score at 72 h) differed significantly in predicting poor outcomes in this patient group. Early-phase quantitative neuroimaging provided a statistically significant improvement for the predictive value when combined with ES and motor score with or without NSE. However, in clinical practice, the additional value is small, and considering the costs and challenges of imaging in this patient group, early-phase DTI/MRS cannot be recommended for routine use.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00879892, April 13, 2009.

Free water corrected diffusion tensor imaging discriminates between good and poor outcomes of comatose patients after cardiac arrest

OBJECTIVES

Approximately 50% of comatose patients after cardiac arrest never regain consciousness. Cerebral ischaemia may lead to cytotoxic and/or vasogenic oedema, which can be detected by diffusion tensor imaging (DTI). Here, we evaluate the potential value of free water corrected mean diffusivity (MD) and fractional anisotropy (FA) based on DTI, for the prediction of neurological recovery of comatose patients after cardiac arrest.

METHODS

A total of 50 patients after cardiac arrest were included in this prospective cohort study in two Dutch hospitals. DTI was obtained 2-4 days after cardiac arrest. Outcome was assessed at 6 months, dichotomised as poor (cerebral performance category 3-5; n = 20) or good (n = 30) neurological outcome. We calculated the whole brain mean MD and FA and compared between patients with good and poor outcomes. In addition, we compared a preliminary prediction model based on clinical parameters with or without the addition of MD and FA.

RESULTS

We found significant differences between patients with good and poor outcome of mean MD (good: 726 [702-740] × 10^-6 mm²/s vs. poor: 663 [575-736] × 10^-6 mm²/s; p = 0.01) and mean FA (0.30 ± 0.03 vs. 0.28 ± 0.03; p = 0.03). An exploratory prediction model combining clinical parameters, MD and FA increased the sensitivity for reliable prediction of poor outcome from 60 to 85%, compared to the model containing clinical parameters only, but confidence intervals are overlapping.

CONCLUSIONS

Free water-corrected MD and FA discriminate between patients with good and poor outcomes after cardiac arrest and hold the potential to add to multimodal outcome prediction.

KEY POINTS

• Whole brain mean MD and FA differ between patients with good and poor outcome after cardiac arrest. • Free water-corrected MD can better discriminate between patients with good and poor outcome than uncorrected MD. • A combination of free water-corrected MD (sensitive to grey matter abnormalities) and FA (sensitive to white matter abnormalities) holds potential to add to the prediction of outcome.

Limbic pathway vulnerability associates with neurologic outcome in children after cardiac arrest

AIM

To analyze whether brain connectivity sequences including diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rsfMRI) identify vulnerable brain regions and networks associated with neurologic outcome after pediatric cardiac arrest.

METHODS

Children aged 2 d-17 y with cardiac arrest were enrolled in one of 2 parent studies at a single center. Clinically indicated brain MRI with DTI and rsfMRI and performed within 2 weeks after arrest were analyzed. Tract-wise fractional anisotropy (FA) and axial, radial, and mean diffusivity assessed DTI, and functional connectivity strength (FCS) assessed rsfMRI between outcome groups. Unfavorable neurologic outcome was defined as Pediatric Cerebral Performance Category score 4-6 or change > 1 between 6 months after arrest vs baseline.

RESULTS

Among children with DTI (n = 28), 57% had unfavorable outcome. Mean, radial, axial diffusivity and FA of varying direction of magnitude in the limbic tracts, including the right cingulum parolfactory, left cingulum parahippocampal, corpus callosum forceps major, and corpus callosum forceps minor tracts, were associated with unfavorable neurologic outcome (p < 0.05). Among children with rsfMRI (n = 12), 67% had unfavorable outcome. Decreased FCS in the ventromedial and dorsolateral prefrontal cortex, insula, precentral gyrus, anterior cingulate, and inferior parietal lobule were correlated regionally with unfavorable neurologic outcome (p < 0.05 Family-Wise Error corrected).

CONCLUSION

Decreased multimodal connectivity measures of paralimbic tracts were associated with unfavorable neurologic outcome after pediatric cardiac arrest. Longitudinal analysis correlating brain connectivity sequences with long term neuropsychological outcomes to identify the impact of pediatric cardiac arrest in vulnerable brain networks over time appears warranted.

Prognostic value of global deep white matter DTI metrics for 1-year outcome prediction in ICU traumatic brain injury patients: an MRI-COMA and CENTER-TBI combined study

PURPOSE

A reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome.

METHODS

Two multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221). Patients were dichotomised depending on their 1-year Glasgow outcome scale extended (GOSE) score: GOSE 1-3, unfavorable outcome (UFO); GOSE 4-8, favorable outcome (FO). A support vector classifier incorporating fractional anisotropy and mean diffusivity measured in deep white matter, and age at the time of injury was developed to predict whether the patients would be either UFO or FO.

RESULTS

The model achieved an area under the ROC curve of 0.93 on MRI-COMA-Train training dataset, and 49% sensitivity for 96.8% specificity in predicting UFO and 58.5% sensitivity for 97.1% specificity in predicting FO on the pooled MRI-COMA-Test and CENTER-TBI validation datasets.

CONCLUSION

The model successfully identified, with a specificity compatible with a personalized decision-making process in ICU, one in two patients who had an unfavorable outcome at 1 year after the injury, and two-thirds of the patients who experienced a favorable outcome.

Cerebral ischemia in the developing brain

Brain ischemia affects all ages, from neonates to the elderly population, and is a leading cause of mortality and morbidity. Multiple preclinical rodent models involving different ages have been developed to investigate the effect of ischemia during different times of key brain maturation events. Traditional models of developmental brain ischemia have focused on rodents at postnatal day 7-10, though emerging models in juvenile rodents (postnatal days 17-25) indicate that there may be fundamental differences in neuronal injury and functional outcomes following focal or global cerebral ischemia at different developmental ages, as well as in adults. Here, we consider the timing of injury in terms of excitation/inhibition balance, oxidative stress, inflammatory responses, blood brain barrier integrity, and white matter injury. Finally, we review translational strategies to improve function after ischemic brain injury, including new ideas regarding neurorestoration, or neural repair strategies that restore plasticity, at delayed time points after ischemia.

Description and Outcome of Severe Hypoglycemic Encephalopathy in the Intensive Care Unit

BACKGROUND

Disorders of consciousness due to severe hypoglycemia are rare but challenging to treat. The aim of this retrospective cohort study was to describe our multimodal neurological assessment of patients with hypoglycemic encephalopathy hospitalized in the intensive care unit and their neurological outcomes.

METHODS

Consecutive patients with disorders of consciousness related to hypoglycemia admitted for neuroprognostication from 2010 to 2020 were included. Multimodal neurological assessment included electroencephalography, somatosensory and cognitive event-related potentials, and morphological and quantitative magnetic resonance imaging (MRI) with quantification of fractional anisotropy. Neurological outcomes at 28 days, 3 months, 6 months, 1 year, and 2 years after hypoglycemia were retrieved.

RESULTS

Twenty patients were included. After 2 years, 75% of patients had died, 5% remained in a permanent vegetative state, 10% were in a minimally conscious state, and 10% were conscious but with severe disabilities (Glasgow Outcome Scale-Extended scores 3 and 4). All patients showed pathologic electroencephalography findings with heterogenous patterns. Morphological brain MRI revealed abnormalities in 95% of patients, with various localizations including cortical atrophy in 65% of patients. When performed, quantitative MRI showed decreased fractional anisotropy affecting widespread white matter tracts in all patients.

CONCLUSIONS

The overall prognosis of patients with severe hypoglycemic encephalopathy was poor, with only a small fraction of patients who slowly improved after intensive care unit discharge. Of note, patients who did not improve during the first 6 months did not recover consciousness. This study suggests that a multimodal approach capitalizing on advanced brain imaging and bedside electrophysiology techniques could improve diagnostic and prognostic performance in severe hypoglycemic encephalopathy.

Use of Magnetic Resonance Imaging in Neuroprognostication After Pediatric Cardiac Arrest: Survey of Current Practices

BACKGROUND

Use of magnetic resonance imaging (MRI) as a tool to aid in neuroprognostication after cardiac arrest (CA) has been described, yet details of specific indications, timing, and sequences are unknown. We aim to define the current practices in use of brain MRI in prognostication after pediatric CA.

METHODS

A survey was distributed to pediatric institutions participating in three international studies. Survey questions related to center demographics, clinical practice patterns of MRI after CA, neuroimaging resources, and details regarding MRI decision support.

RESULTS

Response rate was 31% (44 of 143). Thirty-four percent (15 of 44) of centers have a clinical pathway informing the use of MRI after CA. Fifty percent (22 of 44) of respondents reported that an MRI is obtained in nearly all patients with CA, and 32% (14 of 44) obtain an MRI in those who do not return to baseline neurological status. Poor neurological examination was reported as the most common factor (91% [40 of 44]) determining the timing of the MRI. Conventional sequences (T1, T2, fluid-attenuated inversion recovery, and diffusion-weighted imaging/apparent diffusion coefficient) are routinely used at greater than 97% of centers. Use of advanced imaging techniques (magnetic resonance spectroscopy, diffusion tensor imaging, and functional MRI) were reported by less than half of centers.

CONCLUSIONS

Conventional brain MRI is a common practice for prognostication after CA. Advanced imaging techniques are used infrequently. The lack of standardized clinical pathways and variability in reported practices support a need for higher-quality evidence regarding the indications, timing, and acquisition protocols of clinical MRI studies.

Association of Blood-Based Brain Injury Biomarker Concentrations With Outcomes After Pediatric Cardiac Arrest

IMPORTANCE

Families and clinicians have limited validated tools available to assist in estimating long-term outcomes early after pediatric cardiac arrest. Blood-based brain-specific biomarkers may be helpful tools to aid in outcome assessment.

OBJECTIVE

To analyze the association of blood-based brain injury biomarker concentrations with outcomes 1 year after pediatric cardiac arrest.

DESIGN, SETTING, AND PARTICIPANTS

The Personalizing Outcomes After Child Cardiac Arrest multicenter prospective cohort study was conducted in pediatric intensive care units at 14 academic referral centers in the US between May 16, 2017, and August 19, 2020, with the primary investigators blinded to 1-year outcomes. The study included 120 children aged 48 hours to 17 years who were resuscitated after cardiac arrest, had pre-cardiac arrest Pediatric Cerebral Performance Category scores of 1 to 3 points, and were admitted to an intensive care unit after cardiac arrest.

EXPOSURE

Cardiac arrest.

MAIN OUTCOMES AND MEASURES

The primary outcome was an unfavorable outcome (death or survival with a Vineland Adaptive Behavior Scales, third edition, score of <70 points) at 1 year after cardiac arrest. Glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal esterase L1 (UCH-L1), neurofilament light (NfL), and tau concentrations were measured in blood samples from days 1 to 3 after cardiac arrest. Multivariate logistic regression and area under the receiver operating characteristic curve (AUROC) analyses were performed to examine the association of each biomarker with outcomes on days 1 to 3.

RESULTS

Among 120 children with primary outcome data available, the median (IQR) age was 1.0 (0-8.5) year; 71 children (59.2%) were male. A total of 5 children (4.2%) were Asian, 19 (15.8%) were Black, 81 (67.5%) were White, and 15 (12.5%) were of unknown race; among 110 children with data on ethnicity, 11 (10.0%) were Hispanic, and 99 (90.0%) were non-Hispanic. Overall, 70 children (58.3%) had a favorable outcome, and 50 children (41.7%) had an unfavorable outcome, including 43 deaths. On days 1 to 3 after cardiac arrest, concentrations of all 4 measured biomarkers were higher in children with an unfavorable vs a favorable outcome at 1 year. After covariate adjustment, NfL concentrations on day 1 (adjusted odds ratio [aOR], 5.91; 95% CI, 1.82-19.19), day 2 (aOR, 11.88; 95% CI, 3.82-36.92), and day 3 (aOR, 10.22; 95% CI, 3.14-33.33); UCH-L1 concentrations on day 2 (aOR, 11.27; 95% CI, 3.00-42.36) and day 3 (aOR, 7.56; 95% CI, 2.11-27.09); GFAP concentrations on day 2 (aOR, 2.31; 95% CI, 1.19-4.48) and day 3 (aOR, 2.19; 95% CI, 1.19-4.03); and tau concentrations on day 1 (aOR, 2.44; 95% CI, 1.14-5.25), day 2 (aOR, 2.28; 95% CI, 1.31-3.97), and day 3 (aOR, 2.04; 95% CI, 1.16-3.57) were associated with an unfavorable outcome. The AUROC models were significantly higher with vs without the addition of NfL on day 2 (AUROC, 0.932 [95% CI, 0.877-0.987] vs 0.871 [95% CI, 0.793-0.949]; P = .02) and day 3 (AUROC, 0.921 [95% CI, 0.857-0.986] vs 0.870 [95% CI, 0.786-0.953]; P = .03).

CONCLUSIONS AND RELEVANCE

In this cohort study, blood-based brain injury biomarkers, especially NfL, were associated with an unfavorable outcome at 1 year after pediatric cardiac arrest. Additional evaluation of the accuracy of the association between biomarkers and neurodevelopmental outcomes beyond 1 year is needed.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks

Background

There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma.

Methods

This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both “functional data” from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and “structural data” (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants.

Results

The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94–0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes.

Conclusions

A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data.

Clinical Trial Numberhttp://ClinicalTrials.gov (No. NCT03482115).

Supplementary Information

The online version contains supplementary material available at 10.1007/s12028-022-01525-z.

Applications of Advanced MRI to Disorders of Consciousness

Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.

Neurologic Prognostication After Cardiac Arrest Using Brain Biomarkers: A Systematic Review and Meta-analysis

IMPORTANCE

Brain injury biomarkers released into circulation from the injured neurovascular unit are important prognostic tools in patients with cardiac arrest who develop hypoxic ischemic brain injury (HIBI) after return of spontaneous circulation (ROSC).

OBJECTIVE

To assess the neuroprognostic utility of bloodborne brain injury biomarkers in patients with cardiac arrest with HIBI.

DATA SOURCES

Studies in electronic databases from inception to September 15, 2021. These databases included MEDLINE, Embase, Evidence-Based Medicine Reviews, CINAHL, Cochrane Database of Systematic Reviews, and the World Health Organization Global Health Library.

STUDY SELECTION

Articles included in this systmatic review and meta-analysis were independently assessed by 2 reviewers. We included studies that investigated neuron-specific enolase, S100 calcium-binding protein β, glial fibrillary acidic protein, neurofilament light, tau, or ubiquitin carboxyl hydrolase L1 in patients with cardiac arrest aged 18 years and older for neurologic prognostication. We excluded studies that did not (1) dichotomize neurologic outcome as favorable vs unfavorable, (2) specify the timing of blood sampling or outcome determination, or (3) report diagnostic test accuracy or biomarker concentration.

DATA EXTRACTION AND SYNTHESIS

Data on the study design, inclusion and exclusion criteria, brain biomarkers levels, diagnostic test accuracy, and neurologic outcome were recorded. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

MAIN OUTCOMES AND MEASURES

Summary receiver operating characteristic curve analysis was used to calculate the area under the curve, sensitivity, specificity, and optimal thresholds for each biomarker. Risk of bias and concerns of applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

We identified 2953 studies, of which 86 studies with 10 567 patients (7777 men [73.6] and 2790 women [26.4]; pooled mean [SD] age, 62.8 [10.2] years) were included. Biomarker analysis at 48 hours after ROSC demonstrated that neurofilament light had the highest predictive value for unfavorable neurologic outcome, with an area under the curve of 0.92 (95% CI, 0.84-0.97). Subgroup analyses of patients treated with targeted temperature management and those who specifically had an out-of-hospital cardiac arrest showed similar results (targeted temperature management, 0.92 [95% CI, 0.86-0.95] and out-of-hospital cardiac arrest, 0.93 [95% CI, 0.86-0.97]).

CONCLUSIONS AND RELEVANCE

Neurofilament light, which reflects white matter damage and axonal injury, yielded the highest accuracy in predicting neurologic outcome in patients with HIBI at 48 hours after ROSC.

TRIAL REGISTRATION

PROSPERO Identifier: CRD42020157366.

Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest

BACKGROUND AND OBJECTIVES

To correlate brain metabolites with clinical outcome using magnetic resonance spectroscopy (MRS) in patients undergoing targeted temperature management (TTM) after cardiac arrest and assess their relationships to MRI and EEG variables.

METHODS

A prospective cohort of 50 patients was studied. The primary outcome was coma recovery to follow commands. Comparison of MRS measures in the posterior cingulate gyrus, parietal white matter, basal ganglia, and brainstem were also made to 25 normative controls.

RESULTS

Fourteen of 50 patients achieved coma recovery before hospital discharge. There was a significant decrease in total N-acetylaspartate (NAA/Cr) and an increase in lactate/creatine (Lac/Cr) in patients who did not recover, with changes most prominent in the posterior cingulate gyrus. Patients who recovered had decrease in NAA/Cr as compared to controls. NAA/Cr had a strong monotonic relationship with MRI cortical apparent diffusion coefficient (ADC); Lac level exponentially increased with decreasing ADC. EEG suppression/burst suppression was strongly associated with Lac elevation.

DISCUSSION

NAA and Lac changes are associated with clinical/MRI/EEG changes consistent with hypoxic-ischemic encephalopathy (HIE) and are most prominent in the posterior cingulate gyrus. NAA/Cr decrease observed in patients with good outcomes suggests mild HIE in patients asymptomatic at hospital discharge. The appearance of cortical Lac represents a deterioration of aerobic energy metabolism and is associated with EEG background suppression, synaptic transmission failure, and severe, potentially irreversible HIE.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that in patients undergoing TTM after cardiac arrest, brain MRS-determined decrease in total NAA/Cr and an increase in Lac/Cr are associated with an increased risk of not recovering.

Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates

BACKGROUND AND OBJECTIVES

Disorders of consciousness, EEG background suppression, and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures.

METHODS

We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI after cardiac arrest (n = 204). We classified each patient according to recovery of consciousness (command following) before discharge, the most continuous EEG background (burst suppression vs continuous), and the presence or absence of seizures. Anoxic brain injury was measured with the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to controls without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Last, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures.

RESULTS

Compared to controls, patients with cardiac arrest demonstrated ADC signal reduction that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter.

DISCUSSION

Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.

Mapping the Unconscious Brain: Insights From Advanced Neuroimaging

SUMMARY

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.

Toward a coherent structuration of disorders of consciousness expertise at a country scale: A proposal for France

Probing consciousness and cognitive abilities in non-communicating patients is one of the most challenging diagnostic issues. A fast growing medical and scientific literature explores the various facets of this challenge, often coined under the generic expression of 'Disorders of Consciousness' (DoC). Crucially, a set of independent converging results demonstrated both (1) the diagnostic and prognostic importance of this expertise, and (2) the need to combine behavioural measures with brain structure and activity data to improve diagnostic and prognostication accuracy as well as potential therapeutic intervention. Thus, probing consciousness in DoC patients appears as a crucial activity rich of human, medical, economic and ethical consequences, but this activity needs to be organized in order to offer this expertise to each concerned patient. More precisely, diagnosis of consciousness differs in difficulty across patients: while a minimal set of data can be sufficient to reach a confident result, some patients need a higher level of expertise that relies on additional behavioural and brain activity and brain structure measures. In order to enable this service on a systematic mode, we present two complementary proposals in the present article. First, we sketch a structuration of DoC expertise at a country-scale, namely France. More precisely, we suggest that a 2-tiers network composed of local (Tier-1) and regional (Tier-2) centers backed by distant electronic databases and algorithmic centers could optimally enable the systematic implementation of DoC expertise in France. Second, we propose to create a national common register of DoC patients in order to better monitor this activity, to improve its performance on the basis of nation-wide collected evidence, and to promote rational decision-making.

Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.

Disorders of Consciousness Associated With COVID-19: A Prospective, Multimodal Study of Recovery and Brain Connectivity

BACKGROUND AND OBJECTIVES

In patients with severe coronavirus disease 2019 (COVID-19), disorders of consciousness (COVID-DoC) have emerged as a serious complication. The prognosis and pathophysiology of COVID-DoC remain unclear, complicating decisions about continuing life-sustaining treatment. We describe the natural history of COVID-DoC and investigate its associated brain connectivity profile.

METHODS

In a prospective, longitudinal study, we screened consecutive patients with COVID-19 at our institution. We enrolled critically ill adult patients with a DoC unexplained by sedation or structural brain injury, and who were planned to undergo a brain MRI. We performed resting state functional MRI and diffusion MRI to evaluate functional and structural connectivity, as compared to healthy controls and patients with DoC resulting from severe traumatic brain injury (TBI). We assessed the recovery of consciousness (command-following) and functional outcomes (Glasgow Outcome Scale Extended [GOSE] and the Disability Rating Scale [DRS]) at hospital discharge, three months post-discharge, and six months post-discharge. We also explored whether clinical variables were associated with recovery from COVID-DoC.

RESULTS

After screening 1,105 patients with COVID-19, we enrolled twelve with COVID-DoC. The median age was 63.5 years [interquartile range 55-76.3]. Excluding one who died shortly after enrollment, all of the remaining eleven patients recovered consciousness, after 0-25 days (median 7 [5-14.5]) following the cessation of continuous intravenous sedation. At discharge, all surviving patients remained dependent - median GOSE 3 [1-3], median DRS 23 [16-30]. However ultimately, except for two patients with severe polyneuropathy, all returned home with normal cognition and minimal disability - at three months, median GOSE 3 [3-3], median DRS 7 [5-13]; at six months, median GOSE 4 [4-5], median DRS 3 [3-5]. Ten patients with COVID-DoC underwent advanced neuroimaging; functional and structural brain connectivity in COVID-DoC was diminished compared to healthy controls, and structural connectivity was comparable to patients with severe TBI.

DISCUSSION

Patients who survived invariably recovered consciousness after COVID-DoC. Though disability was common following hospitalization, functional status improved over the ensuing months. While future research is necessary, these prospective findings inform the prognosis and pathophysiology of COVID-DoC.

TRIAL REGISTRATION INFORMATION

Clinicaltrials.gov, NCT04476589, submitted 7/2020, first enrolled 7/20/2020, https://clinicaltrials.gov/ct2/show/NCT04476589.

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness

To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.

Long-term follow-up of neurodegenerative phenomenon in severe traumatic brain injury using MRI

BACKGROUND

Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodeling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute "tertiary" lesions related to the neurological outcome.

OBJECTIVE

The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI.

METHODS

Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the "favourable" recovery group (Glasgow Outcome Scale Extended [GOSE] 6-8) and "unfavourable" recovery group (GOSE 3-5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome.

RESULTS

TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p <0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p <0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3-5 than 6-8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81-0.83]) Conclusions. We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT00577954. Registered on October 2006.