An enhanced cerebral recovery index for coma prognostication following cardiac arrest

Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27

Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64-100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (pvalue ≤ 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia

Clinical biomarkers in brain injury: a lesson from cardiac arrest.

Cardiac arrest (CA) is the primary cause of death in industrialized countries. Successful resuscitation rate is estimated of about 40%, but a good neurological outcome remains difficult to achieve. The majority of resuscitated victims suffers of a pathophysiological entity termed as "post resuscitation disease". Today's efforts are mainly pointed to the chain of survival, often devoting less attention to post-resuscitation care. Resuscitated patients are often victims of nihilistic therapeutic approach, with clinicians failing to promptly institute strategies that mitigate the ischemia-reperfusion injury to vital organs. Only after 72 hours prognostication can be realistically attempted. Neurological evaluation relies on a combination of clinical, instrumental and laboratoristic parameters, since no one alone holds a specificity of 100%. Biochemical markers, such as neuron specific enolase and S-100b, may contribute to predict prognosis after CA. To the contrary, when used individually the necessary precision remains poorly characterized. Biochemical studies suffer from substantial methodological differences hampering attempts to summarize their findings. We review the information available on biochemical markers of brain damage for neurological prognostication after CA

Cardiac arrest – prognostic biomarkers and aspects of shock

Background: Some improvement has been seen in survival after cardiac arrest but the outcome is still poor and 50-70% of patients do not survive despite successful return of spontaneous circulation (ROSC). The cause of death is multifactorial. The majority of patients die from brain injury, but up to 35% die as a result of circulatory failure. Purpose: First, to investigate the release profiles of an array of biomarkers in patients treated with mild induced hypothermia after cardiac arrest and study their correlation to the post-cardiac arrest syndrome (PCAS) and long-term outcome; Second, to investigate the effect of two different target temperatures (33°C and 36°C) on hemodynamics and vasopressor requirement in cardiac arrest patients and; Third, to investigate the association of target temperature with outcome in patients with shock in admission. Methods: The biomarkers were collected serially at 8 time points during the first 72 hours following cardiac arrest in 84 still comatose post-resuscitation cardiac arrest patients treated with mild induced hypothermia. We analysed markers of inflammation; procalcitonin (PCT) and c-reactive protein (CRP), oxidation; peroxiredoxin 4 (prx4), cardiac stress; MR-proANP, cardiac injury; Troponin T (TnT), brain injury; Neuron specific enlolase (NSE), and the stress hormone; CT-proAVP (copeptin). Outcome was assessed at 6 months with the cerebral performance category scale (CPC) where CPC 1-2 was considered a good outcome. The cardiovascular sequential organ failure assessment score (SOFA-score) and the time to ROSC were used as surrogate markers for the PCAS. Three different definitions of infection were used to assess occurrence of infection. The effect of a target temperature of 33°C or 36°C on hemodynamics was investigated in all patients with available vasopressor data (n=920) in the ‘Targeted temperature management at 33°C versus 36°C after cardiac arrest’ trial and in patients with shock on admission (n=139). Primary outcome was mortality. Secondary outcomes were vasopressor requirements as assessed by the cardiovascular SOFA-score, serum lactate concentrations, mean arterial pressure, and heart rate. Results: PCT, CT-proAVP and MR-proANP were all significantly higher in patients with poor outcome and correlated to surrogate markers of the PCAS. No specific cut-off levels were identified. PCT release was not associated to infection. Combinations of biomarkers may be a promising concept to improve prognostication. A targeted temperature of 33°C was associated with increased vasopressor requirements and increased lactate levels in both our investigated cohorts. A low MAP during the intervention (0-36 hours) was associated with poor outcome after adjustment for baseline characteristics. Conclusion: Biomarkers from other sources than the brain are associated to the PCAS and may be promising biomarkers to prognosticate outcome, alone or in combination. Targeted temperature management at 33°C is associated with increased vasopressor requirements and severity of shock and does not improve outcome as compared to 36°C

A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction

Patients resuscitated from cardiac arrest (CA) face a high risk of neurological disability and death, however pragmatic methods are lacking for accurate and reliable prognostication. The aim of this study was to build computational models to predict post-CA outcome by leveraging high-dimensional patient data available early after admission to the intensive care unit (ICU). We hypothesized that model performance could be enhanced by integrating physiological time series (PTS) data and by training machine learning (ML) classifiers. We compared three models integrating features extracted from the electronic health records (EHR) alone, features derived from PTS collected in the first 24hrs after ICU admission (PTS24), and models integrating PTS24 and EHR. Outcomes of interest were survival and neurological outcome at ICU discharge. Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0.85, 0.80 and 0.68 respectively) and neurological outcome (0.87, 0.83 and 0.78). The best ML classifier achieved higher discrimination than the reference logistic regression model (APACHE III) for survival (AUC 0.85 vs 0.70) and neurological outcome prediction (AUC 0.87 vs 0.75). Feature analysis revealed previously unknown factors to be associated with post-CA recovery. Results attest to the effectiveness of ML models for post-CA predictive modeling and suggest that PTS recorded in very early phase after resuscitation encode short-term outcome probabilities.Comment: 51 pages, 7 figures, 4 supplementary figure

Essential Noninvasive Multimodality Neuromonitoring for the Critically Ill Patient

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from

Cardiac Arrhythmias

Cardiac arrhythmias are common triggers of emergency admission to cardiology or high-dependency departments. Most cases are easy to diagnose and treat, while others may present a challenge to healthcare professionals. A translational approach to arrhythmias links molecular and cellular scientific research with clinical diagnostics and therapeutic methods, which may include both pharmacological and non-pharmacologic treatments. This book presents a comprehensive overview of specific cardiac arrhythmias and discusses translational approaches to their diagnosis and treatment

PRESERVED NEURAL FUNCTIONS IN POST-ANOXIC COMA AND THEIR ADDED VALUE FOR THE PREDICTION OF COGNITIVE AND FUNCTIONAL OUTCOME

Post-anoxic coma after cardiac arrest poses critical challenges to clinicians and families regarding the severity of cerebral insult and the potential of recovery. In such an altered state of consciousness, electroencephalography (EEG), assessing spontaneous and evoked cerebral activity without patient's active participation, provides crucial information about the brain condition. In particular, previous studies showed preserved neural responses in simple and more complex paradigms involving mainly auditory stimuli in some post-anoxic comatose patients, and in particular in those who will survive. These results suggest that the investigation of brain functions during coma can be used to predict patient's prognosis and that consciousness may be not required for low-level processing of sensory stimuli. However, to which extent the unconscious brain can process complex stimuli, and how brain responses during coma are associated with other clinical markers of cerebral condition and with detailed functional outcome of the survivors remains under-investigated. In the present thesis, I address these points through three experimental studies. In the first one, I assessed the clinical evolution of patients showing an initial non-reactive EEG, defined as the absence of any visible change in the EEG signal in response to strong nociceptive or auditory stimulation. Despite an extremely poor outcome, patients recovering EEG reactivity on the second day of coma were more likely than the others to also regain other brain functions, suggesting at least partial clinical recovery in these patients. In the second study, I investigated whether the progression of cerebral responses to auditory stimuli during coma could be predictive of the functional recovery of the survivors. My results indicate that patients improving auditory discrimination display better cognitive performances and better long-term outcome as compared with the others. In addition, the extent of the progression was directly associated with the extent of the recovery, suggesting a close relation between brain's condition during early coma and subsequent functional recovery. Finally, in the third study, I challenged current opinions regarding the impossibility of learning in the absence of consciousness, by showing that some deeply comatose patients exhibited neural signs of expectancy of a specific stimulus. These patients presented a reactivation of the EEG activity elicited by a sound, precisely when the sound was expected after a tone, indicating the creation of an association between the tone and the following sound. In summary, my results suggest that the comatose brain can process a large variety of stimuli, shedding new light into the cerebral functions in this clinical state, and that its signal may provide reliable and detailed prognostic information. -- Le coma après arrêt cardio-respiratoire soulève des questions essentielles pour les proches et l'équipe médicale concernant la sévérité de l'atteinte cérébrale et le potentiel de récupération. Dans ce contexte, l'électroencéphalographie (EEG), évaluant l'activité cérébrale spontanée et évoquée sans la participation du patient, fournit des informations cruciales concernant l'état du cerveau. Certains patients comateux, et en particulier ceux qui survivent, montrent une activité neuronale préservée en réponse à des paradigmes simples et plus complexes impliquant des stimuli principalement auditifs. L'investigation des fonctions cérébrales durant le coma peut donc être utile pour prédire le pronostic et la conscience ne semble pas nécessaire pour le traitement de certaines tâches simples. Cependant, il est encore inconnu à quel point le cerveau inconscient peut traiter des stimuli complexes, et comment les réponses cérébrales pendant le coma sont associées avec d'autres marqueurs cliniques de l'état cérébral et avec le devenir fonctionnel (outcome) des survivants. Ce sont ces questions que je vais traiter à travers trois études expérimentales. Dans la première, j'ai examiné l'évolution clinique des patients démontrant initialement un EEG non-réactif, définit comme l'absence de changement visible dans le signal EEG en réponse à de fortes stimulations nociceptives ou auditives. En dépit d'un outcome extrêmement sombre, les patients qui récupèrent la réactivité EEG au deuxième jour du coma ont plus de chance que les autres de récupérer également d'autres fonctions cérébrales, suggérant un rétablissement clinique au moins partiel chez ces patients. Dans la deuxième étude, j'ai investigué si la progression des réponses cérébrales à des stimuli auditifs pendant le coma pouvait prédire la récupération fonctionnelle des survivants. Mes résultats indiquent que les patients qui améliorent la discrimination auditive montrent de meilleures performances cognitives et un meilleur outcome à long-terme par rapport aux autres. De plus, l'étendue de la progression est directement associée à l'étendue de la récupération fonctionnelle, suggérant une forte relation entre l'état du cerveau dans le coma précoce et l'outcome. Finalement, au cours de la troisième étude, j'ai remis en question l'opinion selon laquelle il serait impossible d'apprendre de nouvelles informations en l'absence de conscience. J'ai montré que certains patients, alors qu'ils étaient profondément comateux, présentaient une réactivation de l'activité EEG en réponse à un son, précisément au moment où le son était attendu après un bip, indiquant la création d'une association entre le bip et le son suivant. En résumé, mes résultats suggèrent que le cerveau comateux peux traiter une large variété de stimuli, amenant de nouvelles perspectives de cet état clinique, et que son signal peut fournir des informations fiables et détaillées pour le pronostic

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions