Neurological prognostication of outcome in patients in coma after cardiac arrest

Prognostic Factors of Hospital Mortality After Near Hanging: A Retrospective two-Center French Study

Introductionsuicide is a global public health issue, with over 800 000 people taking their own lives every year. However, most suicide attempts do not result in death. Hanging is the most common method used in France, often leading to post-hanging coma (PHC). The prognosis for patients admitted in intensive care unit (ICU) following PHC is poor, yet predictive criteria of mortality have been poorly evaluated.Methodswe retrospectively collected prehospital and in-hospital data from 65 patients hospitalized in 2 French ICU for PHC, between first March 2010 and first August 2023, and compared characteristics between patients alive and dead.Resultshospital mortality was 52%. Among baseline characteristics, SAPSII and pre-hospital cardiac arrest were associated with mortality, respectively 47 versus 62 (P = .005) and 32% versus 85% (P = .001). Concerning neuroprognostication, abnormal pupillary light reflex (PLR) was more frequent in patients who died (14% vs 56%, P = .002), as abnormal EEG (0% vs 32%, P = .002) and abnormal transcranial doppler (10% vs 35%, P = .031).Conclusionwe identified several poor prognostic factors associated with hospital mortality after PHC. Further larger-scale studies are needed to supplement these findings.

Duration of resuscitation, regain of consciousness and histopathological severity of hypoxic-ischemic encephalopathy after cardiac arrest

Purpose

To study the histopathologically quantified severity of hypoxic-ischemic encephalopathy (HIE) in deceased cardiac arrest unbiased by death causes and correlated with demographic parameters.

Methods

We conducted a retrospective, single-centre study including cardiac arrest patients with postmortem brain autopsies. Using the selective eosinophilic neuronal death (SEND), the histopathological severity of HIE was quantified in the cerebral neocortex, hippocampus, basal ganglia, cerebellum, and brainstem, and correlated with demographic parameters.

Results

We included 319 patients with a median time of return from cardiac arrest to spontaneous circulation (tROSC) of 10 min, of whom 62(19.4%) had a regain of consciousness (RoC) before death. The tROSC was significantly correlated with the SEND in all brain regions (p < 0.05, Spearman's rho = 0.14 to 0.29). The SEND in the neocortex, hippocampus, and basal ganglia was significantly correlated with RoC (p < 0.05, Spearman's rho = -0.25 to -0.11). In 9 patients with tROSCs less than 1 min, all had a brainstem SEND less than 30%, and 8(88.9%) had neocortical SEND less than 30%. Among 69 patients with tROSCs greater than 20 min, 47.8-82.6% showed a SEND less than 30% across brain regions.

Conclusions

We found less SEND and RoC was more likely in patients with shorter tROSCs. A tROSC less than 1 min was mostly associated with SEND less than 30% in all brain regions. Prolonged resuscitations with tROSCs greater than 20 min did not exclude a SEND less than 30% in a relevant proportion of patients. Future histopathological studies are warranted to investigate the impact of modifiable clinical parameters on the severity of HIE.

Deep Brain Stimulation for Post-Hypoxic Myoclonus: A Case Correlating Local Field Potentials to Clinical Outcome

Background

Post-hypoxic myoclonus (PHM) is characterized by generalized myoclonus after hypoxic brain injury. PHM is often functionally impairing and refractory to medical therapies. There are a handful of reports utilizing deep brain stimulation (DBS) to treat medically refractory PHM.

Case Report

A 56-year-old woman developed PHM following an anoxic brain injury. Utilizing a stimulating and sensing DBS system, we show clinical improvement in myoclonus at 6 months and correlate it to local field potential (LFP) activity.

Discussion

We present the first case to utilize DBS sensing to correlate LFP activity to myoclonus improvement. Our case contributes to the growing evidence of DBS for PHM.

Histopathological patterns of hypoxic-ischemic encephalopathy after cardiac arrest: A retrospective brain autopsy study of 319 patients

PURPOSE

Understanding the pathophysiology of hypoxic-ischemic encephalopathy (HIE) provides important knowledge for the interpretation of neuroprognostic investigations after cardiac arrest (CA). One important aspect are the patterns of regional severity of selective neuronal death within the brain.

METHODS

In a monocentric, retrospective study, we included CA patients with initially successful resuscitation, who had received brain autopsies after death. We quantified selective eosinophilic neuronal death (SEND) in cerebral neocortex, hippocampus, basal ganglia, cerebellum, and brainstem. Using a previously established classification, we dichotomized HIE severity in SEND 0-1 (<30%, reflecting no or mild HIE) versus SEND 2-4 (≥30%, reflecting moderate to severe HIE). We analyzed histopathological HIE patterns and analyzed inter-regional and inter-neocortical correlation of SEND.

RESULTS

Of 319 patients, the mean SEND was 1.8 in hippocampus, 1.4 in neocortex, and 0.9 in brainstem. Typical histopathological HIE patterns were: (I) No or mild SEND in all brain regions, (II) predominant SEND in hippocampus with no or mild SEND in other brain regions, (III) severe SEND in neocortex, but not in brainstem, and (IV) severe SEND in the brainstem with neocortical HIE. In 7(9.7%) of 72 patients with histopathology from two different neocortical regions, the SEND differed by more than 30%. Among 154 patients with a SEND greater than 30% in at least one brain region, 14(9.1%) had predominant SEND in cerebellum, and 4(2.6%) predominant SEND in basal ganglia.

CONCLUSIONS

CA causes typical histopathological HIE patterns, with the hippocampus being more susceptible to SEND, than the cerebral neocortex, and the brainstem being the most resistant brain region. The neocortical distribution of SEND is mostly homogeneous; however, a relevant subgroup shows substantial neocortical HIE heterogeneity. Further studies are required to provide a more granular histopathological analysis of infrequent HIE patterns and their implications for neuroprognostication.

Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging

BACKGROUND

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports.

METHODS

The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions.

RESULTS

Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70).

CONCLUSIONS

Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

Temporal dynamics of neural synchrony and complexity of auditory EEG responses in post-hypoxic ischemic coma

The capacity to integrate information across brain regions and sufficient diversity of neural activity is necessary for consciousness. In patients in a post-hypoxic ischemic coma, the integrity of the auditory processing network is indicative of chances of regaining consciousness. However, our understanding of how measures of integration and differentiation of auditory responses manifest across time of coma is limited. We investigated the temporal evolution of neural synchrony of auditory-evoked electroencephalographic (EEG) responses, measured via their phase-locking value (PLV), and of their neural complexity in unconscious post-hypoxic ischemic comatose patients. Our results show that the PLV was predictive of chances to regain consciousness within the first 40 h post-cardiac arrest, while its predictive value diminished over subsequent time after coma onset. This was due to changing trajectories of PLV over time of coma for non-survivors, while survivors had stable PLV. The complexity of EEG responses was not different between patients who regained consciousness and those who did not, but it significantly diminished over time of coma, irrespective of the patient's outcome. Our findings provide novel insights on the optimal temporal window for assessing auditory functions in post-hypoxic ischemic coma. They are of particular importance for guiding the implementation of quantitative techniques for prognostication and contribute to an evolving understanding of neural functions within the acute comatose state.

System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method

BACKGROUND

A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods.

METHODS

The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).

RESULTS

Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).

CONCLUSIONS

The NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.

Brain Injury after Cardiac Arrest: Refining Prognosis

This study critically reviews prognostication, brings into focus its "refinement" over the decades, and provides a template for clinicians who must judge the functioning of patients who awaken. This includes the use of diagnostic tests, including neuroimaging, electrophysiology, and laboratory testing that may aid in evaluating neurologic recovery. The article reviews recent guidelines and provides advice informed by many years of clinical experience.

Development and validation of a prediction model of hospital mortality for patients with cardiac arrest survived 24 hours after cardiopulmonary resuscitation

Objective

Research on predictive models for hospital mortality in patients who have survived 24 h following cardiopulmonary resuscitation (CPR) is limited. We aim to explore the factors associated with hospital mortality in these patients and develop a predictive model to aid clinical decision-making and enhance the survival rates of patients post-resuscitation.

Methods

We sourced the data from a retrospective study within the Dryad dataset, dividing patients who suffered cardiac arrest following CPR into a training set and a validation set at a 7:3 ratio. We identified variables linked to hospital mortality in the training set using Least Absolute Shrinkage and Selection Operator (LASSO) regression, as well as univariate and multivariate logistic analyses. Utilizing these variables, we developed a prognostic nomogram for predicting mortality post-CPR. Calibration curves, the area under receiver operating curves (ROC), decision curve analysis (DCA), and clinical impact curve were used to assess the discriminability, accuracy, and clinical utility of the nomogram.

Results

The study population comprised 374 patients, with 262 allocated to the training group and 112 to the validation group. Of these, 213 patients were dead in the hospital. Multivariate logistic analysis revealed age (OR 1.05, 95% CI: 1.03-1.08), witnessed arrest (OR 0.28, 95% CI: 0.11-0.73), time to return of spontaneous circulation (ROSC) (OR 1.05, 95% CI: 1.02-1.08), non-shockable rhythm (OR 3.41, 95% CI: 1.61-7.18), alkaline phosphatase (OR 1.01, 95% CI: 1-1.01), and sequential organ failure assessment (SOFA) (OR 1.27, 95% CI: 1.15-1.4) were independent risk factors for hospital mortality for patients who survived 24 h after CPR. ROC of the nomogram showed the AUC in the training and validation group was 0.827 and 0.817, respectively. Calibration curves, DCA, and clinical impact curve demonstrated the nomogram with good accuracy and clinical utility.

Conclusion

Our prediction model had accurate predictive value for hospital mortality in patients who survived 24 h after CPR, which will be beneficial for assisting in identifying high-risk patients and intervention. Further confirmation of the model's accuracy required external validation data.

Correlation Between Quantitative Background Suppression on EEG and Serum NSE in Patients With Hypoxic-ischemic Encephalopathy

PURPOSE

We evaluated the correlation between quantitative background activities on electroencephalography (EEG) and serum neuron specific enolase (NSE) in patients with hypoxic-ischemic encephalopathy as well as a diagnostic value of prognostication.

METHODS

This retrospective cohort study enrolled patients with return of spontaneous circulation after cardiac arrest from March 2010 to March 2020. The inclusion criteria were (1) older than the age of 16 years and (2) patients who had both EEG and NSE. The median time for EEG and NSE were 3 days (interquartile range 2-5 days) and 3 days (interquartile range 2-4 days), respectively. The quantification of background activity was conducted with the suppression ratio (SR). We used a machine learning (eXtreme Gradient Boosting algorithm) to evaluate whether the SR could improve the accuracy of prognostication.

RESULTS

We enrolled 151 patients. The receiver operating characteristic analysis revealed a cut-off value of serum NSE and the SR for poor outcome, serum NSE (>31.9 μg/L, area under curve [AUC] = 0.88), and the SR (>21.5%, AUC = 0.75 in the right hemisphere, >34.4%, AUC = 0.76 in the left hemisphere). There was a significant positive correlation between the severity of SR and the level of NSE ( ρ = 0.57, p < 0.0001 for the right hemisphere, ρ = 0.58, p < 0.0001 for the left hemisphere). The SR showed an excellent diagnostic value for predicting poor outcome (93% specificity, 60% sensitivity in the right hemisphere and 93% specificity, 58% sensitivity in the left hemisphere). With machine learning analysis, there was an increment in distinguishing the neurological outcome by adding SR on clinical factors.

CONCLUSIONS

The SR showed a positive correlation with the level of serum NSE. The diagnostic value of the SR for predicting poor outcome was excellent, suggesting that it can be a possible biomarker for neuroprognostication in patients with hypoxic-ischemic encephalopathy.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis

BACKGROUND

Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models show promise in improving these predictions. This systematic review and meta-analysis evaluates how effective these approaches are in predicting clinical outcomes at different time points using structured data.

METHODS

This study followed PRISMA guidelines, involving a comprehensive search across PubMed, Scopus, and Web of Science databases until March 2024. Studies aimed at predicting ROSC, survival (or mortality), and neurological outcomes after cardiac arrest through the application of machine learning or deep learning techniques with structured data were included. Data extraction followed the guidelines of the CHARMS checklist, and the bias risk was evaluated using PROBAST tool. Models reporting the AUC metric with 95 % confidence intervals were incorporated into the quantitative synthesis and meta-analysis.

RESULTS

After extracting 2,753 initial records, 41 studies met the inclusion criteria, yielding 97 machine learning and 16 deep learning models. The pooled AUC for predicting favorable neurological outcomes (CPC 1 or 2) at hospital discharge was 0.871 (95 % CI: 0.813 - 0.928) for machine learning models and 0.877 (95 % CI: 0.831-0.924) across deep learning algorithms. For survival prediction, this value was found to be 0.837 (95 % CI: 0.757-0.916). Considerable heterogeneity and high risk of bias were observed, mainly attributable to inadequate management of missing data and the absence of calibration plots. Most studies focused on pre-hospital factors, with age, sex, and initial arrest rhythm being the most frequent features.

CONCLUSION

Predictive models utilizing AI-based approaches, including machine and deep learning models exhibit enhanced effectiveness compared to previous regression algorithms, but significant heterogeneity and high risk of bias limit their dependability. Evaluating state-of-the-art deep learning models tailored for tabular data and their clinical generalizability can enhance outcome prediction after cardiac arrest.

Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques

BACKGROUND AND OBJECTIVES

Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated for this indication in children. Using machine learning techniques, we studied the predictive value of quantitative EEG (qEEG) features for survival 12 months after CA, based on EEG recordings obtained 24 hours after CA in children. The results were confirmed through visual analysis of EEG background patterns.

METHODS

This is a retrospective single-center study including children (0-17 years) with CA, who were subsequently admitted to the pediatric intensive care unit (PICU) of a tertiary care hospital between 2012 and 2021 after return of circulation (ROC) and were monitored using EEG at 24 hours after ROC. Signal features were extracted from a 30-minute EEG segment 24 hours after CA and used to train a random forest model. The background pattern from the same EEG fragment was visually classified. The primary outcome was survival or death 12 months after CA. Analysis of the prognostic accuracy of the model included calculation of receiver-operating characteristic and predictive values. Feature contribution to the model was analyzed using Shapley values.

RESULTS

Eighty-six children were included (in-hospital CA 27%, out-of-hospital CA 73%). The median age at CA was 2.6 years; 53 (62%) were male. Mortality at 12 months was 56%; main causes of death on the PICU were withdrawal of life-sustaining therapies because of poor neurologic prognosis (52%) and brain death (31%). The random forest model was able to predict death at 12 months with an accuracy of 0.77 and positive predictive value of 1.0. Continuity and amplitude of the EEG signal were the signal parameters most contributing to the model classification. Visual analysis showed that no patients with a background pattern other than continuous with amplitudes exceeding 20 μV were alive after 12 months.

DISCUSSION

Both qEEG and visual EEG background classification for registrations obtained 24 hours after ROC form a strong predictor of nonsurvival 12 months after CA in children.

Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest

Cardiac arrest (CA) is a major disease burden worldwide and has a poor prognosis. Early prediction of CA outcomes helps optimize the therapeutic regimen and improve patients' neurological function. As the current guidelines recommend, many factors can be used to evaluate the neurological outcomes of CA patients. Machine learning (ML) has strong analytical abilities and fast computing speed; thus, it plays an irreplaceable role in prediction model development. An increasing number of researchers are using ML algorithms to incorporate demographics, arrest characteristics, clinical variables, biomarkers, physical examination findings, electroencephalograms, imaging, and other factors with predictive value to construct multi-feature prediction models for neurological outcomes of CA survivors. In this review, we explore the current application of ML models using multiple features to predict the neurological outcomes of CA patients. Although the outcome prediction model is still in development, it has strong potential to become a powerful tool in clinical practice.

Optimal timing of ultra-early diffusion-weighted MRI in out-of-hospital cardiac arrest patients based on a retrospective multicenter cohort study

Diffusion-weighted magnetic resonance imaging (DW-MRI) performed before target temperature management, within 6 h of return of spontaneous circulation (ROSC), is defined as ultra-early DW-MRI. In previous studies, high-signal intensity (HSI) on ultra-early DW-MRI can predict poor neurological outcomes (Cerebral Performance Category 3-5 at 6-months post-ROSC). We aimed to assess the optimal-timing for ultra-early DW-MRI to avoid false-negative outcomes post out-of-hospital cardiac arrest, considering cardiopulmonary resuscitation (CPR) factors. The primary outcomes were HSI in the cerebral cortex or deep gray matter on ultra-early DW-MRI. The impact of CPR factors and ROSC to DW-MRI scan-interval on HSI-presence was assessed. Of 206 included patients, 108 exhibited HSI-presence, exclusively associated with poor neurological outcomes. In multivariate regression analysis, ROSC to DW-MRI scan-interval (adjusted odds ratio [aOR], 1.509; 95% confidence interval (CI): 1.113-2.046; P = 0.008), low-flow time (aOR, 1.176; 95%CI: 1.121-1.233; P < 0.001), and non-shockable rhythm (aOR, 9.974; 95%CI: 3.363-29.578; P < 0.001) were independently associated with HSI-presence. ROSC to DW-MRI scan-interval cutoff of ≥ 2.2 h was particularly significant in low-flow time ≤ 21 min or shockable rhythm group. In conclusion, short low-flow time and shockable rhythm require a longer ROSC to DW-MRI scan-interval. Prolonged low-flow time and non-shockable rhythm reduce the need to consider scan-interval.

Long-term major events after hospital discharge for out-of-hospital cardiac arrest

BACKGROUND

Cardiac arrest remains a global health issue with limited data on long-term outcomes, particularly regarding recurrent cardiovascular events in patients surviving out-of-hospital cardiac arrest. (OHCA). We aimed to describe the long-term occurrence of major cardiac event defined by hospital admission for cardiovascular events or death in OHCA hospital survivors, whichever came first. Our secondary objective were to assess separately occurrence of hospital admission and death, and to identify the factors associated with major event occurrence. We hypothesized that patients surviving an OHCA has a protracted increased risk of cardiovascular events, due to both presence of the baseline conditions that lead to OHCA, and to the cardiovascular consequences of OHCA induced acute ischemia-reperfusion.

METHODS

Consecutive OHCA patients from three hospitals of Sudden Death Expertise Center (SDEC) Registry, discharged alive from 2011 to 2015 were included. Long-term follow-up data were obtained using national inter-regime health insurance information system (SNIIRAM) database and the national French death registry. The primary endpoint was occurrence of a major event defined by hospital admission for cardiovascular events and death, whichever came first during the follow-up. The starting point of the time-to-event analysis was the date of hospital discharge. The follow-up was censored on the date of the first event. For patients without event, follow-up was censored on the date of December, 29th, 2016.

RESULTS

A total of 306 patients (mean age 57; 77% male) were analyzed and followed over a median follow-up of 3 years for hospital admission for cardiovascular event and 6 years for survival. During this period, 38% patients presented a major event. Hospital admission for cardiovascular events mostly occurred during the first year after the OHCA whereas death occurred more linearly during the all period. A previous history of chronic heart failure and coronary artery disease were independently associated with the occurrence of major event (HR 1.75, 95%CI[1.06-2.88] and HR 1.70, 95%CI[1.11-2.61], respectively), whereas post-resuscitation myocardial dysfunction, cardiogenic shock and cardiologic cause of cardiac arrest did not.

CONCLUSION

Survivors from OHCA must to be considered at high risk of cardiovascular event occurrence whatever the etiology, mainly during the first year following the cardiac arrest and should require closed monitoring.

Prognostic value of grey-white matter ratio obtained within two hours after return of spontaneous circulation in out-of-hospital cardiac arrest survivors: A multicenter, observational study

Background

Grey-white matter ratio (GWR) measured by head computed tomography (CT) scan is known as a neurological prognostication tool for out-of-hospital cardiac arrest (OHCA) survivors. The prognostic value of GWR obtained early (within two hours after return of spontaneous circulation [ROSC]) remains a matter of debate.

Methods

We conducted a multicenter, retrospective, observational study at five hospitals. We included adult OHCA survivors who underwent head CT within two hours following ROSC. GWR values were measured using head CT. Average GWR values were calculated by the mean of the GWR-basal ganglia and GWR-Cerebrum. We divided the patients into poor or favorable neurological outcome groups defined by Glasgow-Pittsburgh Cerebral Performance Category scores. The predictive accuracy of GWR performance was assessed using the area under the curve (AUC). The sensitivities and specificities for predicting poor outcome were examined.

Results

Of 377 eligible patients, 281 (74.5%) showed poor neurological outcomes at one month after ROSC. Average GWR values of the poor neurological outcome group were significantly lower than those of the favorable neurological outcome. The average GWR value to predict neurological outcome with Youden index was 1.24 with AUC of 0.799. When average GWR values were 1.15 or lower, poor neurological outcomes could be predicted with 100% specificity.

Conclusions

GWR values measured by head CT scans early (within two hours after ROSC) demonstrated moderate predictive performance for overall ROSC patients. When limited to the patients with GWR values of 1.15 or lower, poor neurological outcomes could be predicted with high specificity.

Neurological prognosis prediction for cardiac arrest patients using quantitative imaging biomarkers from brain computed tomography

PURPOSE

We aimed to predict the neurological prognosis of cardiac arrest (CA) patients using quantitative imaging biomarkers extracted from brain computed tomography images.

METHODS

We retrospectively enrolled 86 CA patients (good prognosis, 32; poor prognosis, 54) who were treated at three hospitals between 2017 and 2019. We then extracted 1131 quantitative imaging biomarkers from whole-brain and local volumes of interest in the computed tomography images of the patients. The data were split into training and test sets containing 60 and 26 samples, respectively, and the training set was used to select representative quantitative imaging biomarkers for classification. In univariate analysis, the classification was evaluated using the p-value of the Brunner-Munzel test and area under the receiver operating characteristic curve (AUC) for the test set. In multivariate analysis, machine learning models reflecting nonlinear and complex relations were trained, and they were evaluated using the AUC on the test set.

RESULTS

The best performance provided p = 0.009 (<0.01) and an AUC of 0.775 (95% confidence interval, 0.590-0.960) for the univariate analysis and an AUCof0.813 (95% confidence interval, 0.640-0.985) for the multivariate analysis. Overall, the gray level with the maximum gradient in the histogram of the three-dimensionally low-pass-filtered image was an important feature for prediction across the analyses.

CONCLUSIONS

Quantitative imaging biomarkers can be used in neurological prognosis prediction for CA patients. Relevant biomarkers may contribute to protocolized computed tomography image acquisition to ensure proper decision support in acute care.

SERUM TRANSACTIVE RESPONSE DNA BINDING PROTEIN 43 ASSOCIATES WITH POOR SHORT-TERM NEUROLOGIC OUTCOME AFTER RETURN OF SPONTANEOUS CIRCULATION FOLLOWING CARDIAC ARREST

ABSTRACT

Objective : To explore the association of serum transactive response DNA binding protein 43 (TDP-43) with 28-day poor neurologic outcome in patients with return of spontaneous circulation (ROSC) after cardiac arrest. Methods : We performed a study between January and December 2023. Eligible patients with ROSC following cardiac arrest were enrolled. Their baseline characteristics were collected, and serum levels of TDP-43, tumor necrosis factor-α, interleukin-6 and 10, C-reactive protein, and neuron-specific enolase (NSE) at 24 h after ROSC were measured. The neurologic function was assessed by the cerebral performance category scores on day 28 after ROSC. Results : A total of 92 patients were included, with 51 and 41 patients in the good and poor neurologic outcome groups, respectively. Serum TDP-43 was significantly higher in the poor than the good neurologic outcome group ( P < 0.05). Univariate and multivariate logistic regression analyses showed that TDP-43, Witnessed CA, IL-6, and NSE were associated with poor 28-day neurologic outcome (all P < 0.05). Restricted cubic spline analysis revealed that TDP-43 at the serum level of 11.64 pg/mL might be an ideal cutoff value for distinguishing between good and poor neurologic outcomes. Area under curve of serum TDP-43 (AUC = 0.78) was close to that of serum NSE (AUC = 0.82). A dynamic nomogram prediction model that combined TDP-43, Witnessed CA, IL-6, and NSE was constructed and validated. Conclusion : Elevated serum TDP-43 level was associated with and could be used together with Witnessed CA, IL-6, and NSE to predict poor 28-day neurologic outcome in patients after ROSC following cardiac arrest.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

Paradoxical cognitive and language function recovery by zolpidem in a patient with traumatic brain injury: A case report

BACKGROUND

Traumatic brain injury (TBI) is a significant public health issue, often resulting from traffic accidents and falls, leading to a wide spectrum of outcomes from mild concussions to severe brain damage. The neurorehabilitation of TBI focuses on enhancing recovery and improving quality of life. Zolpidem, traditionally used for short-term management of insomnia, has shown potential in improving cognitive functions and language in TBI patients. Advances in neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), have facilitated the exploration of the effects of therapeutic interventions on brain activity and functional connectivity in TBI patients.

CASE SUMMARY

We present the case of a 34-year-old male who sustained a TBI from a traffic collision. Despite severe impairments in cognitive and language functions, administration of 10 mg of zolpidem resulted in temporary but significant improvements in these areas, as evidenced by increased Mini-Mental State Examination scores and observed behavioral changes. fNIRS assessments before and after zolpidem administration revealed notable changes in cerebral cortex activity, including increased left hemisphere activation and a shift in functional connectivity to the bilateral frontal lobes, corresponding with the patient's improvement.

CONCLUSION

This case study highlights the potential of zolpidem, a medication traditionally used for insomnia, in enhancing cognitive and verbal functions in a patient with TBI, suggesting a potential therapeutic role for zolpidem in neurorehabilitation, supported by changes in brain activity and connectivity observed through fNIRS. However, further investigation is warranted to validate these findings and elucidate zolpidem's long-term effects on cognitive and functional outcomes in TBI patients.

Comparison of Quantitative EEG Features for the Prediction of Neurological Recovery after Cardiac Arrest in Rodents

The clinical practices of electrophysiological monitoring for the prognosis of neurological recovery in patients were limited by the lack of objective and quantitative assessment. Quantitative EEG (QEEG), a method that utilizes advanced mathematic and signal processing techniques to analyze EEG signals, shows promise in increasing the accuracy of predictions. In this study, we applied wavelet decomposition to EEG signals collected in rodents that suffered asphyxial cardiac arrest and found significant differences in relative band powers and spectral entropies (p<0.05) between rats with favorable and unfavorable recovered outcomes. We trained an SVM classifier with the highest accuracy of 0.864. We also compared the accuracy, sensitivity, and specificity of different predictors.

A potentially lifesaving error: unintentional high-dose adrenaline administration in anaphylaxis-induced cardiac arrest; a case report

BACKGROUND

Cardiopulmonary resuscitation is a crucial skill for emergency medical services. As high-risk-low-frequency events pose an immense mental load to providers, concepts of crew resource management, non-technical skills and the science of human errors are intended to prepare healthcare providers for high-pressure situations. However, medical errors occur, and organizations and institutions face the challenge of providing a blame-free error culture to achieve continuous improvement by avoiding similar errors in the future. In this case, we report a critical medical error during an anaphylaxis-associated cardiac arrest, its handling and the unexpected yet favourable outcome for the patient.

CASE PRESENTATION

During an out-of-hospital cardiac arrest due to chemotherapy-induced anaphylaxis, a patient received a 10-fold dose of epinephrine due to shortcomings in communication and standardization via a central venous port catheter. The patient converted from a non-shockable rhythm into a pulseless ventricular tachycardia and subsequently into ventricular fibrillation. The patient was cardioverted and defibrillated and had a return of spontaneous circulation with profound hypotension only 6 min after the administration of 10 mg epinephrine. The patient survived without any residues or neurological impairment.

CONCLUSIONS

This case demonstrates the potential deleterious effects of shortcomings in communication and deviation from standard protocols, especially in emergencies. Here, precise instructions, closed-loop communication and unambiguous labelling of syringes would probably have avoided the epinephrine overdose central to this case. Interestingly, this serious error may have saved the patient's life, as it led to the development of a shockable rhythm. Furthermore, as the patient was still in profound hypotension after administering 10 mg of epinephrine, this high dose might have counteracted the severe vasoplegic state in anaphylaxis-associated cardiac arrest. Lastly, as the patient was receiving care for advanced malignancy, the likelihood of termination of resuscitation in the initial non-shockable cardiac arrest was significant and possibly averted by the medication error.

Survival, but not the severity of hypoxic-ischemic encephalopathy, is associated with higher mean arterial blood pressure after cardiac arrest: a retrospective cohort study

Background

This study investigates the association between the mean arterial blood pressure (MAP), vasopressor requirement, and severity of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA).

Methods

Between 2008 and 2017, we retrospectively analyzed the MAP 200 h after CA and quantified the vasopressor requirements using the cumulative vasopressor index (CVI). Through a postmortem brain autopsy in non-survivors, the severity of the HIE was histopathologically dichotomized into no/mild and severe HIE. In survivors, we dichotomized the severity of HIE into no/mild cerebral performance category (CPC) 1 and severe HIE (CPC 4). We investigated the regain of consciousness, causes of death, and 5-day survival as hemodynamic confounders.

Results

Among the 350 non-survivors, 117 had histopathologically severe HIE while 233 had no/mild HIE, without differences observed in the MAP (73.1 vs. 72.0 mmHg, pgroup = 0.639). Compared to the non-survivors, 211 patients with CPC 1 and 57 patients with CPC 4 had higher MAP values that showed significant, but clinically non-relevant, MAP differences (81.2 vs. 82.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors (n = 54), who regained consciousness before death, had higher MAP values compared to those with no/mild HIE (n = 179), who remained persistently comatose (74.7 vs. 69.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors, who regained consciousness, required fewer vasopressors (CVI 2.1 vs. 3.6, pgroup < 0.001). Independent of the severity of HIE, the survivors were weaned faster from vasopressors (CVI 1.0).

Conclusions

Although a higher MAP was associated with survival in CA patients treated with a vasopressor-supported MAP target above 65 mmHg, the severity of HIE was not. Awakening from coma was associated with less vasopressor requirements. Our results provide no evidence for a MAP target above the current guideline recommendations that can decrease the severity of HIE.

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.

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.

Combination strategy for prognostication in patients undergoing post-resuscitation care after cardiac arrest

This study investigated the prognostic performance of combination strategies using a multimodal approach in patients treated after cardiac arrest. Prospectively collected registry data were used for this retrospective analysis. Poor outcome was defined as a cerebral performance category of 3-5 at 6 months. Predictors of poor outcome were absence of ocular reflexes (PR/CR) without confounding factors, a highly malignant pattern on the most recent electroencephalography, defined as suppressed background with or without periodic discharges and burst-suppression, high neuron-specific enolase (NSE) after 48 h, and diffuse injury on imaging studies (computed tomography or diffusion-weighted imaging [DWI]) at 72-96 h. The prognostic performances for poor outcomes were analyzed for sensitivity and specificity. A total of 130 patients were included in the analysis. Of these, 68 (52.3%) patients had poor outcomes. The best prognostic performance was observed with the combination of absent PR/CR, high NSE, and diffuse injury on DWI [91.2%, 95% confidence interval (CI) 80.7-97.1], whereas the combination strategy of all available predictors did not improve prognostic performance (87.8%, 95% CI 73.8-95.9). Combining three of the predictors may improve prognostic performance and be more efficient than adding all tests indiscriminately, given limited medical resources.

Longitudinal serum neurofilament light kinetics in post-anoxic encephalopathy

Serum neurofilament light (sNfL) is a promising marker of outcome after cardiac arrest, but its kinetics are unclear. We prospectively measured sNfL concentrations in 62 patients at 0, 1, 3, 5, 7 and 10 days after cardiac arrest. Survivors and non-survivors had similar sNfL at admission (14.2 [8.6-21.9] vs. 22.5 [14.2-46.9] pg/mL) but largely different at 24 h (16.4 [10.2-293] vs. 464.3 [151.8-1658.2], respectively). The AUC for sNfL concentrations predicting death was above 0.95 from Day 1 to 10 (highest on Day 3). Late sNfL measurements may exert prognostic value, especially when early samples are unavailable or prognosis remains unclear.

[Palliative aspects in clinical acute and emergency medicine as well as intensive care medicine : Consensus paper of the DGIIN, DGK, DGP, DGHO, DGfN, DGNI, DGG, DGAI, DGINA and DG Palliativmedizin]

The integration of palliative medicine is an important component in the treatment of various advanced diseases. While a German S3 guideline on palliative medicine exists for patients with incurable cancer, a recommendation for non-oncological patients and especially for palliative patients presenting in the emergency department or intensive care unit is missing to date. Based on the present consensus paper, the palliative care aspects of the respective medical disciplines are addressed. The timely integration of palliative care aims to improve quality of life and symptom control in clinical acute and emergency medicine as well as intensive care.

Predicting neurologic recovery after severe acute brain injury using resting-state networks

OBJECTIVE

There is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit.

METHODS

Each patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient's ten resting-state network values to predict best neurologic outcome within 6 months post-injury.

RESULTS

Of the 25 patients enrolled (mean age = 43.68, range = [19-69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029).

DISCUSSION

Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.

Pupil dilation response elicited by violations of auditory regularities is a promising but challenging approach to probe consciousness at the bedside

Pupil dilation response (PDR) has been proposed as a physiological marker of conscious access to a stimulus or its attributes, such as novelty. In a previous study on healthy volunteers, we adapted the auditory "local global" paradigm and showed that violations of global regularity elicited a PDR. Notably without instructions, this global effect was present only in participants who could consciously report violations of global regularities. In the present study, we used a similar approach in 24 non-communicating patients affected with a Disorder of Consciousness (DoC) and compared PDR to ERPs regarding diagnostic and prognostic performance. At the group level, global effect could not be detected in DoC patients. At the individual level, the only patient with a PDR global effect was in a MCS and recovered consciousness at 6 months. Contrasting the most regular trials to the most irregular ones improved PDR's diagnostic and prognostic power in DoC patients. Pupillometry is a promising tool but requires several methodological improvements to enhance the signal-to-noise ratio and make it more robust for probing consciousness and cognition in DoC patients.

Recommendations on palliative care aspects in intensive care medicine

BACKGROUND

The timely integration of palliative care is important for patients suffering from various advanced diseases with limited prognosis. While a German S-3-guideline on palliative care exists for patients with incurable cancer, a recommendation for non-oncological patients and especially for integration of palliative care into intensive care medicine is missing to date.

METHOD

Ten German medical societies worked on recommendations on palliative care aspects in intensive care in a consensus process from 2018 to 2023.

RESULTS

Based on the german consensus paper, the palliative care aspects of the respective medical disciplines concerning intensive care are addressed. The recommendations partly refer to general situations, but also to specific aspects or diseases, such as geriatric issues, heart or lung diseases, encephalopathies and delirium, terminal renal diseases, oncological diseases and palliative emergencies in intensive care medicine. Measures such as non-invasive ventilation for symptom control and compassionate weaning are also included.

CONCLUSION

The timely integration of palliative care into intensive care medicine aims to improve quality of life and symptom control and also takes into acccount the often urgently needed support for patients' highly stressed relatives.

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.

Prognosis of Cardiac Arrest-Peri-arrest and Post-arrest Considerations

There has been only a small improvement in survival and neurologic outcomes in patients with cardiac arrest in recent decades. Type of arrest, length of total arrest time, and location of arrest alter the trajectory of survival and neurologic outcome. In the post-arrest phase, clinical markers such as blood markers, pupillary light response, corneal reflex, myoclonic jerking, somatosensory evoked potential, and electroencephalography testing can be used to help guide neurological prognostication. Most of the testing should be performed 72 hours post-arrest with special considerations for longer observation periods in patients who underwent TTM or who had prolonged sedation and/or neuromuscular blockade.

Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications

Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.

The effect of sedation and time after cardiac arrest on coma outcome prognostication based on EEG power spectra

Early prognostication of long-term outcome of comatose patients after cardiac arrest remains challenging. Electroencephalography-based power spectra after cardiac arrest have been shown to help with the identification of patients with favourable outcome during the first day of coma. Here, we aim at comparing the power spectra prognostic value during the first and second day after coma onset following cardiac arrest and to investigate the impact of sedation on prognostication. In this cohort observational study, we included comatose patients (N = 91) after cardiac arrest for whom resting-state electroencephalography was collected on the first and second day after cardiac arrest in four Swiss hospitals. We evaluated whether the average power spectra values at 4.6-15.2 Hz were predictive of patients' outcome based on the best cerebral performance category score at 3 months, with scores ranging from 1 to 5 and dichotomized as favourable (1-2) and unfavourable (3-5). We assessed the effect of sedation and its interaction with the electroencephalography-based power spectra on patient outcome prediction through a generalized linear mixed model. Power spectra values provided 100% positive predictive value (95% confidence intervals: 0.81-1.00) on the first day of coma, with correctly predicted 18 out of 45 favourable outcome patients. On the second day, power spectra values were not predictive of patients' outcome (positive predictive value: 0.46, 95% confidence intervals: 0.19-0.75). On the first day, we did not find evidence of any significant contribution of sedative infusion rates to the patient outcome prediction (P > 0.05). Comatose patients' outcome prediction based on electroencephalographic power spectra is higher on the first compared with the second day after cardiac arrest. Sedation does not appear to impact patient outcome prediction.

CT perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients (CANCCAP): protocol for a prospective study

INTRODUCTION

Cardiac arrest remains one of the most common causes of death with the majority occurring outside of hospitals (out of hospital cardiac arrest). Despite advancements in resuscitation management, approximately 50% of comatose cardiac arrest patients (CCAP) will suffer a severe unsurvivable brain injury. To assess brain injury, a neurological examination is conducted, however, its reliability in predicting outcomes in the first days following cardiac arrest is limited. Non-contrast CT is the most employed scan to assess hypoxic changes, even though it is not sensitive to early hypoxic-ischaemic changes in the brain. CT perfusion (CTP) has shown high sensitivity and specificity in brain death patients, although its use in predicting poor neurological outcome in CCAP has not yet been explored. The purpose of this study is to validate CTP for predicting poor neurological outcome (modified Rankin scale, mRS≥4) at hospital discharge in CCAP.

METHODS AND ANALYSIS

The CT Perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients study is a prospective cohort study funded by the Manitoba Medical Research Foundation. Newly admitted CCAP receiving standard Targeted Temperature Management are eligible. Patients undergo a CTP at the same time as the admission standard of care head CT. Admission CTP findings will be compared with the reference standard of an accepted bedside clinical assessment at the time of admission. Deferred consent will be used. The primary outcome is a binary outcome of good neurological status, defined as mRs<4 or poor neurological status (mRs≥4) at hospital discharge. A total of 90 patients will be enrolled.

ETHICS AND DISSEMINATION

This study has been approved by the University of Manitoba Health Research Ethics Board. The findings from our study will be disseminated through peer-reviewed journals and presentations at local rounds, national and international conferences. The public will be informed at the end of the study.

TRIAL REGISTRATION NUMBER

NCT04323020.

Specific thresholds of quantitative pupillometry parameters predict unfavorable outcome in comatose survivors early after cardiac arrest

Aim

Quantitative pupillometry is the guideline-recommended method for assessing pupillary light reflex for multimodal prognostication in comatose patients resuscitated from out-of-hospital cardiac arrest (OHCA). However, threshold values predicting an unfavorable outcome have been inconsistent across studies; therefore, we aimed to identify specific thresholds for all quantitative pupillometry parameters.

Methods

Comatose post-OHCA patients were consecutively admitted to the cardiac arrest center at Copenhagen University Hospital Rigshospitalet from April 2015 to June 2017. The parameters of quantitatively assessed pupillary light reflex (qPLR), Neurological Pupil index (NPi), average/max constriction velocity (CV/MCV), dilation velocity (DV), and latency of constriction (Lat) were recorded on the first three days after admission. We evaluated the prognostic performance and identified thresholds achieving zero percent false positive rate (0% PFR) for an unfavorable outcome of 90-day Cerebral Performance Category (CPC) 3-5. Treating physicians were blinded for pupillometry results.

Results

Of the 135 post-OHCA patients, the primary outcome occurred for 53 (39%) patients.On any day during hospitalization, a qPLR < 4%, NPi < 2.45, CV < 0.1 mm/s, and an MCV < 0.335 mm/s predicted 90-day unfavorable neurological outcome with 0% FPR (95%CI: 0-0%), with sensitivities of 28% (17-40%), 9% (2-19%), 13% (6-23%), and 17% (8-26%), respectively on day 1.

Conclusion

We found that specific thresholds of all quantitative pupillometry parameters, measured at any time following hospital admission until day 3, predicted a 90-day unfavorable outcome with 0% FPR in comatose patients resuscitated from OHCA. However, at 0% FPR, thresholds resulted in low sensitivity. These findings should be further validated in larger multicenter clinical trials.

Between life and death: the brain twilight zones

Clinically, and legally, death is considered a well-defined state of the organism characterized, at least, by a complete and irreversible cessation of brain activities and functions. According to this pragmatic approach, the moment of death is implicitly represented by a discrete event from which all cerebral processes abruptly cease. However, a growing body of experimental and clinical evidence has demonstrated that cardiorespiratory failure, the leading cause of death, causes complex time-dependent changes in neuronal activity that can lead to death but also be reversed with successful resuscitation. This review synthesizes our current knowledge of the succeeding alterations in brain activities that accompany the dying and resuscitation processes. The anoxia-dependent brain defects that usher in a process of potential death successively include: (1) a set of changes in electroencephalographic (EEG) and neuronal activities, (2) a cessation of brain spontaneous electrical activity (isoelectric state), (3) a loss of consciousness whose timing in relation to EEG changes remains unclear, (4) an increase in brain resistivity, caused by neuronal swelling, concomitant with the occurrence of an EEG deviation reflecting the neuronal anoxic insult (the so-called "wave of death," or "terminal spreading depolarization"), followed by, (5) a terminal isoelectric brain state leading to death. However, a timely restoration of brain oxygen supply-or cerebral blood flow-can initiate a mirrored sequence of events: a repolarization of neurons followed by a re-emergence of neuronal, synaptic, and EEG activities from the electrocerebral silence. Accordingly, a recent study has revealed a new death-related brain wave: the "wave of resuscitation," which is a marker of the collective recovery of electrical properties of neurons at the beginning of the brain's reoxygenation phase. The slow process of dying still represents a terra incognita, during which neurons and neural networks evolve in uncertain states that remain to be fully understood. As current event-based models of death have become neurophysiologically inadequate, I propose a new mixed (event-process) model of death and resuscitation. It is based on a detailed description of the different phases that succeed each other in a dying brain, which are generally described separately and without mechanistic linkage, in order to integrate them into a continuum of declining brain activity. The model incorporates cerebral twilight zones (with still unknown neuronal and synaptic processes) punctuated by two characteristic cortical waves providing real-time biomarkers of death- and resuscitation.

Use of Prognostication Instruments in Prognostication Procedures of Postanoxic Coma Patients over Time: A Retrospective Study

BACKGROUND

Many survivors of cardiovascular arrest remain in a postanoxic coma. The neurologist's task is to provide the most accurate assessment of the patient's neurologic prognosis through a multimodal approach of clinical and technical tests. The aim of this study is to analyze differences and developments in the concept of neurological prognosis assessment and in-hospital outcome of patients over a five year-period.

METHODS

This retrospective observational study included 227 patients with postanoxic coma treated in the medical intensive care unit of the University Hospital, Mannheim from January 2016 to May 2021. We retrospectively analyzed patient characteristics, post-cardiac arrest care, and the use of clinical and technical tests for neurological prognosis assessment and patient outcome.

RESULTS

Over the observation period, 215 patients received a completed neurological prognosis assessment. Regarding the multimodal prognostic assessment, patients with poor prognosis (54%) received significantly fewer diagnostic modalities than patients with very likely poor (20.5%), indeterminate (24.2%), or good prognosis (1.4%; p = 0.001). The update of the DGN guidelines in 2017 had no effect on the number of performed prognostic parameters per patient. The finding of bilaterally absent pupillary light reflexes or severe anoxic injury on CT contributed most to a poor prognosis category (OR 8.38, 95%CI 4.01-7.51 and 12.93, 95%CI 5.55-30.13, respectively), whereas a malignant EEG pattern and NSE > 90 µg/L at 72 h resulted in the lowest OR (5.11, 95%CI 2.32-11.25, and 5.89, 95%CI 3.14-11.06, respectively) for a poor prognosis category. Assessment of baseline NSE significantly increased over the years (OR 1.76, 95%CI 1.4-2.22, p < 0.001), and assessment of follow-up NSE at 72 h trended to increase (OR 1.19, 95%CI 0.99-1.43, p = 0.06). In-hospital mortality was high (82.8%), remained unchanged over the observation period, and corresponded to the number of patients in whom life-sustaining measures were discontinued.

CONCLUSIONS

Among comatose survivors of cardiac arrest, the prognosis remains poor. Prognostication of a poor outcome led nearly exclusively to withdrawal of care. Prognostic modalities varied considerably with regard to their contribution to a poor prognosis category. Increasing enforcement of a standardized prognosis assessment and standardized evaluation of diagnostic modalities are needed to avoid false-positive prognostication of poor outcomes.

Spectrum of Ictal-Interictal Continuum: The Significance of 2HELPS2B Score and Background Suppression

PURPOSE

The aims of this study were to identify (1) the spectrum of ictal-interictal continuum (IIC) using the two dimensions of 2HELPS2B score and background suppression and (2) the response to subsequent anti-seizure drugs depends on the spectrum of IIC.

METHODS

The study prospectively enrolled 62 patients with IIC on EEG. The diagnosis of nonconvulsive status epilepticus was attempted with Salzburg criteria as well as clinical and neuroimaging data. IICs were dichotomized into patients with nonconvulsive status epilepticus and coma-IIC. The 2HELPS2B score was evaluated as the original proposal. The suppression ratio was analyzed with Persyst software.

RESULTS

Forty-seven cases (75.8%) were nonconvulsive status epilepticus-IIC and 15 cases (24.2%) were coma-IIC. Multivariate analysis revealed that the 2HELPS2B score was the only significant variable dichotomizing the spectrum of IIC (odds ratio, 3.0; 95% confidence interval, 1.06-8.6; P = 0.03 for nonconvulsive status epilepticus-IIC). In addition, the suppression ratio was significantly negatively correlated with 2HELPS2B scores (Spearman coefficient = -0.37, P = 0.004 for left hemisphere and Spearman coefficient = -0.3, P = 0.02 for right hemisphere). Furthermore, patients with higher 2HELPS2B score (74% [14/19] in ≥2 points vs. 44% [14/32] in <2 points, P = 0.03 by χ 2 test) and lower suppression ratio (62% [23/37] in ≤2.18 vs. 35% [6/17] in >2.18, P = 0.06 by χ 2 test) seemed to be more responsive to subsequent anti-seizure drug.

CONCLUSIONS

The 2HELPS2B score and background suppression can be used to distinguish the spectrum of IIC and thereby predict the response to subsequent anti-seizure drug.

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic- ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.

Are serial neuron-specific enolase levels associated with neurologic outcome of ECPR patients: A retrospective multicenter observational study

AIM OF THE STUDY

This study aims to evaluate whether neuron-specific enolase (NSE) level at 48 h after extracorporeal cardiopulmonary resuscitation (ECPR) is associated with neurologic outcomes at 6 months after hospital discharge.

METHODS

This was a retrospective, multicenter, observational study of adult patients who received ECPR between May 2010 and December 2016. In the two hospitals involved in this study, NSE measurements were a routine part of the protocol for patients who received ECPR. Serial NSE levels were measured in all patients with ECPR. NSE levels were measured 24, 48, and 72 h after ECPR. The primary outcome was Cerebral Performance Categories (CPC) scale at 6 months after hospital discharge according to NSE levels at 48 h after ECPR.

RESULTS

At follow-up 6 months after hospital discharge, favorable neurologic outcomes of CPC 1 or 2 were observed in 9 (36.0%) of the 25 patients, and poor neurologic outcomes of CPC 3, 4, or 5 were observed in 16 (64%) patients. NSE levels at 24 h in the favorable and poor neurologic outcome groups were 58.3 (52.5-73.2) μg/L and 64.2 (37.9-89.8) μg/L, respectively (p = 0.95). NSE levels at 48 h in the favorable and poor neurologic outcome groups were 52.1 (22.3-64.9) μg/L and 302.0 (62.8-360.2) μg/L, respectively (p = 0.01). NSE levels at 72 h were 37.2 (12.5-53.2) μg/L and 240.9 (75.3-370.0) μg/L, respectively (p < 0.01). In receiver operating characteristic (ROC) curve analysis, as the predictor of poor outcome, the optimal cut-off value for NSE level at 48 h was 140.5 μg/L, and the area under the curve (AUC) was 0.844 (p < 0.01). The optimal cut-off NSE level at 72 h was 53.2 μg/L, and the AUC was 0.897 (p < 0.01).

CONCLUSIONS

NSE level at 72 h displayed the highest association with neurologic outcome after ECPR, and NSE level at 48 h was also associated with neurologic outcome after ECPR.

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.

Benign EEG for prognostication of favorable outcome after cardiac arrest: A reappraisal

AIM

The current EEG role for prognostication after cardiac arrest (CA) essentially aims at reliably identifying patients with poor prognosis ("highly malignant" patterns, defined by Westhall et al. in 2014). Conversely, "benign EEGs", defined by the absence of elements of "highly malignant" and "malignant" categories, has limited sensitivity in detecting good prognosis. We postulate that a less stringent "benign EEG" definition would improve sensitivity to detect patients with favorable outcomes.

METHODS

Retrospectively assessing our registry of unconscious adults after CA (1.2018-8.2021), we scored EEGs within 72 h after CA using a modified "benign EEG" classification (allowing discontinuity, low-voltage, or reversed anterio-posterior amplitude development), versus Westhall's "benign EEG" classification (not allowing the former items). We compared predictive performances towards good outcome (Cerebral Performance Category 1-2 at 3 months), using 2x2 tables (and binomial 95% confidence intervals) and proportions comparisons.

RESULTS

Among 381 patients (mean age 61.9 ± 15.4 years, 104 (27.2%) females, 240 (62.9%) having cardiac origin), the modified "benign EEG" definition identified a higher number of patients with potential good outcome (252, 66%, vs 163, 43%). Sensitivity of the modified EEG definition was 0.97 (95% CI: 0.92-0.97) vs 0.71 (95% CI: 0.62-0.78) (p < 0.001). Positive predictive values (PPV) were 0.53 (95% CI: 0.46-0.59) versus 0.59 (95% CI: 0.51-0.67; p = 0.17). Similar statistics were observed at definite recording times, and for survivors.

DISCUSSION

The modified "benign EEG" classification demonstrated a markedly higher sensitivity towards favorable outcome, with minor impact on PPV. Adaptation of "benign EEG" criteria may improve efficient identification of patients who may reach a good outcome.

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.

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Background

Despite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.

Methods

We present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.

Results

Analysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).

Conclusion

statistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA.