Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest

Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine

BACKGROUND

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

METHODS

In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework.

RESULTS

Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

CONCLUSIONS

In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Multimodal assessment of favorable neurological outcome using NSE levels and kinetics, EEG and SSEP in comatose patients after cardiac arrest

BACKGROUND

Prognostic markers of good neurological outcome after cardiac arrest (CA) remain limited. We aimed to evaluate the prognostic value of neuron-specific enolase (NSE), electroencephalogram (EEG) and somatosensory evoked potentials (SSEP) in predicting good outcome, assessed separately and in combination.

METHODS

A retrospective study was conducted in a tertiary CA center, using a prospective registry. We included all patients comatose after discontinuation of sedation and with one EEG and NSE blood measurement at 24, 48 or/and 72 h after CA. The primary outcome was favorable neurological outcome at three months, a Cerebral Performance Categories (CPC) scale 1-2 defining a good outcome.

RESULTS

Between January 2017 and April 2024, 215 patients were included. Participants were 63 years old (IQR [52-73]), and 73% were male. At 3 months, 54 patients (25.1%) had a good outcome. Compared to the poor outcome group, NSE blood levels were significantly lower in the good outcome group at 24 h (39 IQR[27-45] vs 54 IQR[37-82]µg/L, p < 0.001), 48 h (26 [18-43] vs 107 [54-227]µg/L, p < 0.001) and 72 h (20 µg/L IQR [15-30] vs 184 µg/l IQR [60-300], p < 0,001). Normal NSE (i.e., < 17 µg/L) at 24 h was highly predictive of good outcome, with a predictive positive value (PPV) of 71% despite a sensitivity (Se) of 9%. The best cut-off values for NSE at 24, 48 and 72 h were below 45.5, 51.5 and 41.5 µg/L, yielding PPV of 64%, 80% and 83% and sensitivities of 74%, 93% and 90%, respectively. A decreasing trend in NSE levels between 24 and 72 h was also highly predictive of good outcome (PPV 82%, Se 81%). A benign EEG pattern was more frequently observed in the good outcome group (87.1 vs 14.9%, p < 0.001) and predicted a good outcome with a PPV of 72% and a Se of 94%. Regarding SSEPs, a bilateral N20-baseline amplitude > 0.85 µV was predictive of good outcome (PPV 75%, Se 100%). The combination of NSE < 51.5 µg/l at 48 h, a decreasing NSE trend between 24 and 72 h and a benign EEG showed the best predictive value (PPV 96%, Se 76%).

CONCLUSION

In comatose patients after CA, a low NSE levels at 24, 48 h or 72 h, a decreasing trend in NSE over time, a benign EEG and a high N20 amplitude are robust markers of favorable outcome, reducing prognosis uncertainty.

Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest

OBJECTIVE

Clinically, patients in a coma after cardiac arrest are given the prognosis of "neurological recovery" to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma.

APPROACH

Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. In addition, Shapley additive explanation (SHAP) values were utilized to rank and analyze the importance of the features.

RESULTS

Our results indicated that the three different EEG features helped achieve a fivefold cross-validation receiver-operating characteristic (ROC) of 0.87. Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. Among these, low-frequency long-distance functional connectivity (45%) was associated with a poor prognosis, whereas high-frequency short-distance functional connectivity (25%) was linked with a good prognosis. Burst suppression ratio is 20%, concentrated in the left frontal-temporal and right occipital-temporal regions at high thresholds (10/15 mV), demonstrating its strong discriminative power.

SIGNIFICANCE

Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. These findings provide a clinically actionable framework for advancing neurological prognosis and optimizing patient care.

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.

[Recommendations for time-limited trial in neurocritical care]

Many acute brain disorders are associated with acute disorders of consciousness. In an emergency situation, life-saving measures are usually taken first and intensive care is initiated. If there is no significant improvement with recovery of consciousness in the first few days, very complex decision-making situations arise regularly. In neurointensive care, a time-limited therapy trial (TLT) is an important structuring element in treatment planning and communication, as a binding agreement between the treatment team and the patient or legal representative on a treatment concept for a defined period of time. Due to the prolonged neurological rehabilitation phase, the TLT in neurointensive care can also last weeks or months. This often requires interdepartmental communication (acute/rehabilitation/long-term care), re-evaluation and implementation in neurointensive care. The recommendations include the definition, empirical evidence and implementation suggestions for a TLT for critically ill neurointensive care patients.

Eye movement detection using electrooculography and machine learning in cardiac arrest patients

AIM

To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. Eye movement may be a promising marker of arousal recovery, as pathways for eye movement and arousal share common anatomic structures. Continuous quantification of eye movement is feasible through electroencephalogram (EEG) with EOG, but manual quantification is resource-intensive.

METHODS

We conducted a retrospective, single-center cohort study of post-CA patients who underwent standard-of-care EEG/EOG monitoring in the intensive care unit from 2020 to 2023. We trained a machine learning algorithm to detect eye movement on one-hour of EOG data from 145,800 one-second samples from 48 patients. Performance was assessed on a reserved test set of 12-hours of EOG data from 705,600 one-second samples from 24 patients using area under the curve (AUC), sensitivity, and specificity.

RESULTS

Of 72 eligible patients, average age was 56.9 years, and 46 (63.9%) were female. In the training group of 48 patients, 35 (72.9%) survived and 32 (66.7%) followed commands. In the test group, 16 (66.7%) survived and 7 (29.2%) followed commands. Our final algorithm identified eye movement with sensitivity of 94.0%, specificity of 82.0%, and an AUC of 94.2%.

CONCLUSION

Automated eye movement detection from EOG is highly sensitive in CA patients. Potential applications include using eye movement quantification to evaluate associations with recovery.

Electrographic and Clinical Determinants of Good Outcome After Postanoxic Status Epilepticus

BACKGROUND AND OBJECTIVES

Postanoxic electrographic status epilepticus (PSE) affects up to a third of all comatose patients after cardiac arrest (CA) and is associated with high mortality. Late PSE onset (>24 hours), from a restored continuous background pattern, and absence of established indicators of poor outcome at multimodal prognostication are described in survivors. We aimed to determine the increase in probability of good long-term outcome after PSE in patients presenting with this favorable PSE profile compared with all patients with PSE.

METHODS

This is a prospective observational substudy of the international Targeted Hypothermia vs Targeted Normothermia After Out-of-Hospital Cardiac Arrest trial (TTM2-trial, 2017-2020) including adult comatose patients resuscitated from CA with continuous EEG (cEEG) monitoring. EEG background pattern and type of PSE were determined using standardized EEG terminology of the American Clinical Neurophysiology Society, blinded to clinical data. On day 4, multimodal prognostication was performed according to the European postresuscitation guidelines. Good outcome was defined as a modified Rankin Scale score of 0-3 at 6 months. Detailed follow-up was performed at 6 and 24 months.

RESULTS

A total of 191 patients were monitored with cEEG, of whom 52 (27%) developed possible or definite PSE at a median of 42 hours [IQR 32-46] after CA. The median age was 70 (IQR 63-77) years, and 35% were female. Favorable PSE profile was present in 20 patients (38%), of whom 12 patients (60%) survived until 6 months and 8 (40%) had good outcome; thus, the probability of good outcome increased 2.7 times. All patients lacking a favorable PSE profile had poor outcome. All patients with good outcome obeyed commands within the first 7 days. At 24 months, all 12 survivors were still alive and 7 had good functional outcome. Detailed follow-up at 24 months showed that most had only mild cognitive impairment and overall life satisfaction was similar to the general population.

DISCUSSION

PSE is compatible with good outcome when onset is late and from a continuous background and no established indicators of poor outcome are present. One-third of patients with PSE had favorable PSE profile, of whom well over a third eventually had good outcome and showed improved level of consciousness within the first week.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov Identifier: NCT02908308.

Myoclonus After Cardiac Arrest did not Correlate with Cortical Response on Somatosensory Evoked Potentials

PurposeMyoclonus after anoxic brain injury is a marker of significant cerebral injury. Absent cortical signal (N20) on somatosensory evoked potentials (SSEPs) after cardiac arrest is a reliable predictor of poor neurological recovery when combined with an overall clinical picture consistent with severe widespread neurological injury. We evaluated a clinical question of if SSEP result could be predicted from other clinical and neurodiagnostic testing results in patients with post-anoxic myoclonus.MethodsRetrospective chart review of all adult patients with post-cardiac arrest myoclonus who underwent both electroencephalographic (EEG) monitoring and SSEPs for neuroprognostication. Myoclonus was categorized as "non-myoclonic movements," "myoclonus not captured on EEG," "myoclonus without EEG correlate," "myoclonus with EEG correlate," and "status myoclonus." SSEP results were categorized as all absent, all present, N18 and N20 absent bilaterally, and N20 only absent bilaterally. Cox proportional hazards with censoring was used to evaluate the association of myoclonus category, SSEP results, and confounding factors with survival.ResultsIn 56 patients, median time from arrest to either confirmed death or last follow up was 9 days. The category of myoclonus was not associated with SSEP result or length of survival. Absence of N20 s or N18 s was associated with shorter survival (N20 hazard ratio [HR] 4.4, p = 0.0014; N18 HR 5.5, p < 0.00001).ConclusionsCategory of myoclonus did not reliably predict SSEP result. SSEP result was correlated with outcome consistently, but goals of care transitioned to comfort measures only in all patients with present peripheral potentials and either absent N20 s only or absence of N18 s and N20 s. Our results suggest that SSEPs may retain prognostic value in patients with post-anoxic myoclonus.

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.

Retrospective Visual and Quantitative Assessment of Burst Suppression With and Without Identical Bursts in Patients After Cardiac Arrest

BACKGROUND

The objective of this study was to assess the prognostic significance of identical bursts (IBs) in cardiac arrest survivors with burst suppression on continuous electroencephalogram (cEEG) monitoring. Burst suppression with IBs is associated with poor neurological outcomes and mortality.

METHODS

We conducted a retrospective analysis of cardiac arrest survivors admitted to a US academic medical center between 2013 and 2021 who had an EEG background of burst suppression. EEG and clinical features were extracted from our institutional review board-approved repositories. EEG features were qualitatively and quantitatively rated at 0, 12, 24, 48, and 72 h following initiation of monitoring. Qualitative visual assessment occurred, blinded to all clinical features, including outcomes, and in accordance with the current American Clinical Neurophysiology Society definition. Quantitative assessment involved manual marking of 50 consecutive pairs of bursts and interburst intervals (IBIs) for analysis. Similarity of bursts/IBIs were assessed with correlation coefficients. The primary clinical outcome was survival to hospital discharge. Comparisons were performed between groups, and a multivariate model was generated for significant variables.

RESULTS

Of 593 cardiac arrest patients, 203 (34.2%) had burst suppression. Thirty-one (15.3%) patients with burst suppression survived. IBs were detected in 80 patients (39.4% of burst suppression). No patient with qualitatively identified IBs had a good neurological outcome (76 deceased, 4 in a state of unresponsive wakefulness). Whereas 11 of 123 (8.9%) with burst suppression without IB had Cerebral Performance Category scores of 1-2. Quantitative analysis of 268 instances of burst suppression demonstrated that mortality was associated with longer bursts, longer IBIs, and higher burst correlation coefficients (i.e., bursts that were more similar to each other) only when allowing analysis of the first 2 s of bursts. Binary logistic regression showed that the only independent EEG predictor of mortality was the burst correlation coefficient measured over 2 s (adjusted odds ratio 4.82 [95% confidence interval 1.21-8.42], p = 0.009).

CONCLUSIONS

Using a single-center US cohort, IBs within 72 h post cardiac arrest were strongly associated with poor outcomes. Quantitative analysis revealed that including the first 2 s of the bursts was superior to limiting the analysis to 0.5-1 s.

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.

Timing of neuroprognostication in the ICU

PURPOSE OF REVIEW

Neuroprognostication after acute brain injury (ABI) is complex. In this review, we examine the threats to accurate neuroprognostication, discuss strategies to mitigate the self-fulfilling prophecy, and how to approach the indeterminate prognosis.

RECENT FINDINGS

The goal of neuroprognostication is to provide a timely and accurate prediction of a patient's neurologic outcome so treatment can proceed in accordance with a patient's values and preferences. Neuroprognostication should be delayed until at least 72 h after injury and/or only when the necessary prognostic data is available to avoid early withdraw life-sustaining treatment on patients who may otherwise survive with a good outcome. Clinicians should be aware of the limitations of available predictors and prognostic models, the role of flawed heuristics and the self-fulfilling prophecy, and the influence of surrogate decision-maker bias on end-of-life decisions.

SUMMARY

The approach to neuroprognostication after ABI should be systematic, use highly reliable multimodal data, and involve experts to minimize the risk of erroneous prediction and perpetuating the self-fulfilling prophecy. Even when such standards are rigorously upheld, the prognosis may be indeterminate. In such cases, clinicians should engage in shared decision-making with surrogates and consider the use of a time-limited trial.

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.

Time-dependent association of grey-white ratio on early brain CT predicting outcomes after cardiac arrest at hospital discharge

BACKGROUND

Cerebral edema after cardiac arrest can be quantified by the ratio of grey matter to white matter radiodensity (GWR) on computed tomography (CT). Severe edema predicts worse outcomes. We hypothesized the sensitivity and false positive rate of GWR predicting outcomes change over the first 24 hours post-arrest.

METHODS

We performed a single-center retrospective cohort study including patients resuscitated from cardiac arrest between January 2010 and December 2023 who were unresponsive to verbal commands. We excluded patients who arrested from a primary traumatic or neurological etiology and those without brain imaging within 24 hours of arrest. We divided patients into groups based on time from arrest to CT, then quantified the performance of GWR dichotomized at <1.10 and <1.20, predicting in-hospital mortality and death by neurologic criteria (DNC).

RESULTS

We included 2,204 patients with mean age 59 (SD 16) years. Overall, 1651 (75%) died in the hospital, of whom 248 (11%) progressed to DNC. Sensitivity of GWR <1.10 and GWR <1.20 for predicting in-hospital mortality increased over the first four hours post-arrest, reaching a maximum of 25% after five hours, while false positive rates remained <5% at all time points. Similar temporal trends were observed with DNC, although absolute values of sensitivity and false positive rate (FPR) varied.

CONCLUSION

The sensitivity and FPR of early GWR predicting in-hospital mortality and DNC after resuscitation from cardiac arrest varies over the initial post-arrest period. Reduced GWR on brain CTs is most sensitive for in-hospital mortality when obtained more than four hours post-arrest and for DNC when obtained between four and five hours. However, FPR remained execellent throughout, making early reductions in GWR a specific marker of poor outcome regardless of timing. While brain CTs obtained within the first 24 hours post-arrest may be indicated to evaluate for neurologic etiologies of arrest, they may be less informative as an independent marker of prognosis.

Risk scoring systems for early prediction of short-term mortality in resuscitated out-of-hospital cardiac arrest patients

INTRODUCTION

Out-of-hospital cardiac arrest (OHCA) is a critical condition associated with high mortality rates and neurological impairment among survivors. In comatose OHCA patients who achieve return of spontaneous circulation, early risk stratification is important to inform treatment pathways and potentially improve outcomes. A range of prognostic tools have been developed to predict survival and neurological recovery. Each tool incorporates a unique combination of clinical, biochemical and physiological markers.

AREAS COVERED

This review article evaluates the required clinical data, predictive performances and practical applicability of major risk scores. A literature review was conducted in PubMed and Embase for studies published between January 2000 and October 2024. The review emphasizes the variability in discriminative power among the selected scores, with some models offering high sensitivity and specificity in outcome prediction, while others prioritize simplicity and accessibility.

EXPERT OPINION

Despite the advancements of these tools, limitations persist in data dependency and the clinical adaptability, highlighting areas for future improvement. Integrating artificial intelligence and real-time analytics could enhance predictive accuracy, offering dynamic prognostic capabilities that adapt to individual patient trajectories. This evolution must be grounded in ethical considerations to ensure predictive technologies complement rather than replace clinical judgment, balancing technology's potential with the complexities of individualized patient care.

Evoked potentials in patients with disorders of consciousness

Acute coma in the intensive care unit and persistent disorders of consciousness (DoC) in neuro-rehabilitation are frequent in patients with hypoxic-ischemic encephalopathy after cardiac arrest (CA), traumatic brain injury, intracranial hemorrhage, or ischemic stroke. Reliable prognostication of long-term neurologic outcomes cannot be made by clinical examination alone in the early phase for many patients, and thus, additional investigations are necessary. Evoked potentials provide inexpensive, real-time, high temporal resolution, bedside, quantifiable information on different sensory pathways into the brain including local and global cortical processing. Short-latency somatosensory evoked potentials can reliably predict poor neurologic long-term outcome in the early phase after CA and are recommended by guidelines as one investigation within an early multimodal assessment. Middle-latency and event-related or cognitive evoked potentials provide information on the integrity of more advanced cortical processing, some closely related to consciousness. This information can help to identify those comatose patients with a good prognosis in the acute phase and help to better understand their precise clinical state and the chances of further recovery in patients with persistent DoC in neuro-rehabilitation. Further studies are necessary to improve the applicability of research findings in the clinical sphere.

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Cardiac arrest and disorders of consciousness

As the second most common cause of coma and disorders of consciousness, cardiac arrest is defined as a cessation of cardiac mechanical activity and absence of circulation. Cardiac arrest can happen due to an intrinsic cardiac condition or secondary to noncardiac causes such as respiratory, neurologic, metabolic causes or external causes such as toxic ingestion, asphyxia, drowning, trauma, and other environmental exposures. While cardiac arrest resuscitation research and practice has evolved over decades, the overall survival to hospital discharge remains low across different types of cardiac arrest (about 9%-29%). This chapter focuses on disorders of consciousness after cardiac arrest and how it is different from other etiologies. It also discusses advances and controversies in diagnosis, management, prognostication and research.

Optimizing brain protection after cardiac arrest: advanced strategies and best practices

Cardiac arrest (CA) is associated with high incidence and mortality rates. Among patients who survive the acute phase, brain injury stands out as a primary cause of death or disability. Effective intensive care management, including targeted temperature management, seizure treatment and maintenance of normal physiological parameters, plays a crucial role in improving survival and neurological outcomes. Current guidelines advocate for neuroprotective strategies to mitigate secondary brain injury following CA, although certain treatments remain subjects of debate. Clinical examination and neuroimaging studies, both invasive and non-invasive neuromonitoring methods and serum biomarkers are valuable tools for predicting outcomes in comatose resuscitated patients. Neuromonitoring, in particular, provides vital insights for identifying complications, personalizing treatment approaches and forecasting prognosis in patients with brain injury post-CA. In this review, we offer an overview of advanced strategies and best practices aimed at optimizing brain protection after CA.

Neurological Monitoring and Management for Adult Extracorporeal Membrane Oxygenation Patients: Extracorporeal Life Support Organization Consensus Guidelines

BACKGROUND

Critical care of patients on extracorporeal membrane oxygenation (ECMO) with acute brain injury (ABI) is notable for a lack of high-quality clinical evidence. Here, we offer guidelines for neurological care (neurological monitoring and management) of adults during and after ECMO support.

METHODS

These guidelines are based on clinical practice consensus recommendations and scientific statements. We convened an international multidisciplinary consensus panel including 30 clinician-scientists with expertise in ECMO from all chapters of the Extracorporeal Life Support Organization (ELSO). We used a modified Delphi process with three rounds of voting and asked panelists to assess the recommendation levels.

RESULTS

We identified five key clinical areas needing guidance: (1) neurological monitoring, (2) post-cannulation early physiological targets and ABI, (3) neurological therapy including medical and surgical intervention, (4) neurological prognostication, and (5) neurological follow-up and outcomes. The consensus produced 30 statements and recommendations regarding key clinical areas. We identified several knowledge gaps to shape future research efforts.

CONCLUSIONS

The impact of ABI on morbidity and mortality in ECMO patients is significant. Particularly, early detection and timely intervention are crucial for improving outcomes. These consensus recommendations and scientific statements serve to guide the neurological monitoring and prevention of ABI, and management strategy of ECMO-associated ABI.

The Recovery of Consciousness via Evidence-Based Medicine and Research (RECOVER) Program: A Paradigm for Advancing Neuroprognostication

Background

Neuroprognostication for disorders of consciousness (DoC) after severe acute brain injury is a major challenge, and the conventional clinical approach struggles to keep pace with a rapidly evolving literature. Lacking specialization, and fragmented between providers, conventional neuroprognostication is variable, frequently incongruent with guidelines, and prone to error, contributing to avoidable mortality and morbidity.

Recent Findings

We review the limitations of the conventional approach to neuroprognostication and DoC care, and propose a paradigm entitled the Recovery of Consciousness Via Evidence-Based Medicine and Research (RECOVER) program to address them. The aim of the RECOVER program is to provide specialized, comprehensive, and longitudinal care that synthesizes interdisciplinary perspectives, provides continuity to patients and families, and improves the future of DoC care through research and education.

Implications for Practice

This model, if broadly adopted, may help establish neuroprognostication as a new subspecialty that improves the care of this vulnerable patient population.

Cardiogenic shock

Cardiogenic shock is a complex syndrome defined by systemic hypoperfusion and inadequate cardiac output arising from a wide array of underlying causes. Although the understanding of cardiogenic shock epidemiology, specific subphenotypes, haemodynamics, and cardiogenic shock severity staging has evolved, few therapeutic interventions have shown survival benefit. Results from seminal randomised controlled trials support early revascularisation of the culprit vessel in infarct-related cardiogenic shock and provide evidence of improved survival with the use of temporary circulatory support in selected patients. However, numerous questions remain unanswered, including optimal pharmacotherapy regimens, the role of mechanical circulatory support devices, management of secondary organ dysfunction, and best supportive care. This Review summarises current definitions, pathophysiological principles, and management approaches in cardiogenic shock, and highlights key knowledge gaps to advance individualised shock therapy and the evidence-based ethical use of modern technology and resources in cardiogenic shock.

Prevalence and Neurological Outcomes of Comatose Patients With Extracorporeal Membrane Oxygenation

OBJECTIVES

To investigate prevalence, risk factors, and in-hospital outcomes of comatose extracorporeal membrane oxygenation (ECMO) patients.

DESIGN

Retrospective observational.

SETTING

Tertiary academic hospital.

PARTICIPANTS

Adults received venoarterial (VA) or venovenous (VV) ECMO support between November 2017 and April 022.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We defined 24-hour off sedation as no sedative infusion (except dexmedetomidine) or paralytics administration over a continuous 24-hour period while on ECMO. Off-sedation coma (comaoff) was defined as a Glasgow Coma Scale score of ≤8 after achieving 24-hour off sedation. On-sedation coma (comaon) was defined as a Glasgow Coma Scale score of ≤8 during the entire ECMO course without off sedation for 24 hours. Neurological outcomes were assessed at discharge using the modified Rankin scale (good, 0-3; poor, 4-6). We included 230 patients (VA-ECMO 143, 65% male); 24-hour off sedation was achieved in 32.2% VA-ECMO and 26.4% VV-ECMO patients. Among all patients off sedation for 24 hours (n = 69), 56.5% VA-ECMO and 52.2% VV-ECMO patients experienced comaoff. Among those unable to be sedation free for 24 hours (n = 161), 50.5% VA-ECMO and 17.2% VV-ECMO had comaon. Comaoff was associated with poor outcomes (p < 0.05) in VA-ECMO and VV-ECMO groups, whereas comaon only impacted the VA-ECMO group outcomes. In a multivariable analysis, requirement of renal replacement therapy was an independent risk factor for comaoff after adjusting for ECMO configuration, after adjusting for ECMO configuration, acute brain injury, pre-ECMO partial pressure of oxygen in arterial blood, partial pressure of carbon dioxide in arterial blood, pH, and bicarbonate level (worst value within 24 hours before cannulation).

CONCLUSIONS

Comaoff was common and associated with poor outcomes at discharge. Requirement of renal replacement therapy was an independent risk factor.

Recommendations for prediction models in clinical practice guidelines for cardiovascular diseases are over-optimistic: a global survey utilizing a systematic literature search

Background

This study aimed to synthesize the recommendations for prediction models in cardiovascular clinical practice guidelines (CPGs) and assess the methodological quality of the relevant primary modeling studies.

Methods

We performed a systematic literature search of all available cardiovascular CPGs published between 2018 and 2023 that presented specific recommendations (whether in support or non-support) for at least one multivariable clinical prediction model. For the guideline-recommended models, the assessment of the methodological quality of their primary modeling studies was conducted using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

In total, 46 qualified cardiovascular CPGs were included, with 69 prediction models and 80 specific recommendations. Of the 80 specific recommendations, 74 supported 57 models (53 were fully recommended and 4 were conditionally recommended) in cardiovascular practice with moderate to strong strength. Most of the guideline-recommended models were focused on predicting prognosis outcomes (53/57, 93%) in primary and tertiary prevention, focusing primarily on long-term risk stratification and prognosis management. A total of 10 conditions and 7 types of target population were involved in the 57 models, while heart failure (14/57, 25%) and a general population with or without cardiovascular risk factor(s) (12/57, 21%) received the most attention from the guidelines. The assessment of the methodological quality of 57 primary studies on the development of the guideline-recommended models revealed that only 40% of the modeling studies had a low risk of bias (ROB). The causes of high ROB were mainly in the analysis and participant domains.

Conclusions

Global cardiovascular CPGs presented an unduly positive appraisal of the existing prediction models in terms of ROB, leading to stronger recommendations than were warranted. Future cardiovascular practice may benefit from well-established clinical prediction models with better methodological quality and extensive external validation.

Outcomes of an extracorporeal cardiopulmonary resuscitation (ECPR) program for in- and out-of-hospital cardiac arrest in a tertiary hospital in Spain

OBJECTIVE

To analyze if the implementation of a multidisciplinary extracorporeal cardiopulmonary resuscitation (ECPR) program in a tertiary hospital in Spain is feasible and could yield survival outcomes similar to international published experiences.

DESIGN

Retrospective observational cohort study.

SETTING

One tertiary referral university hospital in Spain.

PATIENTS

All adult patients receiving ECPR between January 2019 and April 2023.

INTERVENTIONS

Prospective collection of variables and follow-up for up to 180 days.

MAIN VARIABLES OF INTEREST

To assess outcomes, survival with good neurological outcome defined as a Cerebral Performance Categories scale 1-2 at 180 days was used. Secondary variables were collected including demographics and comorbidities, cardiac arrest and cannulation characteristics, ROSC, ECMO-related complications, survival to ECMO decannulation, survival at Intensive Care Unit (ICU) discharge, survival at 180 days, neurological outcome, cause of death and eligibility for organ donation.

RESULTS

Fifty-four patients received ECPR, 29 for OHCA and 25 for IHCA. Initial shockable rhythm was identified in 27 (50%) patients. The most common cause for cardiac arrest was acute coronary syndrome [29 (53.7%)] followed by pulmonary embolism [7 (13%)] and accidental hypothermia [5 (9.3%)]. Sixteen (29.6%) patients were alive at 180 days, 15 with good neurological outcome. Ten deceased patients (30.3%) became organ donors after neuroprognostication.

CONCLUSIONS

The implementation of a multidisciplinary ECPR program in an experienced Extracorporeal Membrane Oxygenation center in Spain is feasible and can lead to good survival outcomes and valid organ donors.

Neurological monitoring and management for adult extracorporeal membrane oxygenation patients: Extracorporeal Life Support Organization consensus guidelines

BACKGROUND

Critical care of patients on extracorporeal membrane oxygenation (ECMO) with acute brain injury (ABI) is notable for a lack of high-quality clinical evidence. Here, we offer guidelines for neurological care (neurological monitoring and management) of adults during and after ECMO support.

METHODS

These guidelines are based on clinical practice consensus recommendations and scientific statements. We convened an international multidisciplinary consensus panel including 30 clinician-scientists with expertise in ECMO from all chapters of the Extracorporeal Life Support Organization (ELSO). We used a modified Delphi process with three rounds of voting and asked panelists to assess the recommendation levels.

RESULTS

We identified five key clinical areas needing guidance: (1) neurological monitoring, (2) post-cannulation early physiological targets and ABI, (3) neurological therapy including medical and surgical intervention, (4) neurological prognostication, and (5) neurological follow-up and outcomes. The consensus produced 30 statements and recommendations regarding key clinical areas. We identified several knowledge gaps to shape future research efforts.

CONCLUSIONS

The impact of ABI on morbidity and mortality in ECMO patients is significant. Particularly, early detection and timely intervention are crucial for improving outcomes. These consensus recommendations and scientific statements serve to guide the neurological monitoring and prevention of ABI, and management strategy of ECMO-associated ABI.

A practical magnetic-resonance imaging score for outcome prediction in comatose cardiac arrest survivors

AIM

Magnetic Resonance Imaging (MRI) is an important prognostic tool in cardiac arrest (CA) survivors given its sensitivity for detecting hypoxic-ischemic brain injury (HIBI), however, it is limited by poorly defined objective thresholds. To address this limitation, we evaluated a qualitative MRI score for predicting neurological outcome in CA survivors.

METHODS

Adult comatose CA survivors who underwent MRI were retrospectively identified at a single academic medical center. Two blinded neurointensivists qualitatively scored HIBI amongst 12 MRI brain regions. Scores were summated to form four distinct score groups: cortex, deep grey nuclei (DGN), cortex-DGN combined, and total (cortex, DGN, brainstem, and cerebellum). Poor neurological outcome was defined as Cerebral Performance Category (CPC) score 3-5 at hospital discharge. Inter-rater reliability was tested using intra-class correlation (ICC) and discrimination of poor neurological outcome assessed using area under the receiver operating curve (AUC).

RESULTS

Our cohort included 219 patients with median time to MRI of 96 (IQR 81-110) hours. ICC (95% CI) was good to excellent across all MRI scores: cortex 0.92 (0.89-0.94), DGN 0.88 (0.80-0.92), cortex-DGN 0.94 (0.92-0.95), and total 0.93 (0.91-0.95). AUC (95% CI) for poor outcome was good across all MRI scores: cortex 0.84 (0.78-0.90), DGN 0.83 (0.77-0.89), cortex-DGN 0.83 (0.77-0.89), and total 0.83 (0.77-0.88).

CONCLUSION

A simplified, qualitative MRI score had excellent reliability and good discrimination for poor neurologic outcome. Further work is necessary to externally validate our findings in an independent, ideally prospective, cohort.

Extracorporeal Cardiopulmonary Resuscitation: Life-saving or Resource Wasting?

The morbidity and mortality for patients having a cardiac arrest is substantial. Even if optimally performed, conventional cardiopulmonary resuscitation is an inadequate substitute for native cardiac output and results in a 'low-flow' perfusion state. Venoarterial extracorporeal membrane oxygenation during cardiac arrest, also known as extracorporeal cardiopulmonary resuscitation (eCPR), has been proposed as an alternative to restore systemic perfusion. However, conflicting results regarding its efficacy compared to routine advanced cardiac life support have left its role in clinical practice uncertain. In this article, the merits and limitations of the existing data for eCPR are reviewed in a 'point- counterpoint' style debate, followed by potential considerations for future trials.

Markers of Mitochondrial Injury and Neurological Outcomes of Comatose Patients after Cardiac Arrest

Background and Objectives: Most patients who are successfully resuscitated from cardiac arrest remain comatose, and only half regain consciousness 72 h after the arrest. Neuroprognostication methods can be complex and even inconclusive. As mitochondrial components have been identified as markers of post-cardiac-arrest injury and associated with survival, we aimed to investigate cytochrome c and mtDNA in comatose patients after cardiac arrest to compare neurological outcomes and to evaluate the markers' neuroprognostic value. Materials and Methods: This prospective observational study included 86 comatose post-cardiac-arrest patients and 10 healthy controls. Cytochrome c and mtDNA were determined at admission. Neuron-specific enolase (NSE) was measured after 72 h. Additional neuroprognostication methods were performed when patients remained unconscious. Cerebral performance category (CPC) was determined. Results: Cytochrome c was elevated in patients compared to healthy controls (2.029 [0.85-4.97] ng/mL vs. 0 [0.0-0.16], p < 0.001) but not mtDNA (95,228 [52,566-194,060] vs. 41,466 [28,199-104,708] copies/μL, p = 0.074). Compared to patients with CPC 1-2, patients with CPC 3-5 had higher cytochrome c (1.735 [0.717-3.40] vs. 4.109 [1.149-8.457] ng/mL, p = 0.011), with no differences in mtDNA (87,855 [47,598-172,464] vs. 126,452 [69,447-260,334] copies/μL, p = 0.208). Patients with CPC 1-2 and CPC 3-5 differed in all neuroprognostication methods. In patients with good vs. poor neurological outcome, ROC AUC was 0.664 (p = 0.011) for cytochrome c, 0.582 (p = 0.208) for mtDNA, and 0.860 (p < 0.001) for NSE. The correlation between NSE and cytochrome c was moderate, with a coefficient of 0.576 (p < 0.001). Conclusions: Cytochrome c was higher in comatose patients after cardiac arrest compared to healthy controls and higher in post-cardiac-arrest patients with poor neurological outcomes. Although cytochrome c correlated with NSE, its neuroprognostic value was poor. We found no differences in mtDNA.

Detecting awareness after acute brain injury

Advances over the past two decades in functional neuroimaging have provided new diagnostic and prognostic tools for patients with severe brain injury. Some of the most pertinent developments in this area involve the assessment of residual brain function in patients in the intensive care unit during the acute phase of severe injury, when they are at their most vulnerable and prognosis is uncertain. Advanced neuroimaging techniques, such as functional MRI and EEG, have now been used to identify preserved cognitive processing, including covert conscious awareness, and to relate them to outcome in patients who are behaviourally unresponsive. Yet, technical and logistical challenges to clinical integration of these advanced neuroimaging techniques remain, such as the need for specialised expertise to acquire, analyse, and interpret data and to determine the appropriate timing for such assessments. Once these barriers are overcome, advanced functional neuroimaging technologies could improve diagnosis and prognosis for millions of patients worldwide.

Beliefs of physician directors on the management of devastating brain injuries at the Canadian emergency department and intensive care unit interface: a national site-level survey

PURPOSE

Insufficient evidence-based recommendations to guide care for patients with devastating brain injuries (DBIs) leave patients vulnerable to inconsistent practice at the emergency department (ED) and intensive care unit (ICU) interface. We sought to characterize the beliefs of Canadian emergency medicine (EM) and critical care medicine (CCM) physician site directors regarding current management practices for patients with DBI.

METHODS

We conducted a cross-sectional survey of EM and CCM physician directors of adult EDs and ICUs across Canada (December 2022 to March 2023). Our primary outcome was the proportion of respondents who manage (or consult on) patients with DBI in the ED. We conducted subgroup analyses to compare beliefs of EM and CCM physicians.

RESULTS

Of 303 eligible respondents, we received 98 (32%) completed surveys (EM physician directors, 46; CCM physician directors, 52). Most physician directors reported participating in the decision to withdraw life-sustaining measures (WLSM) for patients with DBI in the ED (80%, n = 78), but 63% of these (n = 62) said this was infrequent. Physician directors reported that existing neuroprognostication methods are rarely sufficient to support WLSM in the ED (49%, n = 48) and believed that an ICU stay is required to improve confidence (99%, n = 97). Most (96%, n = 94) felt that providing caregiver visitation time prior to WLSM was a valid reason for ICU admission.

CONCLUSION

In our survey of Canadian EM and CCM physician directors, 80% participated in WLSM in the ED for patients with DBI. Despite this, most supported ICU admission to optimize neuroprognostication and patient-centred end-of-life care, including organ donation.

Emerging Evidence in Out-of-Hospital Cardiac Arrest-A Critical Appraisal of the Cardiac Arrest Center

The morbidity and mortality of out-of-hospital cardiac arrest (OHCA) due to presumed cardiac causes have remained unwaveringly high over the last few decades. Less than 10% of patients survive until hospital discharge. Treatment of OHCA patients has traditionally relied on expert opinions. However, there is growing evidence on managing OHCA patients favorably during the prehospital phase, coronary and intensive care, and even beyond hospital discharge. To improve outcomes in OHCA, experts have proposed the establishment of cardiac arrest centers (CACs) as pivotal elements. CACs are expert facilities that pool resources and staff, provide infrastructure, treatment pathways, and networks to deliver comprehensive and guideline-recommended post-cardiac arrest care, as well as promote research. This review aims to address knowledge gaps in the 2020 consensus on CACs of major European medical associations, considering novel evidence on critical issues in both pre- and in-hospital OHCA management, such as the timing of coronary angiography and the use of extracorporeal cardiopulmonary resuscitation (eCPR). The goal is to harmonize new evidence with the concept of CACs.

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest - A Novel Biomarker for Neuroprognostication

BACKGROUND

Selective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker.

METHODS

We conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort.

RESULTS

We included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86-100%) and 43% (moderate) sensitivity (95%-CI: 31-56%).

CONCLUSION

This pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study

PURPOSE

Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest.

METHODS

Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated.

RESULTS

140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR.

CONCLUSION

Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.

Ferroptosis-related gene MAPK3 is associated with the neurological outcome after cardiac arrest

BACKGROUND

Neuronal ferroptosis is closely related to the disease of the nervous system, and the objective of the present study was to recognize and verify the potential ferroptosis-related genes to forecast the neurological outcome after cardiac arrest.

METHODS

Cardiac Arrest-related microarray datasets GSE29540 and GSE92696 were downloaded from GEO and batch normalization of the expression data was performed using "sva" of the R package. GSE29540 was analyzed to identify DEGs. Venn diagram was applied to recognize ferroptosis-related DEGs from the DEGs. Subsequently, The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed, and PPI network was applied to screen hub genes. Receiver operating characteristic (ROC) curves were adopted to determine the predictive value of the biomarkers, and the GSE92696 dataset was applied to further evaluate the diagnostic efficacy of the biomarkers. We explore transcription factors and miRNAs associated with hub genes. The "CIBERSORT" package of R was utilized to analyse the proportion infiltrating immune cells. Finally, validated by a series of experiments at the cellular level.

RESULTS

112 overlapping ferroptosis-related DEGs were further obtained via intersecting these DEGs and ferroptosis-related genes. The GO and KEGG analysis demonstrate that ferroptosis-related DEGs are mainly involved in response to oxidative stress, ferroptosis, apoptosis, IL-17 signalling pathway, autophagy, toll-like receptor signalling pathway. The top 10 hub genes were selected, including HIF1A, MAPK3, PPARA, IL1B, PTGS2, RELA, TLR4, KEAP1, SREBF1, SIRT6. Only MAPK3 was upregulated in both GSE29540 and GAE92696. The AUC values of the MAPK3 are 0.654 and 0.850 in GSE29540 and GSE92696 respectively. The result of miRNAs associated with hub genes indicates that hsa-miR-214-3p and hsa-miR-483-5p can regulate the expression of MAPK3. MAPK3 was positively correlated with naive B cells, macrophages M0, activated dendritic cells and negatively correlated with activated CD4 memory T cells, CD8 T cells, and memory B cells. Compared to the OGD4/R24 group, the OGD4/R12 group had higher MAPK3 expression at both mRNA and protein levels and more severe ferroptosis.

CONCLUSION

In summary, the MAPK3 ferroptosis-related gene could be used as a biomarker to predict the neurological outcome after cardiac arrest. Potential biological pathways provide novel insights into the pathogenesis of cardiac arrest.

Prognostication in Neurocritical Care

OBJECTIVE

This article synthesizes the current literature on prognostication in neurocritical care, identifies existing challenges, and proposes future research directions to reduce variability and enhance scientific and patient-centered approaches to neuroprognostication.

LATEST DEVELOPMENTS

Patients with severe acute brain injury often lack the capacity to make their own medical decisions, leaving surrogate decision makers responsible for life-or-death choices. These decisions heavily rely on clinicians' prognostication, which is still considered an art because of the previous lack of specific guidelines. Consequently, there is significant variability in neuroprognostication practices. This article examines various aspects of neuroprognostication. It explores the cognitive approach to prognostication, highlights the use of statistical modeling such as Bayesian models and machine learning, emphasizes the importance of clinician-family communication during prognostic disclosures, and proposes shared decision making for more patient-centered care.

ESSENTIAL POINTS

This article identifies ongoing challenges in the field and emphasizes the need for future research to ameliorate variability in neuroprognostication. By focusing on scientific methodologies and patient-centered approaches, this research aims to provide guidance and tools that may enhance neuroprognostication in neurocritical care.

Update in Pediatric Neurocritical Care: What a Neurologist Caring for Critically Ill Children Needs to Know

Currently nearly one-quarter of admissions to pediatric intensive care units (PICUs) worldwide are for neurocritical care diagnoses that are associated with significant morbidity and mortality. Pediatric neurocritical care is a rapidly evolving field with unique challenges due to not only age-related responses to primary neurologic insults and their treatments but also the rarity of pediatric neurocritical care conditions at any given institution. The structure of pediatric neurocritical care services therefore is most commonly a collaborative model where critical care medicine physicians coordinate care and are supported by a multidisciplinary team of pediatric subspecialists, including neurologists. While pediatric neurocritical care lies at the intersection between critical care and the neurosciences, this narrative review focuses on the most common clinical scenarios encountered by pediatric neurologists as consultants in the PICU and synthesizes the recent evidence, best practices, and ongoing research in these cases. We provide an in-depth review of (1) the evaluation and management of abnormal movements (seizures/status epilepticus and status dystonicus); (2) acute weakness and paralysis (focusing on pediatric stroke and select pediatric neuroimmune conditions); (3) neuromonitoring modalities using a pathophysiology-driven approach; (4) neuroprotective strategies for which there is evidence (e.g., pediatric severe traumatic brain injury, post-cardiac arrest care, and ischemic stroke and hemorrhagic stroke); and (5) best practices for neuroprognostication in pediatric traumatic brain injury, cardiac arrest, and disorders of consciousness, with highlights of the 2023 updates on Brain Death/Death by Neurological Criteria. Our review of the current state of pediatric neurocritical care from the viewpoint of what a pediatric neurologist in the PICU needs to know is intended to improve knowledge for providers at the bedside with the goal of better patient care and outcomes.

Emergent Management of Hypoxic-Ischemic Brain Injury

OBJECTIVE

This article outlines interventions used to improve outcomes for patients with hypoxic-ischemic brain injury after cardiac arrest.

LATEST DEVELOPMENTS

Emergent management of patients after cardiac arrest requires prevention and treatment of primary and secondary brain injury. Primary brain injury is minimized by excellent initial resuscitative efforts. Secondary brain injury prevention requires the detection and correction of many pathophysiologic processes that may develop in the hours to days after the initial arrest. Key physiologic parameters important to secondary brain injury prevention include optimization of mean arterial pressure, cerebral perfusion, oxygenation and ventilation, intracranial pressure, temperature, and cortical hyperexcitability. This article outlines recent data regarding the treatment and prevention of secondary brain injury. Different patients likely benefit from different treatment strategies, so an individualized approach to treatment and prevention of secondary brain injury is advisable. Clinicians must use multimodal sources of data to prognosticate outcomes after cardiac arrest while recognizing that all prognostic tools have shortcomings.

ESSENTIAL POINTS

Neurologists should be involved in the postarrest care of patients with hypoxic-ischemic brain injury to improve their outcomes. Postarrest care requires nuanced and patient-centered approaches to the prevention and treatment of primary and secondary brain injury and neuroprognostication.

Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial

PURPOSE

Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial.

METHODS

Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices.

RESULTS

We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially.

CONCLUSION

Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Year in Review 2023: Noteworthy Literature in Cardiothoracic Critical Care

This article reviews noteworthy investigations and society recommendations published in 2023 relevant to the care of critically ill cardiothoracic surgical patients. We reviewed 3,214 articles to identify 18 publications that add to the existing literature across a variety of topics including resuscitation, nutrition, antibiotic management, extracorporeal membrane oxygenation (ECMO), neurologic care following cardiac arrest, coagulopathy and transfusion, steroids in pulmonary infections, and updated guidelines in the management of acute respiratory distress syndrome (ARDS).

Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest

BACKGROUND

The survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes.

OBJECTIVES

To establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest.

METHODS

We enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1-2) or poor (CPC 3-5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models.

RESULTS

Model 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4.

CONCLUSIONS

By integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.