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The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been recognized as predictors of various critical illnesses. Our study aimed to investigate whether the NLR and PLR measured at different timepoints could predict poor neurological outcomes at 6 months. This observational retrospective cohort study included adults who had experienced out-of-hospital cardiac arrest (OHCA) and received targeted temperature management between November 2015 and December 2020. Patients with an active infection, as confirmed by an initial blood culture, were excluded. Multivariate logistic regression models were used to determine the association between the NLR and PLR at 0, 24, and 48 h after return of spontaneous circulation and poor neurological outcomes, defined as a Cerebral Performance Category score of ≥3 at 6 months. The NLR at 24 h, but not the NLR or PLR at other timepoints, was significantly associated with poor neurological outcomes (odds ratio: 1.05; 95% CI: 1.01–1.09; p = 0.018). The NLR at 24 h showed moderate accuracy in predicting poor neurological outcomes, with an AUC of 0.619. A cutoff value of 9.0 achieved 72.5% sensitivity and 47.7% specificity. The NLR measured at 24 h after ROCS could be used for early neuroprognostication given its low cost and widespread availability. [ABSTRACT FROM AUTHOR]

Purpose: Out-of-hospital cardiac arrest (OHCA) survivors face significant risks of complications and death from hypoxic–ischemic brain injury leading to withdrawal of life-sustaining treatment (WLST). Accurate multimodal neuroprognostication, including automated pupillometry, is essential to avoid inappropriate WLST. However, inconsistent study results hinder standardized threshold recommendations. We aimed to validate proposed pupillometry thresholds with no false predictions of unfavorable outcomes in comatose OHCA survivors. Methods: In the multi-center BOX-trial, quantitative measurements of automated pupillometry (quantitatively assessed pupillary light reflex [qPLR] and Neurological Pupil index [NPi]) were obtained at admission (0 h) and after 24, 48, and 72 h in comatose patients resuscitated from OHCA. We aimed to validate qPLR < 4% and NPi ≤ 2, predicting unfavorable neurological conditions defined as Cerebral Performance Category 3–5 at follow-up. Combined with 48-h neuron-specific enolase (NSE) > 60 μg/L, pupillometry was evaluated for multimodal neuroprognostication in comatose patients with Glasgow Motor Score (M) ≤ 3 at ≥ 72 h. Results: From March 2017 to December 2021, we consecutively enrolled 710 OHCA survivors (mean age: 63 ± 14 years; 82% males), and 266 (37%) patients had unfavorable neurological outcomes. An NPi ≤ 2 predicted outcome with 0% false-positive rate (FPR) at all time points (0–72 h), and qPLR < 4% at 24–72 h. In patients with M ≤ 3 at ≥ 72 h, pupillometry thresholds significantly increased the sensitivity of NSE, from 42% (35–51%) to 55% (47–63%) for qPLR and 50% (42–58%) for NPi, maintaining 0% (0–0%) FPR. Conclusion: Quantitative pupillometry thresholds predict unfavorable neurological outcomes in comatose OHCA survivors and increase the sensitivity of NSE in a multimodal approach at ≥ 72 h. [ABSTRACT FROM AUTHOR]

Cardiac arrest is the most important cause of death worldwide. Often, those who survive have an increased mortality and disability risk that is mainly associated with the development of hypoxic- ischemic brain injury (HIBI). This review examines current methods and recent advancements in neuroprognostication after cardiac arrest, focusing on the multimodal approach recommended by current guidelines. Recent studies have shown that a multimodal approach for neuroprognostication has the highest specificity to determine unfavorable outcomes after cardiac arrest. New biomarkers, such as neurofilament light chain alongside advancements in machine learning models, have shown promising results in predicting outcomes. Although several prognostic scoring systems have been developed to predict neurological outcomes as early as hospital admission, their prognostic efficacy is still being determined due to several associated limitations. Although several strategies for improving neurological outcomes during and after cardiac arrest exist, HIBI remains the leading cause of disability among survivors. A multimodal approach, including at least two diagnostic modalities, is crucial for accurate prognostication. Emerging technologies, including machine learning models and biomarkers, offer potential improvements to existing prognostic strategies, emphasizing the need for consistent guideline adherence to optimize patient care. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Background: Despite advances in resuscitation practice, patient survival following cardiac arrest remains poor. The utilization of MRI in neurological outcome prognostication post-cardiac arrest is growing and various classifications has been proposed; however a consensus has yet to be established. MRI, though valuable, is resource-intensive, time-consuming, costly, and not universally available. This study aims to validate a MRI lesion pattern score in a cohort of out of hospital cardiac arrest patients at a tertiary referral hospital in Switzerland. Methods: This cohort study spanned twelve months from February 2021 to January 2022, encompassing all unconscious patients aged ≥ 18 years who experienced out-of-hospital cardiac arrest of any cause and were admitted to the intensive care unit (ICU) at Inselspital, University Hospital Bern, Switzerland. We included patients who underwent the neuroprognostication process, assessing the performance and validation of a MRI scoring system. Results: Over the twelve-month period, 137 patients were admitted to the ICU, with 52 entering the neuroprognostication process and 47 undergoing MRI analysis. Among the 35 MRIs indicating severe hypoxic brain injury, 33 patients (94%) experienced an unfavourable outcome (UO), while ten (83%) of the twelve patients with no or minimal MRI lesions had a favourable outcome. This yielded a sensitivity of 0.94 and specificity of 0.83 for predicting UO with the proposed MRI scoring system. The positive and negative likelihood ratios were 5.53 and 0.07, respectively, resulting in an accuracy of 91.49%. Conclusion: We demonstrated the effectiveness of the MLP scoring scheme in predicting neurological outcome in patients following cardiac arrest. However, to ensure a comprehensive neuroprognostication, MRI results need to be combined with other assessments. While neuroimaging is a promising objective tool for neuroprognostication, given the absence of sedation-related confounders—compared to electroencephalogram (EEG) and clinical examination—the current lack of a validated scoring system necessitates further studies. Incorporating standardized MRI techniques and grading systems is crucial for advancing the reliability of neuroimaging for neuroprognostication. Trial Registration: Registry of all Projects in Switzerland (RAPS) 2020-01761. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Children with severe traumatic brain injury (sTBI) are at risk for neurological sequelae impacting function. Clinicians are tasked with neuroprognostication to assist in decision-making. We describe a single-center study assessing clinicians' neuroprognostication accuracy. Clinicians of various specialties caring for children with sTBI were asked to predict their patients' functioning three to six months postinjury. Clinicians were asked to participate in the study if their patient had survived but not returned to baseline between day 4 and 7 postinjury. The outcome tool utilized was the functional status scale (FSS), ranging from 6 to 30 (best-worst function). Predicted scores were compared with actual scores three to six months postinjury. Lin concordance correlation coefficients were used to estimate agreement between predicted and actual FSS. Outcome was dichotomized as good (FSS 6 to 8) or poor (FSS ≥9). Positive and negative predictive values for poor outcome were calculated. Pessimistic prognostic prediction was defined as predicted worse outcome by ≥3 FSS points. Demographic and clinical variables were collected. A total of 107 surveys were collected on 24 patients. Two children died. Fifteen children had complete (FSS = 6) or near-complete (FSS = 7) recovery. Mean predicted and actual FSS scores were 10.8 (S.D. 5.6) and 8.6 (S.D. 4.1), respectively. Predicted FSS scores were higher than actual scores (P < 0.001). Eight children had collective pessimistic prognostic prediction. Clinicians predicted worse functional outcomes, despite high percentage of patients with near-normal function at follow-up clinic. Certain patient and provider factors were noted to impact accuracy and need to be studied in larger cohorts. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the prevalence of delayed deterioration of electroencephalogram (EEG) in patients with cardiac arrest (CA) without early highly malignant patterns and to determine their associations with clinical findings. Methods: This was a retrospective study of adult patients with CA admitted to the intensive care unit (ICU) of a university hospital. We included all patients with CA who had a normal voltage EEG, no more than 10% discontinuity, and absence of sporadic epileptic discharges, periodic discharges, or electrographic seizures. Delayed deterioration was classified as the following: (1) epileptic deterioration, defined as the appearance, at least 24 h after CA, of sporadic epileptic discharges, periodic discharges, and status epilepticus; or (2) background deterioration, defined as increasing discontinuity or progressive attenuation of the background at least 24 h after CA. The end points were the incidence of EEG deteriorations and their association with clinical features and ICU mortality. Results: We enrolled 188 patients in the analysis. The ICU mortality was 46%. Overall, 30 (16%) patients presented with epileptic deterioration and 9 (5%) patients presented with background deterioration; of those, two patients presented both deteriorations. Patients with epileptic deterioration more frequently had an out-of-hospital CA, and higher time to return of spontaneous circulation and less frequently had bystander resuscitation than others. Patients with background deterioration showed a predominantly noncardiac cause, more frequently developed shock, and had multiple organ failure compared with others. Patients with epileptic deterioration presented with a higher ICU mortality (77% vs. 41%; p < 0.01) than others, whereas all patients with background deterioration died in the ICU. Conclusions: Delayed EEG deterioration was associated with high mortality rate. Epileptic deterioration was associated with worse characteristics of CA, whereas background deterioration was associated with shock and multiple organ failure. [ABSTRACT FROM AUTHOR]

The article discusses outcome prediction after cardiac arrest and the potential for self-fulfilling prophecies in medical care. It presents guidelines for neuroprognostication in unconscious cardiac arrest survivors and the findings of a retrospective study that reassessed the entry point for the algorithm. The study found a false-positive rate of 0% when applied to different patient populations but raises concerns about self-fulfilling prophecies and the need for further research. Another study found that over one-third of patients were alive and independent at the 6-month follow-up, indicating that therapeutic nihilism is misguided. The authors suggest implementing guideline-concordant neuroprognostication timed no earlier than 72 hours postarrest. [Extracted from the article]

OBJECTIVES: To explore broadened entry criteria of the 2021 European Resuscitation Council/European Society of Intensive Care Medicine (ERC/ESICM) algorithm for neuroprognostication including patients with ongoing sedation and Glasgow Coma Scale-Motor score (GCS-M) scores 4-5. DESIGN: Retrospective multicenter observational study. SETTING: Four ICUs, Skane, Sweden. PATIENTS: Postcardiac arrest patients managed at targeted temperature 36°C, 2014-2018. Neurologic outcome was assessed after 2-6 months according to the Cerebral Performance Category scale. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In 794 included patients, median age was 69.5 years (interquartile range, 60.6-77.0 yr), 241 (30.4%) were female, 550 (69.3%) had an out-of-hospital cardiac arrest, and 314 (41.3%) had a shockable rhythm. Four hundred ninety-five patients were dead at follow-up, 330 of 495 died after a decision on withdrawal of life-sustaining therapies. At 72 hours after cardiac arrest 218 patients remained unconscious. The entry criteria of the original algorithm (GCS-M 1-3) was fulfilled by 163 patients and 115 patients with poor outcome were identified, with false positive rate (FPR) of 0% (95% CI, 0-79.4%) and sensitivity of 71.0% (95% CI, 63.6-77.4%). Inclusion of patients with ongoing sedation identified another 13 patients with poor outcome, generating FPR of 0% (95% CI, 0-65.8%) and sensitivity of 69.6% (95% CI, 62.6-75.8%). Inclusion of all unconscious patients (GCS-M 1-5), regardless of sedation, identified one additional patient, generating FPR of 0% (95% CI, 0-22.8) and sensitivity of 62.9% (95% CI, 56.1-69.2). The few patients with true negative prediction (patients with good outcome not fulfilling guideline criteria of a poor outcome) generated wide 95% CI for FPR. CONCLUSION: The 2021 ERC/ESICM algorithm for neuroprognostication predicted poor neurologic outcome with a FPR of 0%. Broadening inclusion criteria to include all unconscious patients regardless of ongoing sedation identified an additional small number of patients with poor outcome but did not affect the FPR. Results are limited by high rate of withdrawal of life-sustaining therapies and few patients with true negative prediction. [ABSTRACT FROM AUTHOR]

The article examines the use of ChatGPT, a chatbot powered by a language model, in predicting outcomes for patients with traumatic brain injury (TBI). The study found that ChatGPT's predictions were less accurate than those of human clinicians and a validated prognostic model. The chatbot tended to overestimate the likelihood of poor outcomes, which could have negative implications for patient care. The article also discusses the limitations and ethical considerations of using AI models like ChatGPT in clinical decision-making. It references several other articles that explore the potential and limitations of AI in various medical applications, including glioma adjuvant therapy, drug information, and prognosis assessment in depression. However, caution and further research are advised when considering the use of AI in healthcare. [Extracted from the article]

Background: International guidelines for neuroprotection following out-of-hospital cardiac arrest (OHCA) recommend fever prevention ahead of routine temperature management. This study aimed to identify any effect of changing from targeted temperature management to fever prevention on neurological outcome following OHCA. Methods: A retrospective observational cohort study was conducted of consecutive admissions to an ICU at a tertiary OHCA centre. Comparison was made between a period of protocolised targeted temperature management (TTM) to 36 °C and a period of fever prevention. Results: Data were available for 183 patients. Active temperature management was administered in 86/118 (72%) of the TTM cohort and 20/65 (31%) of the fever prevention group. The median highest temperature prior to the start of temperature management was significantly lower in the TTM group at 35.6 (IQR 34.9–36.2) compared to 37.9 °C (IQR 37.7–38.2) in the fever prevention group (adjusted p

Long-term outcome studies after pediatric cardiac arrest (CA) are few. They require a CA registry and dedicated outcome teams. Learning about the long-term outcomes is very important for developing prognostication guidelines, improving post-cardiac care, counseling caregivers about the future of their child, and creating opportunities for therapeutic intervention studies to improve outcomes.Few PICUs worldwide provide a multidisciplinary follow-up program as routine practice at an outpatient clinic with standardized measurements, using validated instruments including neuropsychological assessments by psychologists. The primary goal of such a follow-up program should be to provide excellent care to children and their caregivers, thereby resulting in a high attendance. Pediatric psychologists, neurologists and pediatricians/pediatric intensivists should ideally be involved to screen for delayed development and psychosocial problems and offer appropriate care at the same time. Preferably, outcomes should consist of evaluation of morbidity (physical and neuropsychological), functional health and Health Related Quality Of Life (QoL) of the patient and their caregivers.

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.

Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical "life-or-death" decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools. [ABSTRACT FROM AUTHOR]

Prediction of neurological clinical outcome after acute brain injury is critical because it helps guide discussions with patients and families and informs treatment plans and allocation of resources. Numerous clinical grading scales have been published that aim to support prognostication after acute brain injury. However, the development and validation of clinical scales lack a standardized approach. This in turn makes it difficult for clinicians to rely on prognostic grading scales and to integrate them into clinical practice. In this review, we discuss quality measures of score development and validation and summarize available scales to prognosticate outcomes after acute brain injury. These include scales developed for patients with coma, cardiac arrest, ischemic stroke, nontraumatic intracerebral hemorrhage, subarachnoid hemorrhage, and traumatic brain injury; for each scale, we discuss available validation studies. [ABSTRACT FROM AUTHOR]

Cardiac arrest (CA) afflicts ~ 550,000 people each year in the USA. A small fraction of CA sufferers survive with a majority of these survivors emerging in a comatose state. Many CA survivors suffer devastating global brain injury with some remaining indefinitely in a comatose state. The pathogenesis of global brain injury secondary to CA is complex. Mechanisms of CA-induced brain injury include ischemia, hypoxia, cytotoxicity, inflammation, and ultimately, irreversible neuronal damage. Due to this complexity, it is critical for clinicians to have access as early as possible to quantitative metrics for diagnosing injury severity, accurately predicting outcome, and informing patient care. Current recommendations involve using multiple modalities including clinical exam, electrophysiology, brain imaging, and molecular biomarkers. This multi-faceted approach is designed to improve prognostication to avoid "self-fulfilling" prophecy and early withdrawal of life-sustaining treatments. Incorporation of emerging dynamic monitoring tools such as diffuse optical technologies may provide improved diagnosis and early prognostication to better inform treatment. Currently, targeted temperature management (TTM) is the leading treatment, with the number of patients needed to treat being ~ 6 in order to improve outcome for one patient. Future avenues of treatment, which may potentially be combined with TTM, include pharmacotherapy, perfusion/oxygenation targets, and pre/postconditioning. In this review, we provide a bench to bedside approach to delineate the pathophysiology, prognostication methods, current targeted therapies, and future directions of research surrounding hypoxic-ischemic brain injury (HIBI) secondary to CA.

Abstract About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient’s relatives, avoid disproportionate care in patients with irreversible hypoxic–ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as “poor outcome likely” in 32%, the outcome remaining “indeterminate” in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to “highly malignant” patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.

Background: The functional prognosis of severe traumatic brain injury (TBI) during the acute phase is often poor and uncertain. We aimed to quantify the elements that shade the degree of uncertainty in prognostic determination of TBI and to better understand the role of clinical experience in prognostic quality. Methods: This was an observational, prospective, multicenter study. The medical records of 16 patients with moderate or severe TBI in 2020 were randomly drawn from a previous study and submitted to two groups of physicians: senior and junior. The senior physician group had graduated from a critical care fellowship, and the junior physician group had at least 3 years of anesthesia and critical care residency. They were asked for each patient, based on the reading of clinical data and CT images of the first 24 h, to determine the probability of an unfavorable outcome (Glasgow Outcome Scale < 4) at 6 months between 0 and 100, and their level of confidence. These estimations were compared with the actual evolution. Results: Eighteen senior physicians and 18 junior physicians in 4 neuro-intensive care units were included in 2021. We observed that senior physicians performed better than junior physicians, with 73% (95% confidence interval (CI) 65–79) and 62% (95% CI 56–67) correct predictions, respectively, in the senior and junior groups (p = 0.006). The risk factors for incorrect prediction were junior group (OR 1.71, 95% CI 1.15–2.55), low confidence in the estimation (OR 1.76, 95% CI 1.18–2.63), and low level of agreement on prediction between senior physicians (OR 6.78, 95% CI 3.45–13.35). Conclusions: Determining functional prognosis in the acute phase of severe TBI involves uncertainty. This uncertainty should be modulated by the experience and confidence of the physician, and especially on the degree of agreement between physicians. [ABSTRACT FROM AUTHOR]

Neuroprognostication in severe traumatic brain injury (sTBI) is challenging and occurs in critical care settings to determine withdrawal of life-sustaining therapies (WLST). However, formal pediatric sTBI neuroprognostication guidelines are lacking, brain death criteria vary, and dilemmas regarding WLST persist, which lead to institutional differences. We studied WLST practice and outcome in pediatric sTBI to provide insight into WLST-associated factors and survivor recovery trajectory ≥1 year post-sTBI. This retrospective, single center observational study included patients <18 years admitted to the pediatric intensive care unit (PICU) of Erasmus MC-Sophia (a tertiary university hospital) between 2012 and 2020 with sTBI defined as a Glasgow Coma Scale (GCS) ≤8 and requiring intracranial pressure (ICP) monitoring. Clinical, neuroimaging, and electroencephalogram data were reviewed. Multi-disciplinary follow-up included the Pediatric Cerebral Performance Category (PCPC) score, educational level, and commonly cited complaints. Seventy-eight children with sTBI were included (median age 10.5 years; interquartile range [IQR] 5.0-14.1; 56% male; 67% traffic-related accidents). Median ICP monitoring was 5 days (IQR 3-8), 19 (24%) underwent decompressive craniectomy. PICU mortality was 21% (16/78): clinical brain death (5/16), WLST due to poor neurological prognosis (WLST_neuro, 11/16). Significant differences (p < 0.001) between survivors and non-survivors: first GCS score, first pupillary reaction and first lactate, Injury Severity Score, pre-hospital cardiopulmonary resuscitation, and Rotterdam CT (computed tomography) score. WLST_neuro decision timing ranged from 0 to 31 days (median 2 days, IQR 0-5). WLST_neuro decision (n = 11) was based on neurologic examination (100%), brain imaging (100%) and refractory intracranial hypertension (5/11; 45%). WLST discussions were multi-disciplinary with 100% agreement. Immediate agreement between medical team and caregivers was 81%. The majority (42/62, 68%) of survivors were poor outcome (PCPC score 3 to 5) at PICU discharge, of which 12 (19%) in a vegetative state. One year post-injury, no patients were in a vegetative state and the median PCPC score had improved to 2 (IQR 2-3). No patients died after PICU discharge. Twenty percent of survivors could not attend school 2 years post-injury. Survivors requiring an adjusted educational level increased to 45% within this timeframe. Chronic complaints were headache, behavioral problems, and sleeping problems. In conclusion, two-thirds of sTBI PICU mortality was secondary to WLST_neuro and occurred early post-injury. Median survivor PCPC score improved from 4 to 2 with no vegetative patients 1 year post-sTBI. Our findings show the WLST decision process was multi-disciplinary and guided by specific clinical features at presentation, clinical course, and (serial) neurological diagnostic modalities, of which the testing combination was determined by case-to-case variation. This stresses the need for international guidelines to provide accurate neuroprognostication within an appropriate timeframe whereby overall survivor outcome data provides valuable context and guidance in the acute phase decision process. [ABSTRACT FROM AUTHOR]

Abstract Background To assess in comatose patients after cardiac arrest (CA) if amplitudes of two somatosensory evoked potentials (SSEP) responses, namely, N20-baseline (N20-b) and N20–P25, are predictive of neurological outcome. Methods Monocentric prospective study in a tertiary cardiac center between Nov 2019 and July-2021. All patients comatose at 72 h after CA with at least one SSEP recorded were included. The N20-b and N20–P25 amplitudes were automatically measured in microvolts (µV), along with other recommended prognostic markers (status myoclonus, neuron-specific enolase levels at 2 and 3 days, and EEG pattern). We assessed the predictive value of SSEP for neurologic outcome using the best Cerebral Performance Categories (CPC1 or 2 as good outcome) at 3 months (main endpoint) and 6 months (secondary endpoint). Specificity and sensitivity of different thresholds of SSEP amplitudes, alone or in combination with other prognostic markers, were calculated. Results Among 82 patients, a poor outcome (CPC 3–5) was observed in 78% of patients at 3 months. The median time to SSEP recording was 3(2–4) days after CA, with a pattern “bilaterally absent” in 19 patients, “unilaterally present” in 4, and “bilaterally present” in 59 patients. The median N20-b amplitudes were different between patients with poor and good outcomes, i.e., 0.93 [0–2.05]µV vs. 1.56 [1.24–2.75]µV, respectively (p 2 µV predicted good outcome with a specificity of 73% and a moderate sensitivity of 39%, although an N20–P25 > 3.2 µV was 93% specific and only 30% sensitive. A low voltage N20-b

Donation after circulatory death (DCD) is becoming increasingly utilized in heart transplantation and has the potential to further expand the donor pool. As transplant cardiologists gain more familiarity with DCD donor selection, there are many issues that lack consensus including how we incorporate the neurologic examination, how we measure functional warm ischemic time (fWIT), and what fWIT thresholds are acceptable. DCD donor selection calls for prognostication tools to help determine how quickly a donor may expire, and in current practice there is no standardization in how we make these predictions. Current scoring systems help to determine which donor may expire within a specified time window either require the temporary disconnection of ventilatory support or do not incorporate any neurologic examination or imaging. Moreover, the specified time windows differ from other DCD solid organ transplantation without standardization or strong scientific justification for these thresholds. In this perspective, we highlight the challenges faced by transplant cardiologists as they navigate the muddy waters of neuroprognostication in DCD cardiac donation. Given these difficulties, this is also a call to action for the creation of a more standardized approach to improve the DCD donor selection process for appropriate resource allocation and organ utilization. [ABSTRACT FROM AUTHOR]

Background: Hypoxic brain injury is the leading cause of death in comatose patients following resuscitation from cardiac arrest. Neurological outcome can be difficult to prognosticate following resuscitation, and goals of care discussions are often informed by multiple prognostic tools. One tool that has shown promise is the SLANT score, which encompasses five metrics including initial nonshockable rhythm, leukocyte count after targeted temperature management, total adrenaline dose during resuscitation, lack of bystander cardiopulmonary resuscitation, and time to return of spontaneous circulation. This cohort study aimed to provide an external validation of this score by using a database of comatose cardiac arrest survivors from our institution. Methods: We retrospectively queried our database of cardiac arrest survivors, selecting for patients with coma, sustained return of spontaneous circulation, and use of targeted temperature management to have a comparable sample to the index study. We calculated SLANT scores for each patient and separated them into risk levels, both according to the original study and according to a Youden index analysis. The primary outcome was poor neurologic outcome (defined by a cerebral performance category score of 3 or greater at discharge), and the secondary outcome was in-hospital mortality. Univariable and multivariable analyses, as well as a receiver operator characteristic curve, were used to assess the SLANT score for independent predictability and diagnostic accuracy for poor outcomes. Results: We demonstrate significant association between a SLANT group with increased risk and poor neurologic outcome on univariable (p = 0.005) and multivariable analysis (odds ratio 1.162, 95% confidence interval 1.003–1.346, p = 0.046). A receiver operating characteristic analysis indicates that SLANT scoring is a fair prognostic test for poor neurologic outcome (area under the curve 0.708, 95% confidence interval 0.536–0.879, p = 0.024). Among this cohort, the most frequent SLANT elements were initial nonshockable rhythm (84.5%) and total adrenaline dose ≥ 5 mg (63.9%). There was no significant association between SLANT score and in-hospital mortality (p = 0.064). Conclusions: The SLANT score may independently predict poor neurologic outcome but not in-hospital mortality. Including the SLANT score as part of a multimodal approach may improve our ability to accurately prognosticate comatose survivors of cardiac arrest. [ABSTRACT FROM AUTHOR]

Background: Cerebral computed tomography (CT) and various severity scoring systems have been developed for the early prediction of the neurological outcomes of cardiac arrest survivors. However, few studies have combined these approaches. Therefore, we evaluated the value of the combination of cerebral CT and severity score for neuroprognostication. Methods: This single-center, retrospective observational study included consecutive patients surviving nontraumatic cardiac arrest (January 2016 and December 2020). Gray-towhite ratio (GWR), third and fourth ventricle characteristics, and medial temporal lobe atrophy scores were evaluated on noncontrast cerebral CT. Simplified cardiac arrest hospital prognosis (sCAHP) score was calculated for severity assessment. The associations between the CT characteristics, sCAHP score and neurological outcomes were analyzed. Results: This study enrolled 559 patients. Of them, 194 (34.7%) were discharged with favorable neurological outcomes. Patients with favorable neurological outcome had a higher GWR (1.37 vs 1.25, p < 0.001), area of fourth ventricle (461 vs 413 mm², p < 0.001), anteroposterior diameter of fourth ventricle (0.95 vs 0.86 cm, p < 0.001) and a lower sCAHP score (146 vs 190, p < 0.001) than those with poor recovery. Patients with higher sCAHP score had lower GWR (p trend < 0.001), area of fourth ventricle (p trend = 0.019) and anteroposterior diameter of fourth ventricle (p trend = 0.014). The predictive ability by using area under receiver operating characteristic curve (AUC) for the combination of sCAHP score and GWR was significantly higher than that calculated for sCAHP (0.86 vs 0.76, p < 0.001) or GWR (0.86 vs 0.81, p = 0.001) alone. Conclusions: The combination of GWR and sCAHP score can be used to effectively predict the neurological outcomes of cardiac arrest survivors and thus ensure timely intervention for those at high risk of poor recovery. [ABSTRACT FROM AUTHOR]

About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic–ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. M. Brandon Westover has private equity as co-founder of Beacon Biosignals and receives compensation for consulting and scientific advisory roles. Dr. Sahar F. Zafar has served as clinical neurophysiologist for CortiCare and received speaker honoraria from Marinus., (Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Head computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA. Methods/design: This is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of “severe HIE”. Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0–3) and poor (modified Rankin Scale 4–6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines. Conclusions: The results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.

Background:Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods. Methods: Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithmthat uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication. Results: Inter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction. Conclusion: Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA. [ABSTRACT FROM AUTHOR]

We report the case of a 19-year-old patient with an acute-onset non-traumatic coma. Brain MRI scan was normal, CSF showed mild pleocytosis and moderately elevated protein, and continuous EEG-monitoring was compatible with spindle-coma. Cortical somatosensory evoked potentials (SSEPs) and middle-latency auditory evoked potentials (MLAEPs) were bilaterally absent, and brainstem auditory evoked potentials suggested a brainstem dysfunction. Serum anti-GQ1b and anti-GT1a IgG antibodies positivity suggested Bickerstaff's brainstem encephalitis (BBE). The clinical and functional outcomes were favorable and normal cortical SSEPs/MLAEPs reappeared in a few weeks. Based on this report, in cases of unexplained MRI-negative coma with neurophysiological evidence of brainstem dysfunction, BBE should be eliminated before considering withdrawal of life-sustaining therapy (WLST). [ABSTRACT FROM AUTHOR]

BackgroundHead computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.MethodsThree blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.ResultsInter-rater agreement on GWR was very good (ICC 0.82–0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78–0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.ConclusionHuman and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.

Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not fully understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research. [ABSTRACT FROM AUTHOR]

In patients who undergo thrombectomy for acute ischemic stroke, the relationship between pre-admission antithrombotic (anticoagulation or antiplatelet) use and both radiographic and functional outcome is not well understood. We sought to explore the relationship between pre-admission antithrombotic use in patients who underwent thrombectomy for acute ischemic stroke at two medical centers in New York City between December 2018 and November 2020. Analyses were performed using analysis of variance and Pearson's chi-squared tests. Of 234 patients in the analysis cohort, 65 (28%) were on anticoagulation, 64 (27%) were on antiplatelet, and 105 (45%) with no antithrombotic use pre-admission. 3-month Modified Rankin Scale (mRS) score of 3–6 was associated with pre-admission antithrombotic use (71% anticoagulation vs. 77% antiplatelet vs. 56% no antithrombotic, p = 0.04). There was no relationship between pre-admission antithrombotic use and Thrombolysis in Cerebral Iinfarction (TICI) score, post-procedure Alberta Stroke Program Early CT Score (ASPECTS) score, rate of hemorrhagic conversion, length of hospital admission, discharge NIH Stroke Scale (NIHSS), discharge mRS score, or mortality. When initial NIHSS score, post-procedure ASPECTS score, and age at admission were included in multivariate analysis, pre-admission antithrombotic use was still significantly associated with a 3-month mRS score of 3–6 (OR 2.36, 95% CI 1.03–5.54, p = 0.04). In this cohort of patients with acute ischemic stroke who underwent thrombectomy, pre-admission antithrombotic use was associated with 3-month mRS score, but no other measures of radiographic or functional outcome. Further research is needed on the relationship between use of specific anticoagulation or antiplatelet agents and outcome after acute ischemic stroke, but moreover, improve stroke prevention. [ABSTRACT FROM AUTHOR]

Background: Quantitative pupillometry is recommended for neuroprognostication after out-of-hospital cardiac arrest 72 h or more after ICU admission, but the feasibility and utility of earlier assessment is unknown.Methods: This was a study of the utility of an early quantitative pupillometry index in predicting neurological outcome in patients with reduced consciousness after out-of-hospital cardiac arrest. Quantitative infrared pupillometry index was measured at 0, 6, 24, 48, and 72 h from admission. Acceptable predictive utility was defined as a positive predictive value of >95% and false positive rate of zero, with a narrow 95% confidence interval (95% CI).Results: At least one quantitative pupillometry index measurement was available from within the first 6 h for all 77 patients who met inclusion criteria. A quantitative pupillometry index ≤2.4 at baseline and ≤2.3 within the first 6 h met the criteria for utility. The positive predictive value of the baseline cut-off (≤2.4) for poor neurological outcome was 1.00 (95% CI, 0.54-1.00) with an estimated false positive rate of 0% (95% CI, 0-9%). The positive predictive value of the 6 h cut-off (≤2.3) for poor neurological outcome was 1.00 (95% CI, 0.59-1.00) with an estimated false positive rate of 0% (95% CI, 0-8%). Sensitivities of these cut-offs for ruling out poor neurological outcomes at 0 and 6 h were 19% and 22%, respectively. Of seven patients with a quantitative pupillometry index ≤2.3 within 6 h of ICU admission, none survived. Analyses that used quantitative pupillometry index measurements from 24 to 72 h, but excluded baseline and 6 h values, were not predictive by the utility criteria.Conclusions: Quantitative pupillometry within 6 h of ICU admission after out-of-hospital cardiac arrest may identify patients with a very low chance of neurologically intact survival. Further studies of early quantitative pupillometry in this population are warranted. [ABSTRACT FROM AUTHOR]

Background: To assess in comatose patients after cardiac arrest (CA) if amplitudes of two somatosensory evoked potentials (SSEP) responses, namely, N20-baseline (N20-b) and N20–P25, are predictive of neurological outcome. Methods: Monocentric prospective study in a tertiary cardiac center between Nov 2019 and July-2021. All patients comatose at 72 h after CA with at least one SSEP recorded were included. The N20-b and N20–P25 amplitudes were automatically measured in microvolts (µV), along with other recommended prognostic markers (status myoclonus, neuron-specific enolase levels at 2 and 3 days, and EEG pattern). We assessed the predictive value of SSEP for neurologic outcome using the best Cerebral Performance Categories (CPC1 or 2 as good outcome) at 3 months (main endpoint) and 6 months (secondary endpoint). Specificity and sensitivity of different thresholds of SSEP amplitudes, alone or in combination with other prognostic markers, were calculated. Results: Among 82 patients, a poor outcome (CPC 3–5) was observed in 78% of patients at 3 months. The median time to SSEP recording was 3(2–4) days after CA, with a pattern "bilaterally absent" in 19 patients, "unilaterally present" in 4, and "bilaterally present" in 59 patients. The median N20-b amplitudes were different between patients with poor and good outcomes, i.e., 0.93 [0–2.05]µV vs. 1.56 [1.24–2.75]µV, respectively (p < 0.0001), as the median N20–P25 amplitudes (0.57 [0–1.43]µV in poor outcome vs. 2.64 [1.39–3.80]µV in good outcome patients p < 0.0001). An N20-b > 2 µV predicted good outcome with a specificity of 73% and a moderate sensitivity of 39%, although an N20–P25 > 3.2 µV was 93% specific and only 30% sensitive. A low voltage N20-b < 0.88 µV and N20–P25 < 1 µV predicted poor outcome with a high specificity (sp = 94% and 93%, respectively) and a moderate sensitivity (se = 50% and 66%). Association of "bilaterally absent or low voltage SSEP" patterns increased the sensitivity significantly as compared to "bilaterally absent" SSEP alone (se = 58 vs. 30%, p = 0.002) for prediction of poor outcome. Conclusion: In comatose patient after CA, both N20-b and N20–P25 amplitudes could predict both good and poor outcomes with high specificity but low to moderate sensitivity. Our results suggest that caution is needed regarding SSEP amplitudes in clinical routine, and that these indicators should be used in a multimodal approach for prognostication after cardiac arrest. [ABSTRACT FROM AUTHOR]