Prognostication after cardiac arrest

Prevalence and prognostic impact of ST-segment elevation in lead aVR among patients with cardiac arrest

AIMS

In acute coronary syndrome, ST-segment elevation in lead aVR (STE-aVR) indicates global myocardial ischaemia, often related to multivessel or severe left main disease, and correlates with increased mortality. The prevalence and prognostic significance of STE-aVR in cardiac arrest (CA) patients is unknown.

METHODS AND RESULTS

We identified patients (≥18 years) with CA between 2011 and 2022 who achieved return of spontaneous circulation (ROSC). The first electrocardiogram post-ROSC was assessed for STE-aVR, defined as ≥1 mm ST-segment elevation at the J point, measured by two trained assessors. Multivariable logistic regression was used to analyse the association between STE-aVR and outcomes (in-hospital mortality and poor neurologic outcome), adjusted for patient and arrest characteristics. Including 443 CA patients, the median (interquartile range) age was 61 years (50-72 years), with 60.5% (n = 268) male, 65.7% (n = 291) presenting with out-of-hospital CA (OHCA), and 29.8% (n = 132) with shockable rhythms. ST-segment elevation in lead aVR was observed in 18.3% (n = 81) of patients. Those with STE-aVR were more likely to present with OHCA and less likely to have a shockable rhythm (both, P < 0.05). ST-segment elevation in lead aVR was associated with higher in-hospital mortality (86.4% vs. 65.8%, P < 0.001) and poor neurologic outcomes (90.1% vs. 72.9%, P = 0.001). After multivariable adjustment, STE-aVR remained associated with higher in-hospital mortality [odds ratio (OR) 2.23; 95% confidence interval (CI): 1.02-4.84, P = 0.04], but not a poor neurologic outcome (OR 2.12; 95% CI: 0.90-4.98, P = 0.09).

CONCLUSION

ST-segment elevation in lead aVR was present in one in five CA survivors and was independently associated with higher in-hospital mortality.

Development of a nomogram for predicting the outcome in patients with prolonged disorders of consciousness based on the multimodal evaluative information

OBJECTIVE

To establish a nomogram prediction model for the patients with prolonged disorders of consciousness (PDOC) caused by brain injury at six months based on behavioral scale scores, neuroelectro-physiological techniques and hypothalamic-pituitary hormone levels.

METHODS

The clinical data of patients with PDOC who were first diagnosed and hospitalized in the Department of Rehabilitation Medicine of The Affiliated Jiangning Hospital of Nanjing Medical University from March 2023 to July 2024 were collected retrospectively. We performed stratified sampling based on etiology and divided into a training set (121 cases) and a validation set (49 cases) in a ratio of 7:3. After a 6-month follow-up, patients were divided into groups with improved consciousness and those without improved consciousness based on changes in CRS-R scores.Clinical behavioral scores, somatosensory evoked potentials, brainstem auditory evoked potentials, and levels of hypothalamic-pituitary hormones were utilized to identify prognostic factors for prolonged disorders of consciousness. Concurrently, a nomogram prediction model was crafted and validated to forecast the prognosis of patients with prolonged disorders of consciousness. Decision curve analysis (DCA) was subsequently employed to appraise the clinical applicability of this predictive model.

RESULTS

The comparison of clinical data between the training and validation cohorts revealed no significant statistical disparities (P > 0.05). Within the training cohort of 121 PDOC patients, 63 (52.1%)PDOC patients exhibited enhanced consciousness levels. Similarly, in the validation cohort of 49 PDOC patients, 25 (51%) PDOC patients showed improvements in consciousness. Utilizing a combination of random forest analysis, LASSO regression, and multivariate Logistic regression, we identified four key predictive variables: CRS-R score (OR = 1.05, 95%CI 1.02-1.08, P = 0.002), BAEP grading(OR = 0.88, 95%CI 0.79-0.98, P = 0.02), N60 classification (OR = 1.22, 95%CI 1.01-1.48, P = 0.02), and Estradiol (OR = 1.01, 95%CI 1.00-1.02, P = 0.01). The area under the curve (AUC) for the predictive model in the training set was 0.919(95%CI 0.87-0.968),while in the validation set, it was 0.888(95%CI 0.796-0.98). The calibration curves demonstrated a high degree of concordance between predicted probabilities and actual results, suggesting that the model possesses strong discriminative power and calibration accuracy. Furthermore, in the context of clinical decision-making, Decision Curve Analysis indicated a superior net benefit for our predictive model.

CONCLUSION

The nomogram model, which integrates CRS-R score, BAEP grading, N60 classification and Estradiol, provides a comprehensive assessment of short-term prognosis in patients with prolonged disorders of consciousness, demonstrating high accuracy.

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring ECG data. However, the integration of EEG, ECG, and clinical features remains insufficiently investigated, and its potential to enhance predictive accuracy has not been fully established. Moreover, the limited interpretability of current models poses significant barriers to clinical application. Using the I-CARE database, we analyzed EEG, ECG, and clinical data from comatose cardiac arrest patients. After rigorous preprocessing and feature engineering, machine learning models (Logistic Regression, SVM, Random Forest, and Gradient Boosting) were developed. Performance was evaluated through AUC-ROC, accuracy, sensitivity, and specificity, with SHAP applied to interpret feature contributions. Our multi-modal model outperformed single-modality models, achieving AUC values from 0.75 to 1.0. Notably, the model's accuracy peaked at a critical point within the 12-24 h window (e.g., 18 h, AUC = 1.0), surpassing EEG-only (AUC 0.7-0.8) and ECG-only (AUC < 0.6) models. SHAP identified Shockable Rhythm as the most influential feature (mean SHAP value 0.17), emphasizing its role in predictive accuracy. This study presents a novel multi-modal approach that significantly enhances early neurological outcome prediction in critical care. SHAP-based interpretability further supports clinical applicability, paving the way for more personalized patient management post-cardiac arrest.

Pro: All Cardiac Arrest Patients Should Be Transferred To a Cardiac Arrest Center

Out-of-hospital cardiac arrest (OHCA) is characterized by a high prevalence and is burdened by significant mortality and morbidity. While underlying atherosclerotic coronary artery disease accounts for the majority of the cases in the Western world owing to lifestyle and dietary customs, several other conditions and diseases can lead to OHCA. Although patient survival rates have doubled over the past 3 decades, only marginal improvements in terms of overall survival and neurologic outcomes have been observed over the last decade. A growing body of evidence suggests that regional differences in OHCA outcomes may be attributable to differences in hospital infrastructure and healthcare provider expertise, thus contributing to increased awareness of the importance of cardiac arrest centers (CACs). CACs are centers of excellence for post-cardiac arrest care, which provide dedicated, continuous access to specialized multidisciplinary facilities and expert physicians (eg, emergency department, cardiac intensive care unit, coronary angiography laboratory, rehabilitation departments), ultimately seeking to optimize patient management and improve their survival rates and functional outcomes. Here we provide an overview of the complex management of OHCA patients and outline evidence-based benefits that can result from the treatment of OHCA patients in dedicated CACs.

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.

Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning

Objective

This study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.

Methods

Utilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers. Key patient characteristics, encompassing demographic data, comorbidities, hematological and biochemical indices, and vital signs, were extracted using PostgreSQL Administration Tool (pgAdmin) software. The Cox proportional hazards model assessed the impact of WPR on mortality outcomes, while Kaplan-Meier survival curves and restricted cubic spline (RCS) analysis further validated the findings. Subgroup analyses stratified the prognostic value of WPR by demographic and clinical factors.

Results

WPR demonstrated the highest prognostic significance among the variables studied, showing a strong association with 28-day all-cause mortality. In the unadjusted Model 1, hazard ratios (HRs) for WPR quartiles ranged from 1.88 (95% CI: 1.22-2.90) in Q2 to 3.02 (95% CI: 2.04-4.47) in Q4 (Ptrend <0.05). Adjusted models (Models 2-4) confirmed the robustness of these associations, even after accounting for demographic and clinical covariates. Kaplan-Meier and RCS analyses revealed a significant U-shaped relationship between WPR and mortality risk. Subgroup analyses indicated that elevated WPR was particularly associated with increased mortality in males, elderly patients, married individuals, and those with chronic pulmonary disease.

Conclusion

WPR serves as an independent and reliable prognostic biomarker for 28-day mortality in cardiac arrest patients. Its integration into clinical decision-making may enhance the early identification of high-risk patients and guide tailored therapeutic interventions.

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.

A novel translational bioinformatics framework for facilitating multimodal data analyses in preclinical models of neurological injury

Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal datasets. Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in a custom, interactive dashboard for exploratory analysis and filtered dataset download. Compared with existing clinical and preclinical data management solutions, the presented framework better enables management of various data types (single measure, repeated measures, time series, and imaging), integration of datasets for comparison across experimental models, cohorts, and groups, and facilitation of predictive modeling from integrated datasets. Further, a predictive model development use case demonstrated utilization and value of the data framework. The general outline of a preclinical data framework presented here can serve as a template for other translational research labs that generate heterogeneous datasets and require a dynamic platform that can easily evolve alongside their research.

Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes postresuscitation. This narrative review aims to explore the role of artificial intelligence (AI) in predicting neurological outcomes postcardiac resuscitation. The methodology involved a detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools, and assessing their benefit in predicting neurological outcomes in postcardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood, and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets, and provide a reliable prognosis, which only demonstrates the need for AI to help overcome challenges faced. Advanced machine learning algorithms like artificial neural networks (ANN) using supervised learning by AI have improved the accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI-powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4, and AI-based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs, and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article, including data quality, bias, privacy issues, and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method, and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

Validation of CASPRI, GO-FAR, PIHCA scores in predicting favorable neurological outcomes after in-hospital cardiac arrest; A five-year three center retrospective study in IRAN

BACKGROUND

Predicting neurological outcomes following in-hospital cardiac arrest is crucial for guiding subsequent clinical treatments. This study seeks to validate the effectiveness of the CASPRI, GO-FAR, and PIHCA tools in predicting favorable neurological outcomes after in-hospital cardiac arrest.

METHOD

This retrospective study utilized a Utstein-style structured form to review the medical records of patients who experienced in-hospital cardiac arrest between March 2018 and March 2023. Predictors were examined using multivariable logistic regression, and the validity of the tools was assessed using ROC curves. Statistical analysis was conducted using SPSS version 25 software.

RESULTS

Out of the 1100 patients included in the study, 42 individuals (3.8%) achieved a favorable neurological outcome. multivariable regression analysis revealed that age, respiratory failure, resuscitation shift, duration of renal failure, and CPC score 24 h before cardiac arrest were significantly associated with favorable neurological outcomes. The predictive abilities of the CASPRI, GO-FAR, and PIHCA scores were calculated as 0.99 (95% CI, 0.98-1.00), 0.98 (95% CI, 0.97-0.99), and 0.96 (95% CI, 0.94-0.99) respectively. A statistically significant difference was observed in the predictive abilities of the CASPRI and PIHCA scores (P = 0.001), while the difference between CASPRI and GO-FAR did not reach significance (P = 0.057). Additionally, there was no significant difference between the predictive abilities of GO-FAR and PIHCA scores (P = 0.159).

CONCLUSION

The study concludes that CASPRI and GO-FAR scores show strong potential as objective measures for predicting favorable neurological outcomes post-cardiac arrest. Integrating these scores into clinical decision-making may enhance treatment and care strategies, in the Iranian healthcare context.

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

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

Factors predicting mortality in the cardiac ICU during the early phase of targeted temperature management in the treatment of post-cardiac arrest syndrome - The RAPID score

Introduction

Survival rates after out-of-hospital cardiac arrest (OHCA) remain low, and early prognostication is challenging. While numerous intensive care unit scoring systems exist, their utility in the early hours following hospital admission, specifically in the targeted temperature management (TTM) population, is questionable. Our aim was to create a score system that may accurately estimate outcome within the first 12 h after admission in patients receiving TTM.

Methods

We analyzed data from 103 OHCA patients who subsequently underwent TTM between 2016 and 2022. Patient demographic data, prehospital characteristics, clinical and laboratory parameters were already available in the first 12 h after admission were collected. Following a bootstrap-based predictor selection, we constructed a nonlinear logistic regression model. Internal validation was performed using bootstrap resampling. Discrimination was described using the c-statistic, whereas calibration was characterized by the intercept and slope.

Results

According to the Akaike Information Criterion (AIC) heart rate (AIC = 9.24, p = 0.0013), age (AIC = 4.39, p = 0.0115), pH (AIC = 3.68, p = 0.0171), initial rhythm (AIC = 4.76, p = 0.0093) and right ventricular end-diastolic diameter (AIC = 2.49, p = 0.0342) were associated with 30-day mortality and were used to build our predictive model and nomogram. The area under the receiver-operating characteristics curve for the model was 0.84. The model achieved a C-statistic of 0.7974, with internally validated acceptable calibration (intercept: -0.0190, slope: 0.7772) and low error rates (mean absolute error: 0.040).

Conclusion

The model we have developed may be suitable for early risk assessment of patients receiving TTM as part of primary post-resuscitation care. The calculator needed for scoring can be accessed at the following link: https://www.rapidscore.eu/.

Remarkable recovery following prolonged out-of-hospital cardiac arrest: hypoxic-ischemic encephalopathy (HIE) versus posterior reversible encephalopathy syndrome (PRES)-a case report

Background

Cardiac arrest is the most dramatic event that compromises the cerebral blood flow with fatal outcomes. Factors like the presence of bystander cardiopulmonary resuscitation, initial rhythm, and arrest time significantly influence outcomes. However, despite these known factors, there are still aspects of cardiac arrest-related neurological complications that remain less understood. As evidenced by limited case reports, the association between posterior reversible encephalopathy syndrome (PRES) and cardiac arrest is not widely known.

Case Description

We present a case study of out-of-hospital cardiac arrest (OHCA) involving a patient with multiple comorbidities and factors that could complicate her neurological outcome. Despite experiencing a delayed recovery following the cardiac arrest event and an initial insult to the brain, the patient exhibited remarkable neurological recovery. There has been a complex individualized targeted management that contributed to the favorable outcome.

Conclusions

This case study provides valuable insights into the complexities of managing OHCA patients, the factors influencing recovery, and the importance of a multidisciplinary team for early diagnosis and treatment of conditions like PRES to prevent permanent neurological damage. Further research into this area is necessary to better understand the mechanisms and implications of such associations for improving patient care and outcomes following cardiac arrest.

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.

Effects of Dexmedetomidine Added to Ropivacaine in Ultrasound-Guided Continuous Pericapsular Nerve Group Block Among Elderly Patients Undergoing Total Hip Arthroplasty

Total hip arthroplasty (THA) is a highly effective intervention for addressing hip joint issues, yet managing perioperative pain remains a significant challenge. In this study, we aimed to investigate the impact of supplementing ropivacaine with dexmedetomidine in ultrasound-guided continuous pericapsular nerve group block (PENGB) among elderly patients undergoing THA. We conducted a retrospective analysis involving 112 elderly patients who underwent THA. These patients were divided into two groups: the Control group, receiving ropivacaine alone, and the DEX group, receiving ropivacaine combined with dexmedetomidine. We evaluated various parameters including hemodynamic data, postoperative pain levels assessed using the Visual Analog Scale, cognitive status measured with the Montreal Cognitive Assessment, and serum markers (S100β and GFAP). Our findings revealed that the DEX group exhibited improved stability in blood pressure and oxygen saturation following surgery. Moreover, patients in the DEX group reported significantly lower levels of pain at 6 and 12 hours postsurgery, with a prolonged duration of pain relief. Furthermore, dexmedetomidine administration was associated with preserved cognitive function during the early postoperative period. Analysis of serum markers suggested potential cognitive protection conferred by the addition of dexmedetomidine. Overall, our study underscores the multifaceted benefits of incorporating dexmedetomidine into ropivacaine-based PENGB for elderly THA patients.

Serial Systemic Immune-Inflammation Indices (SSIIi) as Prognostic Markers in Persistent Hypoxic Brain Injury Post-cardiac Arrest: A Case Report

This case report presents a novel exploration of serial systemic immune-inflammation indices (SSIIi) as a potential prognostic biomarker in a critical care setting. The subject of this report is a 31-year-old male who, following a heroin overdose, suffered an asystolic cardiac arrest and subsequently passed away two weeks later in the intensive care unit (ICU). The SSIIi, calculated as platelet count × neutrophil count / lymphocyte count, was monitored throughout his stay. The case demonstrates that SSIIi measurements, particularly within the critical initial 24-72 hours, may provide insight into the patient's immune response dynamics following a severe hypoxic event. Specifically, the data suggest that a persistently elevated SSIIi may be indicative of a maladaptive immune response, characterized by ongoing inflammation, which correlates with a deteriorating clinical trajectory. The rapid escalation and sustained high SSIIi values observed in this patient appear to predict a poor outcome. This case underscores the importance of SSIIi as a potential tool for clinicians to assess prognosis in ICU patients, particularly in cases of acute brain injury where hypoxia is a central factor and sepsis is not present. The findings open avenues for further research into SSIIi as an objective measure for guiding treatment decisions and improving outcomes in similar critical care scenarios.

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest

BACKGROUND AND OBJECTIVES

In light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows prognostication after out-of-hospital cardiac arrest.

METHODS

In this monocentric, retrospective cohort study, a supervised machine learning classifier based on an elastic net regularized logistic regression model for gray matter alterations on nonenhanced CCT obtained after cardiac arrest was trained using 10-fold cross-validation and tested on a hold-out sample (random split 75%/25%) for outcome prediction. Following the literature, a favorable outcome was defined as a cerebral performance category of 1-2 and a poor outcome of 3-5. The diagnostic accuracy was compared with established and guideline-recommended prognostic measures within the sample, that is, gray matter-white matter ratio (GWR), neuron-specific enolase (NSE), and neurofilament light chain (NfL) in serum.

RESULTS

Of 279 adult patients, 132 who underwent CCT within 14 days of cardiac arrest with good imaging quality were identified. Our approach discriminated between favorable and poor outcomes with an area under the curve (AUC) of 0.73 (95% CI 0.59-0.82). Thus, the prognostic power outperformed the GWR (AUC 0.66, 95% CI 0.56-0.76). The biomarkers NfL, measured at days 1 and 2, and NSE, measured at day 2, exceeded the reliability of the imaging markers derived from CT (AUC NfL day 1: 0.87, 95% CI 0.75-0.99; AUC NfL day 2: 0.90, 95% CI 0.79-1.00; AUC NSE day: 2 0.78, 95% CI 0.62-0.94).

DISCUSSION

Our data show that machine learning-assisted gray matter analysis of CCT images offers prognostic information after out-of-hospital cardiac arrest. Thus, CCT gray matter analysis could become a reliable and time-independent addition to the standard workup with serum biomarkers sampled at predefined time points. Prospective studies are warranted to replicate these findings.

Early predictive value of lipocalin-type prostaglandin D synthase for 28-day mortality in cardiac arrest patients: study protocol for a prospective study

INTRODUCTION

Public training in cardiopulmonary resuscitation and treatment in emergency and intensive care unit have made tremendous progress. However, cardiac arrest remains a major health burden worldwide, with brain damage being a significant contributor to disability and mortality. Lipocalin-type prostaglandin D synthase (L-PGDS), which is mainly localised in the central nervous system, has been previously shown to inhibit postischemia neuronal apoptosis. Therefore, we aim to observe whether serum L-PGDS can serve as a potential biomarker and explore its role in determining the severity and prognosis of patients who have achieved restoration of spontaneous circulation (ROSC).

METHODS AND ANALYSIS

This is a prospective observational study. The participants (n = 60) who achieve ROSC will be distributed into two groups (non-survivor and survivor) based on 28-day survival. Healthy volunteers (n = 30) will be enrolled as controls. Each individual's relevant information will be extracted from Electronic Medical Record System in Xinhua Hospital, including demographic characteristics, clinical data, laboratory findings and so on. On days 1, 3 and 7 after ROSC, blood samples will be drawn and batch tested on the level of serum neuron-specific enolase, soluble protein 100β, L-PGDS, procalcitonin, tumour necrosis factor-alpha and interleukin-6. The cerebral performance category score was assessed on the 28th day after ROSC.

ETHICS AND DISSEMINATION

This study was performed with the approval of the Clinical Ethical Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Approval No. XHEC-C-2023-130-1). The results will be published in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Registry (ChiCTR2300078564).

Prediction of outcomes after cardiac arrest by a generative artificial intelligence model

Aims

To investigate the prognostic accuracy of a non-medical generative artificial intelligence model (Chat Generative Pre-Trained Transformer 4 - ChatGPT-4) as a novel aspect in predicting death and poor neurological outcome at hospital discharge based on real-life data from cardiac arrest patients.

Methods

This prospective cohort study investigates the prognostic performance of ChatGPT-4 to predict outcomes at hospital discharge of adult cardiac arrest patients admitted to intensive care at a large Swiss tertiary academic medical center (COMMUNICATE/PROPHETIC cohort study). We prompted ChatGPT-4 with sixteen prognostic parameters derived from established post-cardiac arrest scores for each patient. We compared the prognostic performance of ChatGPT-4 regarding the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and likelihood ratios of three cardiac arrest scores (Out-of-Hospital Cardiac Arrest [OHCA], Cardiac Arrest Hospital Prognosis [CAHP], and PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages [PROLOGUE score]) for in-hospital mortality and poor neurological outcome.

Results

Mortality at hospital discharge was 43% (n = 309/713), 54% of patients (n = 387/713) had a poor neurological outcome. ChatGPT-4 showed good discrimination regarding in-hospital mortality with an AUC of 0.85, similar to the OHCA, CAHP, and PROLOGUE (AUCs of 0.82, 0.83, and 0.84, respectively) scores. For poor neurological outcome, ChatGPT-4 showed a similar prediction to the post-cardiac arrest scores (AUC 0.83).

Conclusions

ChatGPT-4 showed a similar performance in predicting mortality and poor neurological outcome compared to validated post-cardiac arrest scores. However, more research is needed regarding illogical answers for potential incorporation of an LLM in the multimodal outcome prognostication after cardiac arrest.

The agreement between jugular bulb and cerebrospinal fluid lactate levels in patients with out-of-hospital cardiac arrest

We investigated the agreement between the jugular bulb (JB) and cerebrospinal fluid (CSF) lactate levels. The study was conducted from July 2021 to June 2023 as a prospective observational cohort study at a single center. The right jugular vein was accessed, and the placement of JB catheter tip was confirmed using lateral cervical spine X-ray. A lumbar catheter was inserted between the 3rd and 4th lumbar spine of the patient. Lactate levels were measured immediately, 24 h, 48 h, and 72 h after ROSC. In patients with a good neurological prognosis, kappa between JB and CSF lactate levels measured immediately, at 24 h, 48 h, and 72 h after ROSC were 0.08, 0.36, 0.14, - 0.05 (p = 0.65, 0.06, 0.48, and 0.75, respectively). However, in patients with a poor neurological prognosis, kappa between JB and CSF lactate levels measured immediately, at 24 h, 48 h, and 72 h after ROSC were 0.38, 0.21, 0.22, 0.12 (p = 0.001, 0.04, 0.04, and 0.27, respectively). This study demonstrated that JB lactate levels exhibited significant agreement with arterial lactate levels, compared to CSF lactate levels. Therefore, this should be considered when using JB lactate to monitor cerebral metabolism.

EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest

PURPOSE

To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest.

METHODS

In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison.

RESULTS

Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%).

CONCLUSIONS

Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.

A Comprehensive Review and Practical Guide of the Applications of Evoked Potentials in Neuroprognostication After Cardiac Arrest

Cardiorespiratory arrest is a very common cause of morbidity and mortality nowadays, and many therapeutic strategies, such as induced coma or targeted temperature management, are used to reduce patient sequelae. However, these procedures can alter a patient's neurological status, making it difficult to obtain useful clinical information for the reliable estimation of neurological prognosis. Therefore, complementary investigations are conducted in the early stages after a cardiac arrest to clarify functional prognosis in comatose cardiac arrest survivors in the first few hours or days. Current practice relies on a multimodal approach, which shows its greatest potential in predicting poor functional prognosis, whereas the data and tools to identify patients with good functional prognosis remain relatively limited in comparison. Therefore, there is considerable interest in investigating alternative biological parameters and advanced imaging technique studies. Among these, somatosensory evoked potentials (SSEPs) remain one of the simplest and most reliable tools. In this article, we discuss the technical principles, advantages, limitations, and prognostic implications of SSEPs in detail. We will also review other types of evoked potentials that can provide useful information but are less commonly used in clinical practice (e.g., visual evoked potentials; short-, medium-, and long-latency auditory evoked potentials; and event-related evoked potentials, such as mismatch negativity or P300).

Prognostic Performance of Initial Clinical Examination in Predicting Good Neurological Outcome in Cardiac Arrest Patients Treated with Targeted Temperature Management

Prognostication studies of cardiac arrest patients have mainly focused on poor neurological outcomes. However, an optimistic prognosis for good outcome could provide both justification to maintain and escalate treatment and evidence-based support to persuade family members or legal surrogates after cardiac arrest. The aim of the study was to evaluate the utility of clinical examinations performed after return of spontaneous circulation (ROSC) in predicting good neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients treated with targeted temperature management (TTM). This retrospective study included OHCA patients treated with TTM from 2009 to 2021. Initial clinical examination findings related to the Glasgow coma scale (GCS) motor score, pupillary light reflex, corneal reflex (CR) and breathing above the set ventilator rate were assessed immediately after ROSC and before the initiation of TTM. The primary outcome was good neurological outcome at 6 months after cardiac arrest. Of 350 patients included in the analysis, 119 (34%) experienced a good neurological outcome at 6 months after cardiac arrest. Among the parameters of the initial clinical examinations, specificity was the highest for the GCS motor score, and sensitivity was the highest for breathing above the set ventilator rate. A GCS motor score of >2 had a sensitivity of 42.0% (95% confidence interval [CI] = 33.0-51.4) and a specificity of 96.5% (95% CI = 93.3-98.5). Breathing above the set ventilator rate had a sensitivity of 84.0% (95% CI = 76.2-90.1) and a specificity of 69.7% (95% CI = 63.3-75.6). As the number of positive responses increased, the proportion of patients with good outcomes increased. Consequently, 87.0% of patients for whom all four examinations were positive experienced good outcomes. As a result, the initial clinical examinations predicted good neurological outcomes with a sensitivity of 42.0-84.0% and a specificity of 69.7-96.5%. When more examinations with positive results are achieved, a good neurological outcome can be expected.

Unfavorable Neurological Outcomes with Incremental Cardiopulmonary Resuscitation Duration in Cardiac Arrest Brain Injury: A Systematic Review and Meta-Analysis

The duration of cardiopulmonary resuscitation (CPR) affects neurological outcomes. Conclusive data on its decremental effect on neurological outcomes have not been explored before in a quantitative review. PubMed and Google Scholar were searched for relevant studies from 2015 up to May 2023 using relevant keywords. The odds of good neurological outcomes were studied. Binary random effects were used to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). A leave-one-out sensitivity analysis was performed. Heterogeneity was assessed using I 2 statistics. For outcomes showing moderate to high heterogeneity, subgroup analysis was performed for follow-up duration or type of study. A p value of < 0.05 was considered statistically significant. A total of 349,027 cardiac arrest patients (mean age, 70.2 years; males, 56.6%) from four studies were included in the meta-analysis. Of them, the initial rhythm was shockable in 11% (38,465/349,027) and non-shockable in 88.97% (310,562/349,027) of the population. Odds of having favorable neurological outcomes were 0.32 (95% CI 0.10–1.01, p = 0.05) for 6–10 min (n = 14,118), 0.10 (95% CI 0.02–0.64, p = 0.02) for 11–15 min (n = 43,885), 0.05 (95% CI 0.01–0.36, p 0.01) for 16–20 min (n = 66,174), 0.04 (95% CI 0.01–0.21, p < 0.01) for > 20 min (n = 181,262), and 0.03 (95% CI 0.00–1.55, p = 0.08) for > 30 min (n = 66,461) when compared to patients receiving CPR for < 5 min (n = 6420). Steady decremental odds of favorable neurological outcomes were seen with every 5 min of increased CPR duration, with a statistically significant decline seen in CPR duration from 11 to 15 min onwards.

Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors

As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.

Neurological outcomes and quality of life in post-cardiac arrest patients with return of spontaneous circulation supported by ECMO: A retrospective case series

Post-cardiac arrest brain injury constitutes a significant contributor to morbidity and mortality, leading to cognitive impairment and subsequent disability. Individuals within this patient cohort grapple with uncertainty regarding the potential advantages of extracorporeal life support (ECMO) cannulation. This study elucidates the neurological outcomes and quality of life of post-cardiac arrest patients who attained spontaneous circulation and underwent ECMO cannulation. This is a retrospective case study within a local context, the research involved 32 patients who received ECMO support following an intrahospital cardiac arrest with return of spontaneous circulation (ROSC). An additional 32 patients experienced cardiac arrest with ROSC before undergoing cannulation. The average age was 41 years, with the primary causes of cardiac arrest identified as acute coronary syndrome (46.8%), pulmonary thromboembolism (21.88%), and hypoxemia (18.7%). The most prevalent arrest rhythm was asystole (37.5%), followed by ventricular fibrillation (34.4%). The mean SOFA score was 7 points (IQR 6.5-9), APACHE II score was 12 (IQR 9-16), RESP score was -1 (IQR -1 to -4) in cases of respiratory ECMO, and SAVE score was -3 (IQR -5 to 2) in cases of cardiac ECMO. Overall survival was 71%, and at 6 months, the Barthel score was 75 points, modified Rankin score was 2, cerebral performance categories score was 1, and the SF-12 had an average score of 30. Notably, there were no significant associations between the time, cause, or rhythm of cardiac arrest and neurological outcomes. Importantly, cardiac arrest is not a contraindication for ECMO cannulation. A meticulous assessment of candidates who have achieved spontaneous circulation after cardiac arrest, considering the absence of early signs of poor neurological prognosis, is crucial in patient selection. Larger prospective studies are warranted to validate and extend these findings.

Fingerprint of Circulating Immunocytes as Biomarkers for the Prognosis of Brain Inflammation and Neuronal Injury after Cardiac Arrest

Cardiac arrest is one of the most dangerous health problems in the world. Outcome prognosis is largely based on cerebral performance categories determined by neurological evaluations. Few systemic tests are currently available to predict survival to hospital discharge. Here, we present the results from the preclinical studies of cardiac arrest and resuscitation (CAR) in mice to identify signatures of circulating immune cells as blood-derived biomarkers to predict outcomes after CAR. Two flow cytometry panels for circulating blood lymphocytes and myeloid-derived cells, respectively, were designed to correlate with neuroinflammation and neuronal and dendritic losses in the selectively vulnerable regions of bilateral hippocampi. We found that CD4+CD25+ regulatory T cells, CD11b+CD11c- and CD11b+Ly6C+Ly6G+ myeloid-derived cells, and cells positive for the costimulatory molecules CD80 and CD86 in the blood were correlated with activation of microglia and astrocytosis, and CD4+CD25+ T cells are additionally correlated with neuronal and dendritic losses. A fingerprint pattern of blood T cells and monocytes is devised as a diagnostic tool to predict CAR outcomes. Blood tests aimed at identifying these immunocyte patterns in cardiac arrest patients will guide future clinical trials to establish better prognostication tools to avoid unnecessary early withdrawal from life-sustaining treatment.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group

BACKGROUND

Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.

METHODS

After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.

RESULTS

After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.

CONCLUSIONS

Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

Longitudinal serum neurofilament light kinetics in post-anoxic encephalopathy

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

DISCUSSION

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

Electroencephalography Prediction of Neurological Outcomes After Hypoxic-Ischemic Brain Injury: A Systematic Review and Meta-Analysis

Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.

Outcome of comatose patients following cardiac arrest: When mRS completes CPC

AIM

Good outcome in patients following cardiac arrest (CA) is usually defined as Cerebral Performance Category (CPC) 1-2, while CPC 3 is debated, and CPC 4-5 represent poor outcome. We aimed to assess when the modified Rankin Scale (mRS) can improve CPC outcome description, especially in CPC 3. We further aimed to correlate neuron specific enolase (NSE) with both functional measures to explore their relationship with neuronal damage.

METHODS

Peak NSE within the first 48 hours, and CPC and mRS at 3 months were prospectively collected for 665 consecutive comatose adults following CA treated between April 2016 and April 2023. For each CPC category, mRS was described. We considered good outcome as mRS 1-3, in line with existing recommendations. CPC and mRS were correlated to peak serum NSE using non-parametric assessments.

RESULTS

CPC 1, 2, 4 and 5 correlated almost perfectly with mRS in terms of good and poor outcomes. However, CPC 3 was heterogeneously associated to the dichotomized mRS (53.1% had good outcome (mRS 0-3), 46.9% poor outcome (mRS 4-6)). NSE was strongly correlated with CPC (Spearman's rho 0.616, P < 0.001) and mRS (Spearman's rho 0.613, P < 0.001).

CONCLUSION

CPC and mRS correlate similarly with neuronal damage. Whilst CPC 1-2 and CPC 4-5 are strongly associated with mRS 0-3 and, respectively, with mRS 5-6, CPC 3 is heterogenous: both good and poor mRS scores are found within this category. Therefore, we suggest that the mRS should be routinely assessed in patients with CPC 3 to refine outcome description.

Alterations of the gut microbial community structure modulates the Th17 cells response in a rat model of asphyxial cardiac arrest

Th17 cells triggered inflammation is a critical element in cerebral ischemic injury, and the gut microbiota intricately impacts T lymphocytes. Nevertheless, it remains unclear whether the gut microbiota involves in cardiac arrest/cardiopulmonary resuscitation (CA/CPR) induced-brain injury through Th17 cells. The present study investigated the interaction between gut microbiota and Th17 cells in a rat model. We observed that CA/CPR induced the alterations of the gut microbial community structure, and elevated the level of IL-17 in the serum, and a slight infiltration of Th17 cells into the brain. The Th17 cells were increased significantly in the peripheral blood, 28.33 ± 6.18% of these Th17 cells were derived from the Peyer's patches of small intestine. Furthermore, fecal microbiota transplantation (FMT) from rats with CA/CPR induced Th17 cell response, promoting hippocampal cell apoptosis and declining learning ability and memory in recipient rats. Taken together, CA/CPR-induced alterations of the gut microbial community structure stimulated Th17 cell response which aggravated brain injury.

Different neuroprognostication thresholds of neuron-specific enolase in shockable and non-shockable out-of-hospital cardiac arrest: a prospective multicenter observational study in Korea (the KORHN-PRO registry)

BACKGROUND

Serum neuron-specific enolase (NSE) is the only recommended biomarker for multimodal prognostication in postcardiac arrest patients, but low sensitivity of absolute NSE threshold limits its utility. This study aimed to evaluate the prognostic performance of serum NSE for poor neurologic outcome in out-of-hospital cardiac arrest (OHCA) survivors based on their initial rhythm and to determine the NSE cutoff values with false positive rate (FPR) < 1% for each group.

METHODS

This study included OHCA survivors who received targeted temperature management (TTM) and had serum NSE levels measured at 48 h after return of spontaneous circulation in the Korean Hypothermia Network, a prospective multicenter registry from 22 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. The primary outcome was poor outcome at 6 month, defined as a cerebral performance category of 3-5.

RESULTS

Of 623 patients who underwent TTM with NSE measured 48 h after the return of spontaneous circulation, 245 had an initial shockable rhythm. Median NSE level was significantly higher in the non-shockable group than in the shockable group (104.6 [40.6-228.4] vs. 25.9 [16.7-53.4] ng/mL, P < 0.001). Prognostic performance of NSE assessed by area under the receiver operating characteristic curve to predict poor outcome was significantly higher in the non-shockable group than in the shockable group (0.92 vs 0.86). NSE cutoff values with an FPR < 1% in the non-shockable and shockable groups were 69.3 (sensitivity of 42.1%) and 102.7 ng/mL (sensitivity of 76%), respectively.

CONCLUSION

NSE prognostic performance and its cutoff values with FPR < 1% for predicting poor outcome in OHCA survivors who underwent TTM differed between shockable and non-shockable rhythms, suggesting postcardiac arrest survivor heterogeneity. Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02827422.

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

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

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

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

A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of Neurological Injury

Background

Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets.

Methods

Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download.

Results

Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome.

Conclusions

The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.

Relationship between serum neuron-specific enolase and EEG after cardiac arrest: A reappraisal

OBJECTIVE

Electroencephalogram (EEG) and serum neuron specific enolase (NSE) are frequently used prognosticators after cardiac arrest (CA). This study explored the association between NSE and EEG, considering the role of EEG timing, its background continuity, reactivity, occurrence of epileptiform discharges, and pre-defined malignancy degree.

METHODS

Retrospective analysis including 445 consecutive adults from a prospective registry, surviving the first 24 hours after CA and undergoing multimodal evaluation. EEG were interpreted blinded to NSE results.

RESULTS

Higher NSE was associated with poor EEG prognosticators, such as increasing malignancy, repetitive epileptiform discharges and lack of background reactivity, independently of EEG timing (including sedation and temperature). When stratified for background continuity, NSE was higher with repetitive epileptiform discharges, except in the case of suppressed EEGs. This relationship showed some variation according to the recording time.

CONCLUSIONS

Neuronal injury after CA, reflected by NSE, correlates with several EEG features: increasing EEG malignancy, lack of background reactivity, and presence of repetitive epileptiform discharges. The correlation between epileptiform discharges and NSE is influenced by underlying EEG background and timing.

SIGNIFICANCE

This study, describing the complex interplay between serum NSE and epileptiform features, suggests that epileptiform discharges reflect neuronal injury particularly in non-suppressed EEG.

Clinical presentation, diagnosis, treatment, and outcome in 8 dogs and 2 cats with global hypoxic-ischemic brain injury (2010-2022)

BACKGROUND

Global hypoxic-ischemic brain injury (GHIBI) results in variable degrees of neurological dysfunction. Limited data exists to guide prognostication on likelihood of functional recovery.

HYPOTHESIS

Prolonged duration of hypoxic-ischemic insult and absence of neurological improvement in the first 72 hours are negative prognostic indicators.

ANIMALS

Ten clinical cases with GHIBI.

METHODS

Retrospective case series describing 8 dogs and 2 cats with GHIBI, including clinical signs, treatment, and outcome.

RESULTS

Six dogs and 2 cats experienced cardiopulmonary arrest or anesthetic complication in a veterinary hospital and were promptly resuscitated. Seven showed progressive neurological improvement within 72 hours of the hypoxic-ischemic insult. Four fully recovered and 3 had residual neurological deficits. One dog presented comatose after resuscitation at the primary care practice. Magnetic resonance imaging confirmed diffuse cerebral cortical swelling and severe brainstem compression and the dog was euthanized. Two dogs suffered out-of-hospital cardiopulmonary arrest, secondary to a road traffic accident in 1 and laryngeal obstruction in the other. The first dog was euthanized after MRI that identified diffuse cerebral cortical swelling with severe brainstem compression. In the other dog, spontaneous circulation was recovered after 22 minutes of cardiopulmonary resuscitation. However, the dog remained blind, disorientated, and ambulatory tetraparetic with vestibular ataxia and was euthanized 58 days after presentation. Histopathological examination of the brain confirmed severe diffuse cerebral and cerebellar cortical necrosis.

CONCLUSIONS AND CLINICAL IMPORTANCE

Duration of hypoxic-ischemic insult, diffuse brainstem involvement, MRI features, and rate of neurological recovery could provide indications of the likelihood of functional recovery after GHIBI.

Cardiac arrest centres: what do they add?

There are wide regional variations in outcome following resuscitated out of hospital cardiac arrest. These geographical differences appear to be due to hospital infrastructure and provider experience rather than baseline characteristics. It is proposed that post-arrest care be delivered in a systematic fashion by concentrating services in Cardiac Arrest Centres, with greater provider experience, 24-hour access to diagnostics, and specialist treatment to minimise the impact of ischaemia-reperfusion injury and treat the causative pathology. These cardiac arrest centres would provide access to targeted critical care, acute cardiac care, radiology services and appropriate neuro-prognostication. However implementation of cardiac arrest networks with specialist receiving hospitals is complex and requires alignment of pre-hospital care services with those delivered in hospital. Furthermore there are no randomised trial data currently supporting pre-hospital delivery to a Cardiac Arrest Centre and definitions are heterogeneous. In this review article, we propose a universal definition of a Cardiac Arrest Centre and review the current observational data evidence and the potential impact of the ARREST trial.

Hypoxic-Ischemic Brain Injury in ECMO: Pathophysiology, Neuromonitoring, and Therapeutic Opportunities

Extracorporeal membrane oxygenation (ECMO), in conjunction with its life-saving benefits, carries a significant risk of acute brain injury (ABI). Hypoxic-ischemic brain injury (HIBI) is one of the most common types of ABI in ECMO patients. Various risk factors, such as history of hypertension, high day 1 lactate level, low pH, cannulation technique, large peri-cannulation PaCO2 drop (∆PaCO2), and early low pulse pressure, have been associated with the development of HIBI in ECMO patients. The pathogenic mechanisms of HIBI in ECMO are complex and multifactorial, attributing to the underlying pathology requiring initiation of ECMO and the risk of HIBI associated with ECMO itself. HIBI is likely to occur in the peri-cannulation or peri-decannulation time secondary to underlying refractory cardiopulmonary failure before or after ECMO. Current therapeutics target pathological mechanisms, cerebral hypoxia and ischemia, by employing targeted temperature management in the case of extracorporeal cardiopulmonary resuscitation (eCPR), and optimizing cerebral O2 saturations and cerebral perfusion. This review describes the pathophysiology, neuromonitoring, and therapeutic techniques to improve neurological outcomes in ECMO patients in order to prevent and minimize the morbidity of HIBI. Further studies aimed at standardizing the most relevant neuromonitoring techniques, optimizing cerebral perfusion, and minimizing the severity of HIBI once it occurs will improve long-term neurological outcomes in ECMO patients.

Temperature Control After Cardiac Arrest: A Narrative Review

Cardiac arrest (CA) is a critical public health issue affecting more than half a million Americans annually. The main determinant of outcome post-CA is hypoxic-ischemic brain injury (HIBI), and temperature control is currently the only evidence-based, guideline-recommended intervention targeting secondary brain injury. Temperature control is a key component of a post-CA care bundle; however, conflicting evidence challenges its wide implementation across the vastly heterogeneous population of CA survivors. Here, we critically appraise the available literature on temperature control in HIBI, detail how the evidence has been integrated into clinical practice, and highlight the complications associated with its use and the timing of neuroprognostication after CA. Future clinical trials evaluating different temperature targets, rates of rewarming, duration of cooling, and identifying which patient phenotype benefits from different temperature control methods are needed to address these prevailing knowledge gaps.

Diagnostic Value of Serum Lactate Dehydrogenase Level Measured in the Emergency Department in Predicting Clinical Outcome in Out-of-Hospital Cardiac Arrest: A Multicenter, Observational Study

INTRODUCTION

Out-of-hospital cardiac arrest (OHCA) is complex, and risk stratification tools have the potential to include components other than clinical risk indicators, thus requiring extensive studies. Simple and accurate biomarkers for OHCA patients with poor prognoses are still needed. Serum lactate dehydrogenase (LDH) has been identified as a risk factor in patients with various diseases, such as cancer, liver disease, severe infections, and sepsis. The primary aim of this study was to assess the accuracy of LDH values at initial presentation in the emergency department (ED) in predicting the clinical outcome in OHCA.

METHODS

This retrospective multicenter observational study was performed in the ED of two tertiary university hospitals and one general hospital between January 2015 and December 2021. All patients with OHCA who visited the ED were included. The primary outcome was the sustained return of spontaneous circulation (ROSC; >20 min) after advanced cardiac life support (ACLS). The secondary outcome was survival to discharge (including home care and nursing care discharge) among patients with ROSC. The neurological prognosis was considered a tertiary outcome in patients who survived to discharge.

RESULTS

In total, 759 patients were enrolled in the final analysis. The median LDH level in the ROSC group was 448 U/L (range: 112-4500), which was significantly lower than that in the no-ROSC group (p < 0.001). The median LDH level in the survival-to-discharge group was 376 U/L (range: 171-1620), which was significantly lower than that in the death group (p < 0.001). Using the adjusted model, the odds ratio of the LDH value (≤634 U/L) for primary outcomes was 2.418 (1.665-3.513) and the odds ratio of LDH value (≤553 U/L) for secondary outcomes was 4.961 (2.184-11.269).

CONCLUSIONS

In conclusion, the serum LDH levels of patients with OHCA measured in the ED can potentially serve as a predictive marker for clinical outcomes such as ROSC and survival to discharge, although it may be difficult to predict neurological outcomes.