Continuous Amplitude-Integrated Electroencephalographic Monitoring Is a Useful Prognostic Tool for Hypothermia-Treated Cardiac Arrest Patients

The Evolving Role of Electroencephalography in Postarrest Care

Electroencephalography is an accessible, portable, noninvasive and safe means of evaluating a patient's brain activity. It can aid in diagnosis and management decisions for post-cardiac arrest patients with seizures, myoclonus and other non-epileptic movements. It also plays an important role in a multimodal approach to neuroprognostication predicting both poor and favorable outcomes. Individuals ordering, performing and interpreting these tests, regardless of the indication, should understand the supporting evidence, logistical considerations, limitations and impact the results may have on postarrest patients and their families as outlined herein.

"Drum Tower Hospital" strategy for acute type A aortic dissection with coma

OVERVIEW

Acute type A aortic dissection (ATAAD) with persistent coma is a life-threatening condition associated with high mortality and poor neurological outcomes. The optimal timing for surgical intervention in these patients remains uncertain, and many patients are not eligible for surgery due to their poor prognosis.

DESCRIPTION

In this case, a 53-year-old man with hypertension presented to the emergency department in a coma that had lasted for 9 hours. The patient was diagnosed with ATAAD and underwent the "Drum Tower Hospital" strategy, which involved preoperative assessments, including computed tomography angiography (CTA) and quantitative electroencephalogram (qEEG) monitoring. Surgical interventions, such as emergency stenting and aortic replacement, were performed to restore blood flow and repair the aorta. Postoperative monitoring, including qEEG, showed improvements in brain function. Despite the patient experiencing hemiplegia and a neurological deficit, the "Drum Tower Hospital" strategy, guided by comprehensive brain assessments, showed promise in managing ATAAD with coma. However, further research is needed to establish effective treatment strategies for these patients. Overall, ATAAD with persistent coma is a critical condition with limited treatment options. The "Drum Tower Hospital" strategy, supported by multimodal brain assessment, offers a potential approach to improve outcomes in these patients.

Prognosis of consciousness disorders in the intensive care unit

Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest

OBJECTIVES

Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias.

DESIGN

Retrospective observational cohort study.

SETTING

Two academic medical centers ("UPMC" and "University of Alabama Birmingham" [UAB]).

PATIENTS

Comatose adults resuscitated from cardiac arrest.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients' Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome.

CONCLUSIONS

Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.

Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement

Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.

Plasma neurofilament light is a predictor of neurological outcome 12 h after cardiac arrest

BACKGROUND

Previous studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24-72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes.

METHODS

Observational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1-2 was considered a good outcome and CPC 3-5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC).

RESULTS

Of 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p ≤ 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%.

CONCLUSIONS

The prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA.

Association of ultra-early diffusion-weighted magnetic resonance imaging with neurological outcomes after out-of-hospital cardiac arrest

BACKGROUND

This study aimed to investigate the association between ultra-early (within 6 h after return of spontaneous circulation [ROSC]) brain diffusion-weighted magnetic resonance imaging (DW-MRI) and neurological outcomes in comatose survivors after out-of-hospital cardiac arrest.

METHODS

We conducted a registry-based observational study from May 2018 to February 2022 at a Chungnam national university hospital in Daejeon, Korea. Presence of high-signal intensity (HSI) (P_HSI) was defined as a HSI on DW-MRI with corresponding hypoattenuation on the apparent diffusion coefficient map irrespective of volume after hypoxic ischemic brain injury; absence of HSI was defined as A_HSI. The primary outcome was the dichotomized cerebral performance category (CPC) at 6 months, defined as good (CPC 1-2) or poor (CPC 3-5).

RESULTS

Of the 110 patients (30 women [27.3%]; median (interquartile range [IQR]) age, 58 [38-69] years), 48 (43.6%) had a good neurological outcome, time from ROSC to MRI scan was 2.8 h (IQR 2.0-4.0 h), and the P_HSI on DW-MRI was observed in 46 (41.8%) patients. No patients in the P_HSI group had a good neurological outcome compared with 48 (75%) patients in the A_HSI group. In the A_HSI group, cerebrospinal fluid (CSF) neuron-specific enolase (NSE) levels were significantly lower in the group with good neurological outcome compared to the group with poor neurological outcome (20.1 [14.4-30.7] ng/mL vs. 84.3 [32.4-167.0] ng/mL, P < 0.001). The area under the curve for P_HSI on DW-MRI was 0.87 (95% confidence interval [CI] 0.80-0.93), and the specificity and sensitivity for predicting a poor neurological outcome were 100% (95% CI 91.2%-100%) and 74.2% (95% CI 62.0-83.5%), respectively. A higher sensitivity was observed when CSF NSE levels were combined (88.7% [95% CI 77.1-95.1%]; 100% specificity).

CONCLUSIONS

In this cohort study, P_HSI findings on ultra-early DW-MRI were associated with poor neurological outcomes 6 months following the cardiac arrest. The combined CSF NSE levels showed higher sensitivity at 100% specificity than on DW-MRI alone. Prospective multicenter studies are required to confirm these results.

Nomogram for Early Prediction of Outcome in Coma Patients with Severe Traumatic Brain Injury Receiving Right Median Nerve Electrical Stimulation Treatment

BACKGROUND

Accurate outcome prediction can serve to approach, quantify and categorize severe traumatic brain injury (TBI) coma patients for right median electrical stimulation (RMNS) treatment, which can support rehabilitation plans. As a proof of concept for individual risk prediction, we created a novel nomogram model combining amplitude-integrated electroencephalography (AEEG) and clinically relevant parameters.

METHODS

This study retrospective collected and analyzed a total of 228 coma patients after severe TBI in two medical centers. According to the extended Glasgow Outcome Scale (GOSE), patients were divided into a good outcome (GOSE 3-8) or a poor outcome (GOSE 1-2) group. Their clinical and biochemical indicators, together with EEG features, were explored retrospectively. The risk factors connected to the outcome of coma patients receiving RMNS treatment were identified using Cox proportional hazards regression. The discriminative capability and calibration of the model to forecast outcome were assessed by C statistics, calibration plots, and Kaplan-Meier curves on a personalized nomogram forecasting model.

RESULTS

The study included 228 patients who received RMNS treatment for long-term coma after a severe TBI. The median age was 40 years, and 57.8% (132 of 228) of the patients were male. 67.0% (77 of 115) of coma patients in the high-risk group experienced a poor outcome after one year and the comparative data merely was 30.1% (34 of 113) in low-risk group patients. The following variables were integrated into the forecasting of outcome using the backward stepwise selection of Akaike information criterion: age, Glasgow Coma Scale (GCS) at admission, EEG reactivity (normal, absence, or the stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs)), and AEEG background pattern (A mode, B mode, or C mode). The C statistics revealed that the nomograms' discriminative potential and calibration demonstrated good predictive ability (0.71).

CONCLUSION

Our findings show that the nomogram model using AEEG parameters has the potential to predict outcomes in severe TBI coma patients receiving RMNS treatment. The model could classify patients into prognostic groups and worked well in internal validation.

Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review

PURPOSE

To assess the ability of clinical examination, blood biomarkers, electrophysiology or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict good neurological outcome, defined as no, mild, or moderate disability (CPC 1-2 or mRS 0-3) at discharge from intensive care unit or later, in comatose adult survivors from cardiac arrest (CA).

METHODS

PubMed, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews were searched. Sensitivity and specificity for good outcome were calculated for each predictor. The risk of bias was assessed using the QUIPS tool.

RESULTS

A total of 37 studies were included. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. A withdrawal or localisation motor response to pain immediately or at 72-96 h after ROSC, normal blood values of neuron-specific enolase (NSE) at 24 h-72 h after ROSC, a short-latency somatosensory evoked potentials (SSEPs) N20 wave amplitude > 4 µV or a continuous background without discharges on electroencephalogram (EEG) within 72 h from ROSC, and absent diffusion restriction in the cortex or deep grey matter on MRI on days 2-7 after ROSC predicted good neurological outcome with more than 80% specificity and a sensitivity above 40% in most studies. Most studies had moderate or high risk of bias.

CONCLUSIONS

In comatose cardiac arrest survivors, clinical, biomarker, electrophysiology, and imaging studies identified patients destined to a good neurological outcome with high specificity within the first week after cardiac arrest (CA).

Early Neuroprognostication Using Frontal Spectrograms in Moderately Sedated Cardiac Arrest Patients

Introduction. The integrated suppression ratio throughout all electroencephalography (EEG) patterns has rarely been studied. The aim of this study was to evaluate the clinical utility of the suppression ratio and hyperactivity of EEG on spectrograms. Methods. This prospective observational study included 73 cardiac arrest patients. Hardwired frontal EEG monitoring with spectrograms (color density spectral arrays, CDSA) was used to predict neurological outcomes. The mean suppression ratio (MSR) and hyperactivity in the high-frequency band (HHF) in the spectrogram were investigated in moderately sedated patients. Sedative doses were considered to estimate the MSR, which was automatically measured. Results. Using propofol 30 to 40 µg/kg/min and remifentanil 0.1 to 0.15 µg/kg/min, all the patients with an MSR >30% died. At day 2, the MSR in patients with a good outcome was 0%. The cut off values were different as an MSR >30% at day 1 (AUC 0.815) and an MSR >1% at day 2 (AUC 0.891). Of the patients with an MSR ≤30%, HHF was the greatest predictor of a poor outcome (OR 12.858, P = .006). The best predictors of a poor outcome using the spectrogram were suppression ratio (SR) >30% or HHF at day 1 (AUC 0.88) and SR >1% or HHF at day 2 (AUC 0.909). Conclusions. The use of MSR and HHF in frontal spectrograms is convenient and may be successfully employed for early neuroprognostication in moderately sedated cardiac arrest patients. However, spectrograms should be used with electroencephalogram considering the effects of sedatives because of the imperfect detection of electrographic seizures and artifacts.

Utilization of Amplitude-Integrated Electroencephalography to Predict Neurologic Function after Resuscitation in Adults with Cardiogenic Cardiac Arrest

BACKGROUND

Amplitude-integrated electroencephalography (aEEG) has been used as a tool to recognize brain activity in children with hypoxic encephalopathy.

OBJECTIVES

To assess the prognostic value of aEEG during the post-resuscitation period of adult cardiogenic cardiac arrest, comatose survivors were monitored within 24 h of a return of spontaneous circulation using aEEG.

METHODS

Forty-two consecutive patients experiencing cardiac arrest were retrospectively enrolled, and a return of spontaneous circulation was achieved in all cases. These patients were admitted to the Coronary Intensive Care Unit due to cardiogenic cardiac arrest. The primary outcome was the best neurologic outcome within 6 months after resuscitation, and the registered patients were divided into two groups based on the Cerebral Performance Category (CPC) scale (CPC 1-2, good neurologic function group; CPC 3-5, poor neurologic function group). All patients received an aEEG examination within 24 h after a return of spontaneous circulation, and the parameters and patterns of aEEG recordings were compared.

RESULTS

Nineteen patients were in the good neurologic function group, and 23 were in the poor group. The four voltage parameters (minimum, maximum, span, average) of the aEEG recordings in the good neurologic function groups were significantly higher than in the poor group. Moreover, the continuous pattern, but not the status epilepticus or burst suppression patterns, could predict mid-term good neurologic function.

CONCLUSIONS

aEEG can be used to predict neurologic outcomes based on the recordings' parameters and patterns in unconscious adults who have experienced a cardiac collapse, resuscitation, and return of spontaneous circulation.

The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome

Early prognostication in cardiac arrest survivors is challenging for physicians. Unlike other prognostic modalities, biomarkers are easily accessible and provide an objective assessment method. We hypothesized that in cardiac arrest patients with targeted temperature management (TTM), early circulating microRNA (miRNA) levels are associated with the 6-month neurological outcome. In the discovery phase, we identified candidate miRNAs associated with cardiac arrest patients who underwent TTM by comparing circulating expression levels in patients and healthy controls. Next, using a larger cohort, we validated the prognostic values of the identified early miRNAs by measuring the serum levels of miRNAs, neuron-specific enolase (NSE), and S100 calcium-binding protein B (S100B) 6 h after cardiac arrest. The validation cohort consisted of 54 patients with TTM. The areas under the curve (AUCs) for poor outcome were 0.85 (95% CI (confidence interval), 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87) for miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p, respectively. When the cut-off was based on miRNA levels predicting poor outcome with 100% specificity, sensitivities were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4) for the above miRNAs, respectively. The models combining early miRNAs with protein biomarkers demonstrated superior prognostic performance to those of protein biomarkers.

Outcome and prognostication after cardiac arrest

The outcome after out-of-hospital cardiac arrest has historically been grim at best. The current overall survival rate of patients admitted to a hospital is approximately 10%, making cardiac arrest one of the leading causes of death in the United States. The situation is improving with the incorporation of therapeutic temperature modulation, aggressive prevention of secondary brain injury, and improved access to advanced cardiovascular support, all of which have decreased mortality and allowed for better outcomes. Mortality after cardiac arrest is often the direct result of active withdrawal of life-sustaining therapy based on the perception that neurological recovery is not possible. This reality highlights the importance of providing accurate estimates of neurological prognosis to decision makers when discussing goals of care. The current standard of care for assessing neurological status in patients with hypoxic-ischemic encephalopathy emphasizes a multimodal approach that includes five elements: (1) neurological examination off sedation, (2) continuous electroencephalography, (3) serum neuron-specific enolase levels, (4) magnetic resonance brain imaging, and (5) somatosensory-evoked potential testing. Sophisticated decision support systems that can integrate these clinical, imaging, and biomarker and neurophysiologic data and translate it into meaningful projections of neurological outcome are urgently needed.

Interaction between gender and post resuscitation interventions on neurological outcome in an asphyxial rat model of cardiac arrest

PURPOSE

Previous clinical studies have suggested an effect of gender on outcome after out-of-hospital cardiac arrest, but the results are conflicting and there is no uniform agreement regarding gender differences in survival and prognosis. The present study was aimed to investigate the interaction between gender and post resuscitation interventions on neurological outcome in an asphyxial rat model of cardiac arrest.

METHODS

Asphyxia was induced by blocking the endotracheal tube in 120 adult Sprague-Dawley rats (60 males and 60 females) at the same age. Cardiopulmonary resuscitation (CPR) was started after 5 min of untreated cardiac arrest. Animals were randomized into one of the three post resuscitation care intervention groups (n = 40, 20 males) immediately after resuscitation: (1) normothermic control (NC): ventilated with 2% N2/98% O2 for 1 h under normothermia; (2) targeted temperature management (TTM): ventilated with 2% N2/98% O2 for 1 h under hypothermia; (3) hydrogen inhalation (HI): ventilated with 2% H2/98% O2 for 1 h under normothermia. Physiological variables were recorded during the 5 h post resuscitation monitoring period. Neurological deficit score (NDS) and accumulative survival were used to assess 96 h outcomes. Mutual independence analysis and Mantel-Haenszel stratified analysis were used to explore the associations among gender, intervention and survival.

RESULTS

The body weights of female rats were significantly lighter than males, but CPR characteristics did not differ between genders. Compared with male rats, females had significantly lower mean arterial pressure, longer onset time of the electroencephalogram (EEG) burst and time to normal EEG trace (TTNT) in the NC group; relatively longer TTNT in the TTM group; and substantially longer TTNT, lower NDSs, and higher survival in the HI group. Mutual independence analysis revealed that both gender and intervention were associated with neurological outcome. Mantel-Haenszel stratified analysis demonstrated that female rats had significantly higher survival rate than males when adjusted for the confounder intervention.

CONCLUSION

In this rat model cardiac arrest and CPR, gender did not affect resuscitation but associated with neurological outcome. The superiority of female rats in neurological recovery was affected by post resuscitation interventions and female rats were more likely to benefit from hydrogen therapy.

Serum markers of brain injury can predict good neurological outcome after out-of-hospital cardiac arrest

Purpose

The majority of unconscious patients after cardiac arrest (CA) do not fulfill guideline criteria for a likely poor outcome, their prognosis is considered “indeterminate”. We compared brain injury markers in blood for prediction of good outcome and for identifying false positive predictions of poor outcome as recommended by guidelines.

Methods

Retrospective analysis of prospectively collected serum samples at 24, 48 and 72 h post arrest within the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Clinically available markers neuron-specific enolase (NSE) and S100B, and novel markers neurofilament light chain (NFL), total tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) were analysed. Normal levels with a priori cutoffs specified by reference laboratories or defined from literature were used to predict good outcome (no to moderate disability, Cerebral Performance Category scale 1–2) at 6 months.

Results

Seven hundred and seventeen patients were included. Normal NFL, tau and GFAP had the highest sensitivities (97.2–98% of poor outcome patients had abnormal serum levels) and NPV (normal levels predicted good outcome in 87–95% of patients). Normal S100B and NSE predicted good outcome with NPV 76–82.2%. Normal NSE correctly identified 67/190 (35.3%) patients with good outcome among those classified as “indeterminate outcome” by guidelines. Five patients with single pathological prognostic findings despite normal biomarkers had good outcome.

Conclusion

Low levels of brain injury markers in blood are associated with good neurological outcome after CA. Incorporating biomarkers into neuroprognostication may help prevent premature withdrawal of life-sustaining therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06481-4.

Chasing Certainty After Cardiac Arrest: Can a Technological Innovation Solve a Moral Dilemma?

When information on a coma patient’s expected outcome is uncertain, a moral dilemma arises in clinical practice: if life-sustaining treatment is continued, the patient may survive with unacceptably poor neurological prospects, but if withdrawn a patient who could have recovered may die. Continuous electroencephalogram-monitoring (cEEG) is expected to substantially improve neuroprognostication for patients in coma after cardiac arrest. This raises expectations that decisions whether or not to withdraw will become easier. This paper investigates that expectation, exploring cEEG’s impacts when it becomes part of a socio-technical network in an Intensive Care Unit (ICU). Based on observations in two ICUs in the Netherlands and one in the USA that had cEEG implemented for research, we interviewed 25 family members, healthcare professionals, and surviving patients. The analysis focuses on (a) the way patient outcomes are constructed, (b) the kind of decision support these outcomes provide, and (c) how cEEG affects communication between professionals and relatives. We argue that cEEG can take away or decrease the intensity of the dilemma in some cases, while increasing uncertainty for others. It also raises new concerns. Since its actual impacts furthermore hinge on how cEEG is designed and implemented, we end with recommendations for ensuring responsible development and implementation.

Early neuro-prognostication with the Patient State Index and suppression ratio in post-cardiac arrest patients

PURPOSE

Cardiopulmonary resuscitation guidelines recommend multimodal neuro-prognostication after cardiac arrest using neurological examination, electroencephalography, biomarkers, and brain imaging. The Patient State Index (PSI) and suppression ratio (SR) represent the depth and degree of sedation, respectively. We evaluated the predictive ability of PSI and SR for neuro-prognostication of post-cardiac arrest patients who underwent targeted temperature management.

METHODS

This prospective observational study was conducted between January 2017 and August 2020 and enrolled adult patients in an intensive care unit (ICU) with non-traumatic out-of-hospital cardiac arrest with return of spontaneous circulation (ROSC). PSI and SR were monitored continuously during ICU stay, and their maximum, mean, and minimum cutoff values 24 h after ROSC were analyzed to predict poor neurologic outcome and long-term survival.

RESULTS

The final analysis included 103 patients. A mean PSI ≤ 14.53 and mean SR > 36.6 showed high diagnostic accuracy as single prognostic factors. Multimodal prediction using the mean PSI and mean SR showed the highest area-under-the-curve value of 0.965 (95% confidence interval 0.909-0.991). Patients with mean PSI ≤ 14.53 and mean SR > 36.6 had relatively higher long-term mortality rates than those of patients with values >14.53 and ≤ 36.6, respectively.

CONCLUSIONS

The PSI and SR are good predictors for early neuro-prognostication in post-cardiac arrest patients.

European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care

The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.

European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: Post-resuscitation care

The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation, and organ donation.

A matter of timing: EEG monitoring for neurological prognostication after cardiac arrest in the era of targeted temperature management

Neuromonitoring with electroencephalography (EEG) is an essential tool in neurological prognostication post-cardiac arrest. EEG allows reliable and real-time assessment of early changes in background patterns, development of seizures and epileptiform activity, as well as testing for background reactivity to stimuli despite use of sedation or targeted temperature management. Delayed emergence of consciousness post-cardiac arrest is common, therefore longitudinal monitoring of EEG allows the detection of trends indicative of neurological improvement before coma recovery can be observed clinically. In this review, we summarize essential recent literature in EEG monitoring for neurological prognostication post-cardiac arrest in the context of targeted temperature management, with a particular focus on the importance of the evolution of EEG patterns in the first few days following resuscitation.

Neurologic complications of cardiac arrest

Cardiac arrest is a catastrophic event with high morbidity and mortality. Despite advances over time in cardiac arrest management and postresuscitation care, the neurologic consequences of cardiac arrest are frequently devastating to patients and their families. Targeted temperature management is an intervention aimed at limiting postanoxic injury and improving neurologic outcomes following cardiac arrest. Recovery of neurologic function governs long-term outcome after cardiac arrest and prognosticating on the potential for recovery is a heavy burden for physicians. An early and accurate estimate of the potential for recovery can establish realistic expectations and avoid futile care in those destined for a poor outcome. This chapter reviews the epidemiology, pathophysiology, therapeutic interventions, prognostication, and neurologic sequelae of cardiac arrest.

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

Neuron-specific enolase and neuroimaging for prognostication after cardiac arrest treated with targeted temperature management

BACKGROUND

Prognostication after cardiac arrest (CA) needs a multimodal approach, but the optimal method is not known. We tested the hypothesis that the combination of neuron-specific enolase (NSE) and neuroimaging could improve outcome prediction after CA treated with targeted temperature management (TTM).

METHODS

A retrospective observational cohort study was performed on patients who underwent at least one NSE measurement between 48 and 72 hr; received both a brain computed tomography (CT) scan within 24 hr and diffusion-weighted magnetic resonance imaging (DW-MRI) within 7 days after return of spontaneous circulation (ROSC); and were treated with TTM after out-of-hospital CA between 2009 and 2017 at the Seoul St. Mary's Hospital in Korea. The primary outcome was a poor neurological outcome at 6 months after CA, defined as a cerebral performance category of 3-5.

RESULTS

A total of 109 subjects underwent all three tests and were ultimately included in this study. Thirty-four subjects (31.2%) experienced good neurological outcomes at 6 months after CA. The gray matter to white matter attenuation ratio (GWR) was weakly correlated with the mean apparent diffusion coefficient (ADC), PV400 and NSE (Spearman's rho: 0.359, -0.362 and -0.263, respectively). NSE was strongly correlated with the mean ADC and PV400 (Spearman's rho: -0.623 and 0.666, respectively). Serum NSE had the highest predictive value among the single parameters (area under the curve (AUC) 0.912, sensitivity 70.7% for maintaining 100% specificity). The combination of a DWI parameter (mean ADC or PV400) and NSE had better prognostic performance than the combination of the CT parameter (GWR) and NSE. The addition of the GWR to a DWI parameter and NSE did not improve the prediction of neurological outcomes.

CONCLUSION

The GWR (≤ 24 hr) is weakly correlated with the mean ADC (≤ 7 days) and NSE (highest between 48 and 72 hr). The combination of a DWI parameter and NSE has better prognostic performance than the combination of the GWR and NSE. The addition of the GWR to a DWI parameter and NSE does not improve the prediction of neurological outcomes after CA treatment with TTM.

Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review

Purpose

To assess the ability of clinical examination, blood biomarkers, electrophysiology, or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict poor neurological outcome, defined as death, vegetative state, or severe disability (CPC 3–5) at hospital discharge/1 month or later, in comatose adult survivors from cardiac arrest (CA).

Methods

PubMed, EMBASE, Web of Science, and the Cochrane Database of Systematic Reviews (January 2013–April 2020) were searched. Sensitivity and false-positive rate (FPR) for each predictor were calculated. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed.

Results

Ninety-four studies (30,200 patients) were included. Bilaterally absent pupillary or corneal reflexes after day 4 from ROSC, high blood values of neuron-specific enolase from 24 h after ROSC, absent N20 waves of short-latency somatosensory-evoked potentials (SSEPs) or unequivocal seizures on electroencephalogram (EEG) from the day of ROSC, EEG background suppression or burst-suppression from 24 h after ROSC, diffuse cerebral oedema on brain CT from 2 h after ROSC, or reduced diffusion on brain MRI at 2–5 days after ROSC had 0% FPR for poor outcome in most studies. Risk of bias assessed using the QUIPS tool was high for all predictors.

Conclusion

In comatose resuscitated patients, clinical, biochemical, neurophysiological, and radiological tests have a potential to predict poor neurological outcome with no false-positive predictions within the first week after CA. Guidelines should consider the methodological concerns and limited sensitivity for individual modalities. (PROSPERO CRD42019141169)

Electronic supplementary material

The online version of this article (10.1007/s00134-020-06198-w) contains supplementary material, which is available to authorized users.

Continuous amplitude-integrated electroencephalography for prognostication of cardiac arrest patients undergoing extracorporeal cardiopulmonary resuscitation with targeted temperature management

BACKGROUND

Extracorporeal cardiopulmonary resuscitation (ECPR) has been increasingly used for adult cardiac arrest (CA) patients refractory to conventional CPR. However, data on early prognosticators of neurological outcome are lacking.

METHODS

CA patients undergoing ECPR were prospectively monitored via amplitude-integrated EEG (aEEG). Targeted temperature management (TTM) was induced using an extracorporeal membrane oxygenation system. aEEG background patterns were classified into continuous normal voltage (CNV), discontinuous normal voltage (DNV), low voltage (LV), flat trace (FT), burst suppression (BS), and status epilepticus (SE). The Cerebral Performance Category (CPC) scale scores at hospital discharge and at 6 months after discharge were assessed, as was wakefulness after TTM. Good neurological outcome was defined as a CPC score of 1 or 2.

RESULTS

Twenty-two patients were studied. Six patients who showed CNV within 24 hours after arrival, including one with initial FT and two with initial LV, regained consciousness and had good neurological outcome except for one who died of haemorrhagic complication. Patients with persistent FT or BS at any time did not regain consciousness. Regarding 19 patients in whom aEEG data were obtained within 24 hours, CNV background predicted good outcome at 6 months with 100% sensitivity, 93% specificity, 83% positive predictive values, and 100% negative predictive values. All these indices were 100% concerning wakefulness after TTM.

CONCLUSION

aEEG monitoring was feasible and practical in adult CA patients undergoing ECPR and TTM. Evolution of aEEG background within 24 hours provides early accurate information for neurological prognostication.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

Electroencephalography: Clinical Applications During the Perioperative Period

Electroencephalography (EEG) monitoring has become technically feasible in daily clinical anesthesia practice. EEG is a sensitive method for detecting neurophysiological changes in the brain and represents an important frontier in the monitoring and treatment of patients in the perioperative period. In this review, we briefly introduce the essential principles of EEG. We review EEG application during anesthesia practice in the operating room, including the use of processed EEG in depth of anesthesia assessment, raw EEG monitoring in recognizing brain states under different anesthetic agents, the use of EEG in the prevention of perioperative neurocognitive disorders and detection of cerebral ischemia. We then discuss EEG utilization in the intensive care units, including the use of EEG in sedative level titration and prognostication of clinical outcomes. Existing literature provides insight into both the advances and challenges of the clinical applications of EEG. Future study is clearly needed to elucidate the precise EEG features that can reliably optimize perioperative care for individual patients.

Prognostic Value of Early Intermittent Electroencephalography in Patients after Extracorporeal Cardiopulmonary Resuscitation

The aim of this study was to investigate whether early intermittent electroencephalography (EEG) could be used to predict neurological prognosis of patients who underwent extracorporeal cardiopulmonary resuscitation (ECPR). This was a retrospective and observational study of adult patients who were evaluated by EEG scan within 96 h after ECPR. The primary endpoint was neurological status upon discharge from the hospital assessed with a Cerebral Performance Categories (CPC) scale. Among 69 adult cardiac arrest patients who underwent ECPR, 17 (24.6%) patients had favorable neurological outcomes (CPC score of 1 or 2). Malignant EEG patterns were more common in patients with poor neurological outcomes (CPC score of 3, 4 or 5) than in patients with favorable neurological outcomes (73.1% vs. 5.9%, p < 0.001). All patients with highly malignant EEG patterns (43.5%) had poor neurological outcomes. In multivariable analysis, malignant EEG patterns and duration of cardiopulmonary resuscitation were significantly associated with poor neurological outcomes. In this study, malignant EEG patterns within 96 h after cardiac arrest were significantly associated with poor neurological outcomes. Therefore, an early intermittent EEG scan could be helpful for predicting neurological prognosis of post-cardiac arrest patients after ECPR.

Validation of the suppression ratio from a simplified EEG montage during targeted temperature management after cardiac arrest

AIM

The Suppression Ratio (SR) estimates the percent of the electroencephalography (EEG) epoch with very low voltage, and is associated with neurological outcome after cardiac arrest. We aimed to compare the SR generated by two monitoring devices and determine the association between SR and patterns on amplitude integrated EEG (aEEG) and full conventional EEG (cEEG).

METHODS

Consecutive adult patients treated with TTM after cardiac arrest were enrolled. We compared the SR from the Medtronic Vista monitor (MSR) to the SR generated from the full montage cEEG with Persyst Magic-Marker software (PSR). A blinded neurologist, board certified in epilepsy, scored the 4-channel aEEG pattern and the cEEG background using standardized terminology. Values for SR were compared to aEEG and cEEG categories using Kruskal-Wallis ANOVA, and to each other using Altman-Bland methodology.

RESULTS

23 adults treated with TTM had a mean core temperature of 33.8 °C at the time of SR and EEG background analysis. The MSR was 0% during continuous cEEG background, 23% when cEEG was discontinuous, and 64% during cEEG burst suppression (p = 0.01). The MSR was 0% during aEEG continuous patterns, 34% during aEEG burst suppression, and 46% during flat aEEG (p < 0.001). The MSR and PSR were highly correlated (0.88, p < 0.0001), with minimal bias (0.3%) and excellent 95% limits of agreement (-2.9 to 2.4%).

CONCLUSION

The Suppression Ratio from the Medtronic Vista monitor is highly correlated with the full montage SR from Persyst software. The MSR values are valid, changing with different aEEG patterns and cEEG background categories.

Relevance of Somatosensory Evoked Potential Amplitude After Cardiac Arrest

Objective: We present relations of SSEP amplitude with neurological outcome and of SSEP amplitude with EEG amplitude in comatose patients after cardiac arrest. Methods: This is a post hoc analysis of a prospective cohort study in comatose patients after cardiac arrest. Amplitude of SSEP recordings obtained within 48-72 h, and EEG patterns obtained at 12 and 24h after cardiac arrest were related to good (CPC 1-2) or poor (CPC 3-5) outcome at 6 months. In 39% of the study population multiple SSEP measurements were performed. Additionally, SSEP amplitude was related to mean EEG amplitude. Results: We included 138 patients (77% poor outcome). Absent SSEP responses, a N20 amplitude <0.4 μV within 48-72 h, and suppressed or synchronous EEG with suppressed background at 12 or 24 h after cardiac arrest were invariably associated with a poor outcome. Combined, these tests reached a sensitivity for prediction of poor outcome up to 58 at 100% specificity. N20 amplitude increased with a mean of 0.55 μV per day in patients with a poor outcome, and remained stable with a good outcome. There was no statistically significant correlation between SSEP and EEG amplitudes in 182 combined SSEP and EEG measurements (R ² < 0.01). Conclusions: N20 amplitude <0.4 μV is invariably associated with poor outcome. There is no correlation between SSEP and EEG amplitude. Significance: SSEP amplitude analysis may contribute to outcome prediction after cardiac arrest.

Background Frequency Patterns in Standard Electroencephalography as an Early Prognostic Tool in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management

We investigated the prognostic value of standard electroencephalography, a 30-min recording using 21 electrodes on the scalp, during the early post-cardiac arrest period, and evaluated the performance of electroencephalography findings combined with other clinical features for predicting favourable outcomes in comatose out-of-hospital cardiac arrest (OHCA) survivors treated with targeted temperature management (TTM). This observational registry-based study was conducted at a tertiary care hospital in Korea using the data of all consecutive adult non-traumatic comatose OHCA survivors who underwent standard electroencephalography during TTM between 2010 and 2018. The primary outcome was a 6-month favourable neurological outcome (Cerebral Performance Category score of 1 or 2). Among 170 comatose OHCA survivors with median electroencephalography time of 22 h, a 6-month favourable neurologic outcome was observed in 34.1% (58/170). After adjusting other clinical characteristics, an electroencephalography background with dominant alpha and theta waves had the highest odds ratio of 13.03 (95% confidence interval, 4.69–36.22) in multivariable logistic analysis. A combination of other clinical features (age < 65 years, initial shockable rhythm, resuscitation duration < 20 min) with an electroencephalography background with dominant alpha and theta waves increased predictive performance for favourable neurologic outcomes with a high specificity of up to 100%. A background with dominant alpha and theta waves in standard electroencephalography during TTM could be a simple and early favourable prognostic finding in comatose OHCA survivors.

Bedside interpretation of simplified continuous EEG after cardiac arrest

BACKGROUND

Continuous EEG-monitoring (cEEG) in the ICU is recommended to assess prognosis and detect seizures after cardiac arrest but implementation is often limited by the lack of EEG-technicians and experts. The aim of the study was to assess ICU physicians ability to perform preliminary interpretations of a simplified cEEG in the post cardiac arrest setting.

METHODS

Five ICU physicians received training in interpretation of simplified cEEG - total training duration 1 day. The ICU physicians then interpreted 71 simplified cEEG recordings from 37 comatose survivors of cardiac arrest. The cEEG included amplitude-integrated EEG trends and two channels with original EEG-signals. Basic EEG background patterns and presence of epileptiform discharges or seizure activity were assessed on 5-grade rank-ordered scales based on standardized EEG terminology. An EEG-expert was used as reference.

RESULTS

There was substantial agreement (κ 0.69) for EEG background patterns and moderate agreement (κ 0.43) for epileptiform discharges between ICU physicians and the EEG-expert. Sensitivity for detecting seizure activity by ICU physicians was limited (50%), but with high specificity (87%).

CONCLUSIONS

After cardiac arrest, preliminary bedside interpretations of simplified cEEGs by trained ICU physicians may allow earlier detection of clinically relevant cEEG changes, prompting changes in patient management as well as additional evaluation by an EEG-expert. This strategy requires awareness of limitations of both the simplified electrode montage and the cEEG interpretations performed by ICU physicians. cEEG evaluation by an expert should not be delayed.

Comparison of Quantitative Characteristics of Early Post-resuscitation EEG Between Asphyxial and Ventricular Fibrillation Cardiac Arrest in Rats

BACKGROUND

Quantitative electroencephalogram (EEG) analysis has shown promising results in studying brain injury and functional recovery after cardiac arrest (CA). However, whether the quantitative characteristics of EEG, as potential indicators of neurological prognosis, are influenced by CA causes is unknown. The purpose of this study was designed to compare the quantitative characteristics of early post-resuscitation EEG between asphyxial CA (ACA) and ventricular fibrillation CA (VFCA) in rats.

METHODS

Thirty-two Sprague-Dawley rats of both sexes were randomized into either ACA or VFCA group. Cardiopulmonary resuscitation was initiated after 5-min untreated CA. Characteristics of early post-resuscitation EEG were compared, and the relationships between quantitative EEG features and neurological outcomes were investigated.

RESULTS

Compared with VFCA, serum level of S100B, neurological deficit score and brain histopathologic damage score were dramatically higher in the ACA group. Quantitative measures of EEG, including onset time of EEG burst, time to normal trace, burst suppression ratio, and information quantity, were significantly lower for CA caused by asphyxia and correlated with the 96-h neurological outcome and survival.

CONCLUSIONS

Characteristics of earlier post-resuscitation EEG differed between cardiac and respiratory causes. Quantitative measures of EEG not only predicted neurological outcome and survival, but also have the potential to stratify CA with different causes.

Hyperglycemia and Glucose Variability Are Associated with Worse Brain Function and Seizures in Neonatal Encephalopathy: A Prospective Cohort Study

OBJECTIVES

To investigate how glucose abnormalities correlate with brain function on amplitude-integrated electroencephalography (aEEG) in infants with neonatal encephalopathy.

STUDY DESIGN

Neonates born at full term with encephalopathy were enrolled within 6 hours of birth in a prospective cohort study at a pediatric academic referral hospital. Continuous interstitial glucose monitors and aEEG were placed soon after birth and continued for 3 days. Episodes of hypoglycemia (≤50 mg/dL; ≤2.8 mmol/L) and hyperglycemia (>144 mg/dL; >8.0 mmol/L) were identified. aEEG was classified in 6-hour epochs for 3 domains (background, sleep-wake cycling, electrographic seizures). Generalized estimating equations assessed the relationship of hypo- or hyperglycemia with aEEG findings, adjusting for clinical markers of hypoxia-ischemia (Apgar scores, umbilical artery pH, and base deficit).

RESULTS

Forty-five infants (gestational age 39.5 ± 1.4 weeks) were included (24 males). During aEEG monitoring, 16 episodes of hypoglycemia were detected (9 infants, median duration 77.5, maximum 220 minutes) and 18 episodes of hyperglycemia (13 infants, median duration 237.5, maximum 3125 minutes). Epochs of hypoglycemia were not associated with aEEG changes. Compared with epochs of normoglycemia, epochs of hyperglycemia were associated with worse aEEG background scores (B 1.120, 95% CI 0.501-1.738, P < .001), less sleep-wake cycling (B 0.587, 95% CI 0.417-0.757, P < .001) and more electrographic seizures (B 0.433, 95% CI 0.185-0.681, P = .001), after adjusting for hypoxia-ischemia severity.

CONCLUSIONS

In neonates with encephalopathy, epochs of hyperglycemia were temporally associated with worse global brain function and seizures, even after we adjusted for hypoxia-ischemia severity. Whether hyperglycemia causes neuronal injury or is simply a marker of severe brain injury requires further study.

Prognostic value of phase information of 2D T2*-weighted gradient echo brain imaging in cardiac arrest survivors: A preliminary study

BACKGROUND

Predicting neurological outcomes after cardiac arrest is important to guide therapeutic decisions. We assessed the prognostic value of phase information of 2D T2*-weighted gradient echo imaging (T2*WI) of the brain in CA survivors.

METHODS

This study included cardiac arrest survivors who had undergone MRI for prognostication. After application of homodyne filtering to T2*WI phase images, the contrast of three venous structures was assessed as normal (score 0) or abnormal (score 1): the superior sagittal sinus, the cortical veins, and the thalamostriate veins. The scores were summarized into a gradient-recalled echo (GRE) summary score. The prognostic performances of T2*WI, diffusion-weighted imaging (DWI), electroencephalography and serum biomarkers were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Of the 39 enrolled patients, 12 (31%) had good outcomes and 27 (69%) had poor outcomes. ROC curve analysis showed that T2*WI had good prognostic performance; the area under the curve (AUC) of the GRE summary score (0.980, 95% confidence interval CI 0.950-1.000) was comparable to those of conventional outcome predictors, including DWI patterns (0.949, 95% CI 0.889-1.000). The AUC increased when the summary GRE score was added to DWI patterns (0.991, 95% CI 0.973-1.000), although the difference was not statistically significant (P=0.117). Most subjects with isoelectric electroencephalography (5/6) showed abnormally high phase contrast in the cerebral veins.

CONCLUSIONS

Filtered phase images of T2*WI showed good prognostic value and can reveal various features of the cerebral metabolic consequences of cardiac arrest, such as decreased neuronal activity and brain death-like patterns.