Clinical review: Continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest

Developing predictive nomogram models using quantitative electroencephalography for brain function in type a aortic dissection: a prospective observational study

BACKGROUND

Type A aortic dissection (TAAD) remains a significant challenge in cardiac surgery, presenting high risks of adverse outcomes such as permanent neurological dysfunction and mortality despite advances in medical technology and surgical techniques. This study investigates the use of quantitative electroencephalography (QEEG) to monitor and predict neurological outcomes during the perioperative period in TAAD patients.

METHODS

This prospective observational study was conducted at the hospital, involving patients undergoing TAAD surgery from February 2022 to January 2023. QEEG parameters, including the dynamic amplitude-integrated electroencephalography (aEEG) grade, which assesses changes in brain function over time, alongside aEEG and relative band power (RBP), were monitored and analyzed to assess brain function preoperatively, intraoperatively, and within 2 hours postoperatively. A predictive nomogram model was developed using these QEEG metrics along with other clinical variables to forecast neurological outcomes.

RESULTS

In this study, we analyzed the factors contributing to adverse outcomes (AO) and transient neurological dysfunction (TND) following TAAD surgery. For AO, multivariable analysis identified pre-mental status (odds ratio [OR] = 4.652, 95% confidence interval [CI] = 2.316-10.074, P < 0.001), cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006-1.023, P = 0.001), and dynamic aEEG grade (OR = 9.926, 95% CI = 4.493-25.268, P < 0.001) as independent risk factors. The AO model showed high discriminative ability with an area under the curve of 0.888 (95% CI = 0.818-0.960) and good calibration (Brier score = 0.0728). For TND, significant preoperative differences included dynamic aEEG grade ( P < 0.001) and Log(Post-RBP Alpha%) (6.00 vs. 4.00, P < 0.001). Multivariable analysis identified cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006-1.023, P = 0.001), Post-RBP Alpha% (OR = 0.263, 95% CI = 0.121-0.532, P < 0.001), and dynamic aEEG grade (OR = 12.444, 95% CI = 5.337-30.814, P < 0.001) as independent risk factors. The TND model had an area under the curve of 0.893 (95% CI = 0.844-0.941) and good calibration (Brier score = 0.125). These findings highlight the role of QEEG in predicting postoperative neurological dysfunction in TAAD patients.

CONCLUSION

Through perioperative QEEG monitoring of TAAD patients, combined with clinical indicators such as cardiopulmonary bypass time and preoperative mental status, we developed clinical predictive models for AO and TND after surgery. These models allow for early detection of postoperative brain function impairment, as assessed by QEEG parameters monitored intraoperatively and during the first 2 hours after surgery, a period chosen based on clinical definitions of delayed awakening and supported by the findings of this study. This study provides evidence supporting postoperative brain function monitoring in TAAD patients, with potential clinical implications for improved outcomes.

Quantitative Electroencephalography for Predication of Neurological Dysfunction in Type A Aortic Dissection: A Prospective Observational Study

BACKGROUND

Type A aortic dissection presents challenges with postoperative cerebral complications, and this study evaluates the predictive value of quantitative electroencephalography for perioperative brain function prognosis.

METHODS AND RESULTS

Amplitude-integrated electroencephalography (aEEG) processes raw signals through filtering, amplitude integration, and time compression, displaying the data in a semilogarithmic format. Using this method, postoperative relative band power (post-RBP) α% and dynamic aEEG (ΔaEEG) grade were significantly associated with neurological dysfunction in univariate and multivariable analyses, with area under the receiver operating characteristic curve of 0.876 (95% CI, 0.825-0.926) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with adverse outcomes, with area under the receiver operating characteristic curve of 0.903 (95% CI, 0.835-0.971) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with transient neurological dysfunction and stroke, with areas under the receiver operating characteristic curve of 0.818 (95% CI, 0.760-0.876) and 0.868 (95% CI, 0.810-0.926) for transient neurological dysfunction, and 0.815 (95% CI, 0.743-0.886) and 0.831 (95% CI, 0.746-0.916) for stroke. Among 56 patients, the Alberta Stroke Program Early Computed Tomography score was superior to ΔaEEG in predicting neurological outcomes (area under the receiver operating characteristic curve of 0.872 versus 0.708 [95% CI, 0.633-0.783]; P<0.05).

CONCLUSIONS

Perioperative quantitative electroencephalography monitoring offers valuable insights into brain function changes in patients with type A aortic dissection. ∆aEEG grades can aid in early detection of adverse outcomes, while postoperative relative band power and ∆aEEG grades predict transient neurological dysfunction. Quantitative electroencephalography can assist cardiac surgeons in assessing brain function and improving outcomes in patients with type A aortic dissection.

REGISTRATION

URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2200055980.

Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study

Temporary neurological dysfunction (TND), a common complication following surgical repair of Type A Aortic Dissection (TAAD), is closely associated with increased mortality and long-term cognitive impairment. Currently, effective treatment options for TND remain elusive. Therefore, we sought to investigate the potential of postoperative relative band power (RBP) in predicting the occurrence of postoperative TND, with the aim of identifying high-risk patients prior to the onset of TND. We conducted a prospective observational study between February and December 2022, involving 165 patients who underwent surgical repair for TAAD at our institution. Bedside Quantitative electroencephalography (QEEG) was utilized to monitor the post-operative brain electrical activity of each participant, recording changes in RBP (RBP Delta, RBP Theta, RBP Beta and RBP Alpha), and analyzing their correlation with TND. Univariate and multivariate analyses were employed to identify independent risk factors for TND. Subsequently, line graphs were generated to estimate the incidence of TND. The primary outcome of interest was the development of TND, while secondary outcomes included intensive care unit (ICU) admission and length of hospital stay. A total of 165 patients were included in the study, among whom 68 (41.2%) experienced TND. To further investigate the independent risk factors for postoperative TND, we conducted both univariate and multivariate logistic regression analyses on all variables. In the univariate regression analysis, we identified age (Odds Ratio [OR], 1.025; 95% CI, 1.002-1.049), age ≥ 60 years (OR, 2.588; 95% CI, 1.250-5.475), hemopericardium (OR, 2.767; 95% CI, 1.150-7.009), cardiopulmonary bypass (CPB) (OR, 1.007; 95% CI, 1.001-1.014), RBP Delta (OR, 1.047; 95% CI, 1.020-1.077), RBP Alpha (OR, 0.853; 95% CI, 0.794-0.907), and Beta (OR, 0.755; 95% CI, 0.649-0.855) as independent risk factors for postoperative TND. Further multivariate regression analyses, we discovered that CPB time ≥ 180 min (OR, 1.021; 95% CI, 1.011-1.032), RBP Delta (OR, 1.168; 95% CI, 1.105-1.245), and RBP Theta (OR, 1.227; 95% CI, 1.135-1.342) emerged as independent risk factors. TND patients had significantly longer ICU stays (p < 0.001), and hospital stays (p = 0.002). We obtained the simplest predictive model for TND, consisting of three variables (CPB time ≥ 180 min, RBP Delta, RBP Theta, upon which we constructed column charts. The areas under the receiver operating characteristic (AUROC) were 0.821 (0.755, 0.887). Our study demonstrates that postoperative RBP monitoring can detect changes in brain function in patients with TAAD during the perioperative period, providing clinicians with an effective predictive method that can help improve postoperative TND in TAAD patients. These findings have important implications for improving clinical care in this population.Trial registration ChiCTR2200055980. Registered 30th Jan. 2022. This trial was registered before the first participant was enrolled.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.

"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.

A predictive model for consciousness recovery of comatose patients after acute brain injury

Background

Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery.

Objectives

We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury.

Methods

The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model.

Results

A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, P < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, P = 0.038), EEG background activity (OR = 4.309, P = 0.023), EEG reactivity (OR = 4.154, P = 0.030), and sleep spindles (OR = 4.316, P = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA.

Conclusion

This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels

BACKGROUND

In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results.

NEW METHOD

Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks.

COMPARISON WITH EXISTING METHOD

The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5-100 subjects.

CONCLUSIONS

Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation.

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.

Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring

OBJECTIVE

To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication.

METHODS

We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1-2 and poor outcome as CPC of 3-5.

RESULTS

An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility (u) assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3-4) worse than death (u = -0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687).

CONCLUSION

Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.

aEEG Use in Pediatric Critical Care-An Online Survey

Background: Evidence supporting continuous EEG monitoring in pediatric intensive care is increasing, but continuous full-channel EEG is a scarce resource. Amplitude-integrated EEG (aEEG) monitors are broadly available in children's hospitals due to their use in neonatology and can easily be applied to older patients. Objective: The aim of this survey was to evaluate the use of amplitude-integrated EEG in German and Swiss pediatric intensive care units (PICUs). Design: An online survey was sent to German and Swiss PICUs that were identified via databases provided by the German Pediatric Association (DGKJ) and the Swiss Society of Intensive Care (SGI). The questionnaire contained 18 multiple choice questions including the PICU size and specialization, indications for aEEG use, perceived benefits from aEEG, and data storage. Main results: Forty-three (26%) PICUs filled out the questionnaire. Two thirds of all interviewed PICUs use aEEG in non-neonates. Main indications were neurological complications or disease and altered mental state. Features assessed were mostly seizures and side differences, less frequently height of amplitude and background pattern. Interpretation of raw EEG also played an important role. All interviewees would appreciate the establishment of reference values for toddlers and children. Conclusions: aEEG is used in a large proportion of the interviewed PICUs. The wide-spread use without validation of data generates the need for further evaluation of this technique and the establishment of reference values for non-neonates.

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.

The Use of Continuous EEG Monitoring in Intensive Care Units in The Netherlands: A National Survey

Background

Currently, continuous electroencephalographic monitoring (cEEG) is the only available diagnostic tool for continuous monitoring of brain function in intensive care unit (ICU) patients. Yet, the exact relevance of routinely applied ICU cEEG remains unclear, and information on the implementation of cEEG, especially in Europe, is scarce. This study explores current practices of cEEG in adult Dutch ICU departments focusing on organizational and operational factors, development over time and factors perceived relevant for abstaining its use.

Methods

A national survey on cEEG in adults among the neurology and adult intensive care departments of all Dutch hospitals (n = 82) was performed.

Results

The overall institutional response rate was 78%. ICU cEEG is increasingly used in the Netherlands (in 37% of all hospitals in 2016 versus in 21% in 2008). Currently in 88% of university, 55% of teaching and 14% of general hospitals use ICU cEEG. Reasons for not performing cEEG are diverse, including perceived non-feasibility and lack of data on the effect of cEEG use on patient outcome. Mostly, ICU cEEG is used for non-convulsive seizures or status epilepticus and prognostication. However, cEEG is never or rarely used for monitoring cerebral ischemia and raised intracranial pressure in traumatic brain injury. Review and reporting practices differ considerably between hospitals. Nearly all hospitals perform non-continuous review of cEEG traces. Methods for moving toward continuous review of cEEG traces are available but infrequently used in practice.

Conclusions

cEEG is increasingly used in Dutch ICUs. However, cEEG practices vastly differ between hospitals. Future research should focus on uniform cEEG practices including unambiguous EEG interpretation to facilitate collaborative research on cEEG, aiming to provide improved standard patient care and robust data on the impact of cEEG use on patient outcome.

Amplitude-Integrated Electroencephalography and Brain Oxygenation for Postcardiac Arrest Patients with Targeted Temperature Management

Brain injury is the most common cause of death postcardiac arrest. Amplitude-integrated electroencephalography (aEEG) is suggested to be useful in the prognostication in cases of postcardiac arrest brain injury. However, combined monitoring with aEEG and regional oxygen saturation (rSO₂) for postcardiac arrest syndrome (PCAS) patients to improve accuracy has not been reported. The purpose of this prospective observational study is to assess the usefulness of aEEG and rSO₂ for PCAS patients with targeted temperature management (TTM) to predict neurological outcome and possibly identify the pathophysiology of postcardiac arrest brain injury. PCAS patients with TTM at 34°C were monitored by aEEG and rSO₂ immediately after admission to the intensive care unit and evaluated at the start of monitoring, and 24 and 48 hours after return of spontaneous circulation (ROSC). Patients were divided into two groups according to electroencephalography (EEG) pattern: a continuous EEG (C) pattern group and a noncontinuous EEG (NC) pattern group. Patients with C pattern had a significantly more favorable neurologic outcome compared with patients with an NC pattern at each point in time. No significant difference in rSO₂ values was observed between the C pattern and the NC pattern at any time point. Variation coefficient at rSO₂ in the NC group was significantly greater than that in the C group from the start of the monitoring to 24 hours. aEEG is useful in predicting outcome for PCAS patients whereas rSO₂ is not.

Management of post-cardiac arrest syndrome

Post-cardiac arrest syndrome is a complex and critical issue in resuscitated patients undergone cardiac arrest. Ischemic-reperfusion injury occurs in multiple organs due to the return of spontaneous circulation. Bundle of management practicies are required for post-cardiac arrest care. Early invasive coronary angiography should be considered to identify and treat coronary artery obstructive disease. Vasopressors such as norepinephrine and dobutamine are the first-line treatment for shock. Maintainance of oxyhemoglobin saturation greater than 94% but less than 100% is recommended to avoid fatality. Target temperature therapeutic hypothermia helps to resuscitated patients. Strict temperature control is required and is maintained with the help of cooling devices and monitoring the core temperature. Montorings include electrocardiogram, oxymetry, capnography, and electroencephalography (EEG) along with blood pressue, temprature, and vital signs. Seizure should be treated if EEG shows evidence of seizure or epileptiform activity. Clinical neurologic examination and magnetic resonance imaging are considered to predict neurological outcome. Glycemic control and metabolic management are favorable for a good neurological outcome. Recovery from acute kidney injury is essential for survival and a good neurological outcome.

Amplitude-Integrated EEG for Neurological Assessment and Seizure Detection in a German Pediatric Intensive Care Unit

Objective: The aim of our study was to assess the use of aEEG in our pediatric intensive care unit (PICU), indications for neuromonitoring and its findings, utility for seizure detection, and associations with outcome. Design: We retrospectively analyzed non-neonates who were treated in our PICU and received amplitude-integrated EEG (aEEG). Patients: 27 patients aged between 29 days and 10 0/12 years (median 7.3 months) were included, who received a total of 35 aEEGS. Measurements: aEEG tracings were assessed for background (BG) pattern and its evolution, seizures, and side differences using a visual classification (Hellström-Westas). Clinical data were collected from patients' histories and analyzed for correlation with aEEG findings. Main results: While rare in early years, there was an increase in use over time. Most aEEGs were conducted because of (suspected) seizures or for management of antiepileptic treatment. aEEG had low sensitivity but high specificity for recognition of pathological BG pattern with reference to conventional EEG. Worsening of BG pattern or failure to improve was associated with death. Seizure detection rates by aEEG were higher than by clinical observation, especially for identification of non-convulsive epileptic state (ES). Side differences in aEEG were rare, but if present, they were associated with unilateral brain injury. Conclusions: aEEG is useful for the detection of seizures and ES in pediatric intensive care patients. Abnormal BG pattern and poor evolution of BG are negatively associated with survival. aEEG is a potential supplement to conventional EEG, facilitating long-term surveillance of cerebral function when continuous full-channel EEG is not available. Further investigation is needed.

Targeted Temperature Management and Multimodality Monitoring of Comatose Patients After Cardiac Arrest

Out-of-hospital cardiac arrest (CA) remains a leading cause of sudden morbidity and mortality; however, outcomes have continued to improve in the era of targeted temperature management (TTM). In this review, we highlight the clinical use of TTM, and provide an updated summary of multimodality monitoring possible in a modern ICU. TTM is neuroprotective for survivors of CA by inhibiting multiple pathophysiologic processes caused by anoxic brain injury, with a final common pathway of neuronal death. Current guidelines recommend the use of TTM for out-of-hospital CA survivors who present with a shockable rhythm. Further studies are being completed to determine the optimal timing, depth and duration of hypothermia to optimize patient outcomes. Although a multidisciplinary approach is necessary in the CA population, neurologists and neurointensivists are central in selecting TTM candidates and guiding patient care and prognostic evaluation. Established prognostic tools include clinal exam, SSEP, EEG and MR imaging, while functional MRI and invasive monitoring is not validated to improve outcomes in CA or aid in prognosis. We recommend that an evidence-based TTM and prognostication algorithm be locally implemented, based on each institution's resources and limitations. Given the high incidence of CA and difficulty in predicting outcomes, further study is urgently needed to determine the utility of more recent multimodality devices and studies.

Neurological Prognostications for the Therapeutic Hypothermia among Comatose Survivors of Cardiac Arrest

Background

Currently, there are limited data of prognostic clues for neurological recovery in comatose survivors undergoing therapeutic hypothermia (TH). We aimed to evaluate clinical signs and findings that could predict neurological outcomes, and determine the optimal time for the prognostication.

Materials and Methods

We retrospectively reviewed database of postarrest survivors treated with TH in our hospital from 2006 to 2014. Cerebral performance category (CPC), neurological signs and findings in electroencephalography (EEG) and brain computed tomography (CT) were evaluated. In addition, the optimal time to evaluate neurological status was analyzed.

Results

TH was performed in 51 postarrest patients. Approximately 53% of TH patients survived at discharge and 33% of the hospital survivors had favorable outcome (CPC1-2). The prognostic clues for unfavorable outcome (CPC3-5) at discharge were lack of pupillary light response (PLR) and/or gag reflex after rewarming, and the absence of at least one of the brainstem reflexes, no eye-opening, or abnormal motor response on the 7^th day. Myoclonus and seizure could not be used to indicate poor prognosis. In addition, prognostic values of EEG and CT findings were inconclusive.

Conclusions

Our study showed the simple neurological signs helped predict short-term neurological prognosis. The most reliable sign determining unfavorable outcome was the lack of PLR. The optimal time to assess prognosis was either at 48-72 h or 7 days after return of spontaneous circulation.

Effect of sedation on quantitative electroencephalography after cardiac arrest

BACKGROUND

Electroencephalography (EEG) has clinical and prognostic importance after cardiac arrest (CA). Recently, interest in quantitative EEG (qEEG) analysis has grown. The qualitative effects of sedation on EEG are well known, but potentially confounding effects of sedatives on qEEG after anoxic injury are poorly characterized. We hypothesize that sedation increases suppression ratio (SR) and decreases alpha/delta ratio (ADR) and amplitude-integrated EEG (aEEG), and that the magnitude of sedation effects will be associated with outcome.

METHODS

We routinely monitor comatose post-arrest patients with EEG for 48-72h. We included comatose EEG-monitored patients after CA who had protocolized daily sedation interruptions. We used Persyst v12 to quantify qEEG parameters and calculated medians for 10min immediately prior to sedation interruption and for the last 5min of interruption. We used paired t-tests to determine whether qEEG parameters changed with sedation cessation, and logistic regression to determine whether these changes predicted functional recovery or survival at discharge.

RESULTS

78 subjects were included (median age 56, 65% male). Interruptions occurred a median duration of 34h post-arrest and lasted a median duration of 60min. Prior to interruption, higher aEEG predicted survival, while lower SR predicted both survival and favorable outcome. During interruption, SR decreased (p<0.001), aEEG increased (p=0.002), and ADR did not change. Larger decreases in SR predicted decreased survival (OR=1.04 per percent change; 95% CI 1.00-1.09).

CONCLUSION

Higher aEEG and lower SR predict survival after CA. Sedation alters aEEG and SR, but importantly does not appear to affect the relationship between these parameter values and outcome.

Accuracy of Limited-Montage Electroencephalography in Monitoring Postanoxic Comatose Patients

BACKGROUND

Continuous EEG (cEEG) monitoring may help to identify the small percentage of adults with hypoxic-ischemic encephalopathy (HIE) who will regain consciousness if allowed sufficient time. However, the limited yield in this population has led some to question the cost-effectiveness cEEG monitoring in this population. We hypothesized that limited-montage cEEG could provide essentially the same neurophysiologic information at lower cost. In this proof of concept study, we aim to demonstrate the potentials of limited channel EEG in prognostication in postanoxic patients.

METHODS

We retrospectively reviewed cEEG data from cases monitored at our institution with conventional 21-channel EEG over a 6-month period. Twenty-eight cases were identified in which patients with HIE underwent cEEG for at least 24 hours. Gold-standard findings were determined by conventional visual analysis of the full cEEG, and 2 independent electroencephalographers scored the same data using only limited-montage (4-channel) views. The sensitivity and specificity of limited-montage cEEG review were compared with conventional analysis. We also compared the relative costs of conventional and limited-montage EEG.

RESULTS

Using 4-channel limited montage cEEG, reviewers were able to classify accurately background continuity (in 88%), background amplitude (in 81%), maximum background frequency (in 70%), periodic epileptiform discharges, including a seizure (in 92%) and sporadic discharges (in 91%). All epileptiform features were detected with greater than 90% sensitivity and specificity. Eye movement artifact seen over bifrontal electrodes gave false positive detections of periodic epileptiform discharges in 31% of cases.

CONCLUSIONS

Limited-channel continuous EEG monitoring can provide meaningful electrophysiological data that can be used for prognostication in postanoxic comatose patients. Limited channel EEG can be a cost-effective alternative to conventional EEG monitoring in post-anoxic comatose patients.

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

BACKGROUND

Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR).

METHODS

We included comatose CA patients hospitalized from April 2010 to October 2014, excluding CA from trauma or neurological catastrophe. We used Persyst^®v12 to generate SR trends and used semi-quantitative methods to choose appropriate sampling and averaging strategies. We used GBTM to partition SR data into different trajectories and regression associate trajectories with outcome. We derived a multivariate logistic model using clinical variables without qEEG to predict survival, then added trajectories and/or non-longitudinal SR estimates, and assessed changes in model performance.

RESULTS

Overall, 289 CA patients had ≥36 h of EEG yielding 10,404 h of data (mean age 57 years, 81 % arrested out-of-hospital, 33 % shockable rhythms, 31 % overall survival, 17 % discharged to home or acute rehabilitation). We identified 4 distinct SR trajectories associated with survival (62, 26, 12, and 0 %, P < 0.0001 across groups) and CPC (35, 10, 4, and 0 %, P < 0.0001 across groups). Adding trajectories significantly improved model performance compared to adding non-longitudinal data.

CONCLUSIONS

Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.

Derivation and Validation of the CREST Model for Very Early Prediction of Circulatory Etiology Death in Patients Without ST-Segment-Elevation Myocardial Infarction After Cardiac Arrest

BACKGROUND

No practical tool quantitates the risk of circulatory-etiology death (CED) immediately after successful cardiopulmonary resuscitation in patients without ST-segment-elevation myocardial infarction. We developed and validated a prediction model to rapidly determine that risk and facilitate triage to individualized treatment pathways.

METHODS

With the use of INTCAR (International Cardiac Arrest Registry), an 87-question data set representing 44 centers in the United States and Europe, patients were classified as having had CED or a combined end point of neurological-etiology death or survival. Demographics and clinical factors were modeled in a derivation cohort, and backward stepwise logistic regression was used to identify factors independently associated with CED. We demonstrated model performance using area under the curve and the Hosmer-Lemeshow test in the derivation and validation cohorts, and assigned a simplified point-scoring system.

RESULTS

Among 638 patients in the derivation cohort, 121 (18.9%) had CED. The final model included preexisting coronary artery disease (odds ratio [OR], 2.86; confidence interval [CI], 1.83-4.49; P≤0.001), nonshockable rhythm (OR, 1.75; CI, 1.10-2.77; P=0.017), initial ejection fraction<30% (OR, 2.11; CI, 1.32-3.37; P=0.002), shock at presentation (OR, 2.27; CI, 1.42-3.62; P<0.001), and ischemic time >25 minutes (OR, 1.42; CI, 0.90-2.23; P=0.13). The derivation model area under the curve was 0.73, and Hosmer-Lemeshow test P=0.47. Outcomes were similar in the 318-patient validation cohort (area under the curve 0.68, Hosmer-Lemeshow test P=0.41). When assigned a point for each associated factor in the derivation model, the average predicted versus observed probability of CED with a CREST score (coronary artery disease, initial heart rhythm, low ejection fraction, shock at the time of admission, and ischemic time >25 minutes) of 0 to 5 was: 7.1% versus 10.2%, 9.5% versus 11%, 22.5% versus 19.6%, 32.4% versus 29.6%, 38.5% versus 30%, and 55.7% versus 50%.

CONCLUSIONS

The CREST model stratified patients immediately after resuscitation according to risk of a circulatory-etiology death. The tool may allow for estimation of circulatory risk and improve the triage of survivors of cardiac arrest without ST-segment-elevation myocardial infarction at the point of care.

Continuous electroencephalographic-monitoring in the ICU: an overview of current strengths and future challenges

PURPOSE OF REVIEW

In ICUs, numerous physiological parameters are continuously monitored and displayed. Yet, functional monitoring of the organ of primary concern, the brain, is not routinely performed. Despite the benefits of ICU use of continuous electroencephalographic (EEG)-monitoring (cEEG) is increasingly recognized, several issues nevertheless seem to hamper its widespread clinical implementation.

RECENT FINDINGS

Utilization of ICU cEEG has significantly improved detection and characterization of cerebral pathology, prognostication and clinical management in specific patient groups. Potential solutions to several remaining challenges are currently being established. Descriptive EEG-terminology is evolving, whereas logistical issues are dealt with using telemedicine and quantitative EEG trends, training of nonexpert personnel and development of specialized detection algorithms. These concerted solutions are advancing cEEG-registration towards cEEG-monitoring. Notwithstanding these advances, obstacles such as ambiguous EEG-interpretation and differences in treatment based on EEG-findings need yet to be overcome.

SUMMARY

In selected critically ill patient groups, ICU cEEG has clear benefits over (repeated) standard EEG or no functional brain monitoring at all and if available, cEEG should be used. However, several issues preventing optimal ICU cEEG usage persist and should be further explored.

Minimal EEG montage with high yield for the detection of status epilepticus in the setting of postanoxic brain damage

For the diagnosis of electrographic seizures or status epilepticus, we reduced the number of EEG-electrodes to make urgent EEG monitoring more feasible. Unlike the current existing research, with mixed results, we studied a specific population with postanoxic brain damage, expecting a higher yield of detection of ictal EEG patterns. In a population treated with therapeutic hypothermia post-cardiac arrest, the initial EEGs were reformatted in a longitudinal, a hairline and an 8-lead montage, and independently reviewed by two investigators. The EEGs were categorized into three categories: one without ictal EEG activity, one with interictal activity and one with probable electrographic seizure(s). Generalized ictal EEG activity was the most frequently observed EEG pattern. The average sensitivity for the detection of probable electrographic seizure(s) was 100 % for the 8-lead montage and 92 % in the hairline montage. In comparison to the routine longitudinal montage, the 8-lead montage proved to be reliable for the detection of electrographic seizure activity in a postanoxic population even with limited training in EEG interpretation. The hairline montage did not suffice with regard to the differential diagnosis of triphasic waves associated with metabolic encephalopathy and generalized nonconvulsive status epilepticus, but nonetheless detected the vast majority of probable electrographic seizure(s). Our results support the use of EEG monitoring with fewer electrodes for the detection of ictal EEG activity in the postanoxic population.

Critical Care Management Focused on Optimizing Brain Function After Cardiac Arrest

The discussion of neurocritical care management in post-cardiac arrest syndrome (PCAS) has generally focused on target values used for targeted temperature management (TTM). There has been less attention paid to target values for systemic and cerebral parameters to minimize secondary brain damage in PCAS. And the neurologic indications for TTM to produce a favorable neurologic outcome remain to be determined. Critical care management of PCAS patients is fundamental and essential for both cardiologists and general intensivists to improve neurologic outcome, because definitive therapy of PCAS includes both special management of the cause of cardiac arrest, such as coronary intervention to ischemic heart disease, and intensive management of the results of cardiac arrest, such as ventilation strategies to avoid brain ischemia. We reviewed the literature and the latest research about the following issues and propose practical care recommendations. Issues are (1) prediction of TTM candidate on admission, (2) cerebral blood flow and metabolism and target value of them, (3) seizure management using continuous electroencephalography, (4) target value of hemodynamic stabilization and its method, (5) management and analysis of respiration, (6) sedation and its monitoring, (7) shivering control and its monitoring, and (8) glucose management. We hope to establish standards of neurocritical care to optimize brain function and produce a favorable neurologic outcome.

The Brain after Cardiac Arrest

Cardiac arrest is common and deadly. Most patients who are treated in the hospital after achieving return of spontaneous circulation still go on to die from the sequelae of anoxic brain injury. In this review, the authors provide an overview of the mechanisms and consequences of postarrest brain injury. Special attention is paid to potentially modifiable mechanisms of secondary brain injury including seizures, hyperpyrexia, cerebral hypoxia and hypoperfusion, oxidative injury, and the development of cerebral edema. Finally, the authors discuss the outcomes of cardiac arrest survivors with a focus on commonly observed patterns of injury as well as the scales used to measure patient outcome and their limitations.

EEG as an Indicator of Cerebral Functioning in Postanoxic Coma

Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.

Guiding Antiepileptic Therapy in a Pediatric Patient with Severe Meningoencephalitis and Decompressive Craniectomy with the Use of Amplitude-Integrated Electroencephalography

Introduction Amplitude-integrated electroencephalography (aEEG) is one of the most widely used neuromonitoring tools in neonatology today. However, little is known about its clinical indications and potential benefits in pediatric intensive care patients. Based on limited experience, its impact on therapeutic decision-making in this patient population is unclear. Case Description We report the case of a 16-year-old boy who, after a pansinusitis, developed a severe meningoencephalitis and intracranial empyema with increased intracranial pressure that required drainage and decompressive craniectomy. He subsequently developed status epilepticus despite a combination of various anticonvulsants. Only after the initialization of an aEEG, we were able to adequately diagnose and continuously monitor his seizure activity and titrate the effect of the antiepileptic drugs. During his hospital stay, we were able to clearly monitor and guide our therapy by accurately identifying the termination of status epilepticus and the recurrence of seizures. Discussion With the help of aEEG, it was easy to identify the nonconvulsive status epilepticus (NCSE) and the ongoing seizure activity in this teenage patient. NCSE is a clinical problem with an effect on the outcome of the patient and is often underdiagnosed. AEEG enabled a rapid detection and management of seizure activity and thereby reduced the overall seizure burden, which was associated with better neurologic outcome.

Neurocritical care update

This update comprises six important topics under neurocritical care that require reevaluation. For post-cardiac arrest brain injury, the evaluation of the injury and its corresponding therapy, including temperature modulation, is required. Analgosedation for target temperature management is an essential strategy to prevent shivering and minimizes endogenous stress induced by catecholamine surges. For severe traumatic brain injury, the diverse effects of therapeutic hypothermia depend on the complicated pathophysiology of the condition. Continuous electroencephalogram monitoring is an essential tool for detecting nonconvulsive status epilepticus in the intensive care unit (ICU). Neurocritical care, including advanced hemodynamic monitoring, is a fundamental approach for delayed cerebral ischemia following subarachnoid hemorrhage. We must be mindful of the high percentage of ICU patients who may develop sepsis-associated brain dysfunction.

Clinical characteristics of non-convulsive status epilepticus diagnosed by simplified continuous electroencephalogram monitoring at an emergency intensive care unit

Aim

The present study aimed to elucidate the clinical characteristics of non‐convulsive status epilepticus (NCSE) in patients with altered mental status (AMS).

Methods

This single‐center retrospective study comprised 149 patients who were hospitalized between March 1, 2015 and September 30, 2015 at the emergency intensive care unit (ICU) of the Kagawa University Hospital (Kagawa, Japan). The primary outcome was NCSE incidence. The secondary outcome was the comparison of duration of ICU stay, hospital stay, and a favorable neurological outcome, as assessed using the modified Rankin Scale score, at discharge from our hospital between patients with and without NCSE. Favorable neurological outcome and poor neurological outcome were defined as modified Rankin Scale scores of 0–2 and 3–6, respectively.

Results

Simplified continuous electroencephalogram was used to monitor 36 patients (median age, 68 years; 69.4% males) with acute AMS; among them, NCSE was observed in 11 (30.1%) patients. Rates of favorable neurological outcome, duration of ICU stay, and hospital stay were not significantly different between the NCSE and non‐NCSE groups (P = 0.45, P = 0.30, and P = 0.26, respectively).

Conclusion

Approximately 30% of the patients with AMS admitted to emergency ICUs developed NCSE. The outcomes of AMS patients with and without NCSE did not differ significantly when appropriate medical attention and antiepileptic drugs were initiated. Simplified continuous electroencephalogram monitoring may be recommended in patients with AMS in emergency ICU to obtain early detection of NCSE followed by appropriate intervention.

EEG in postanoxic coma: Prognostic and diagnostic value

Evolution of the EEG background pattern is a robust contributor to prediction of poor or good outcome of comatose patients after cardiac arrest. At 24h, persistent isoelectricity, low voltage activity, or burst-suppression with identical bursts predicts a poor outcome without false positives. Rapid recovery toward continuous patterns within 12h is strongly associated with a good neurological outcome. Predictive values are highest in the first 24h, despite the use of mild therapeutic hypothermia and sedative medication. Studies on reactivity or mismatch negativity have not included the EEG background pattern. Therefore, the additional predictive value of reactivity parameters remains unclear. Whether or not treatment of electrographic status epilepticus improves outcome is studied in the randomized multicenter Treatment of Electroencephalographic STatus epilepticus After cardiopulmonary Resuscitation (TELSTAR) trial (NCT02056236).

Breakthrough in cardiac arrest: reports from the 4th Paris International Conference

Jean-Luc Diehl The French Intensive Care Society organized on 5th and 6th June 2014 its 4th "Paris International Conference in Intensive Care", whose principle is to bring together the best international experts on a hot topic in critical care medicine. The 2014 theme was "Breakthrough in cardiac arrest", with many high-quality updates on epidemiology, public health data, pre-hospital and in-ICU cares. The present review includes short summaries of the major presentations, classified into six main chapters: Epidemiology of CA Pre-hospital management Post-resuscitation management: targeted temperature management Post-resuscitation management: optimizing organ perfusion and metabolic parameters Neurological assessment of brain damages Public healthcare.

Successful treatment of non-convulsive status epilepticus diagnosed using bedside monitoring by a combination of amplitude-integrated and two-channel simplified electroencephalography

Case

A 66-year-old man developed disturbed consciousness and right hemiparesis with transient convulsions in the right arm. Bedside monitoring using a combination of amplitude-integrated electroencephalography and two-channel simplified electroencephalography revealed intermittent episodes of 1-3 Hz δ waves lasting for approximately 5 min, consistent with non-convulsive status epilepticus. Fosphenytoin (22.5 mg/kg/day) and levetiracetam (1,000 mg) prevented right arm convulsions but did not restore consciousness. The two-channel simplified electroencephalography also showed an intermittent periodic δ wave pattern in the Fp1-C3 channel. Conventional electroencephalography revealed a polymorphic δ activity that was abolished by 2.5 mg diazepam, thus confirming the diagnosis of non-convulsive status epilepticus.

Outcome

The patient recovered completely with the antiepileptic drug combination.

Conclusion

Immediate initiation of bedside monitoring using amplitude-integrated electroencephalography and two-channel simplified electroencephalography allows early detection of non-convulsive status epilepticus in patients with disturbed consciousness, which considerably improves the prognosis.

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

Supplemental Digital Content is available in the text.

Modern treatments have improved the survival rate following cardiac arrest, but prognostication remains a challenge. We examined the prognostic value of continuous electroencephalography according to time by performing amplitude-integrated electroencephalography on patients with cardiac arrest receiving therapeutic hypothermia.

A random forest model based classification scheme for neonatal amplitude-integrated EEG

BACKGROUND

Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs).

METHODS

This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA.

RESULTS

The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

How to assess prognosis after cardiac arrest and therapeutic hypothermia

The prognosis of patients who are admitted in a comatose state following successful resuscitation after cardiac arrest remains uncertain. Although the introduction of therapeutic hypothermia (TH) and improvements in post-resuscitation care have significantly increased the number of patients who are discharged home with minimal brain damage, short-term assessment of neurological outcome remains a challenge. The need for early and accurate prognostic predictors is crucial, especially since sedation and TH may alter the neurological examination and delay the recovery of motor response for several days. The development of additional tools, including electrophysiological examinations (electroencephalography and somatosensory evoked potentials), neuroimaging and chemical biomarkers, may help to evaluate the extent of brain injury in these patients. Given the extensive literature existing on this topic and the confounding effects of TH on the strength of these tools in outcome prognostication after cardiac arrest, the aim of this narrative review is to provide a practical approach to post-anoxic brain injury when TH is used. We also discuss when and how these tools could be combined with the neurological examination in a multimodal approach to improve outcome prediction in this population.

The obesity paradox and acute kidney injury: beneficial effects of hyper-inflammation?

In the general population, obesity is associated with an increased mortality risk, whereas several epidemiological studies demonstrated a protective effect of obesity in critically ill patients. In this context, Sleeman and colleagues investigated the effects of obesity on kidney function in a well-established porcine model of cardiopulmonary bypass. The authors confirm literature data that obesity per se is associated with a chronic hyper-inflammatory status. Nevertheless, obese swine undergoing the surgical procedure presented with attenuated kidney dysfunction and tissue apoptosis. The authors suggest that the chronic inflammation causes pre-conditioning against excessive acute hyper-inflammation. The authors have to be commended for using a long-term, clinically relevant model that, moreover, addresses a variety of putative mechanisms. The study is discussed in the context of the controversial findings that, in contrast to the existing literature on improved survival, most studies available suggest a higher incidence and severity of acute kidney injury in obese patients when compared with lean controls.