Pain-related Somato Sensory Evoked Potentials: A potential new tool to improve the prognostic prediction of coma after cardiac arrest

INTRODUCTION: Early prediction of a good outcome in comatose patients after cardiac arrest still remains an unsolved problem. The main aim of the present study was to examine the accuracy of middle-latency SSEP triggered by a painful electrical stimulation on median nerves to predict a favorable outcome. METHODS: No- and low-flow times, pupillary reflex, Glasgow motor score and biochemical data were evaluated at ICU admission. The following were considered within 72 h of cardiac arrest: highest creatinine value, hyperthermia occurrence, EEG, SSEP at low- (10 mA) and high-intensity (50 mA) stimulation, and blood pressure reactivity to 50 mA. Intensive care treatments were also considered. Data were compared to survival, consciousness recovery and 6-month CPC (Cerebral Performance Category). RESULTS: Pupillary reflex and EEG were statistically significant in predicting survival; the absence of blood pressure reactivity seems to predict brain death within 7 days of cardiac arrest. Middle- and short-latency SSEP were statistically significant in predicting consciousness recovery, and middle-latency SSEP was statistically significant in predicting 6-month CPC outcome. The prognostic capability of 50 mA middle-latency-SSEP was demonstrated to occur earlier than that of EEG reactivity. CONCLUSIONS: Neurophysiological evaluation constitutes the key to early information about the neurological prognostication of postanoxic coma. In particular, the presence of 50 mA middle-latency SSEP seems to be an early and reliable predictor of good neurological outcome, and its absence constitutes a marker of poor prognosis. Moreover, the absence 50 mA blood pressure reactivity seems to identify patients evolving towards the brain death

Dissociation of Cerebral Blood Flow and Femoral Artery Blood Pressure Pulsatility After Cardiac Arrest and Resuscitation in a Rodent Model: Implications for Neurological Recovery.

Background Impaired neurological function affects 85% to 90% of cardiac arrest (CA) survivors. Pulsatile blood flow may play an important role in neurological recovery after CA. Cerebral blood flow (CBF) pulsatility immediately, during, and after CA and resuscitation has not been investigated. We characterized the effects of asphyxial CA on short-term (<2 hours after CA) CBF and femoral arterial blood pressure (ABP) pulsatility and studied their relationship to cerebrovascular resistance (CVR) and short-term neuroelectrical recovery. Methods and Results Male rats underwent asphyxial CA followed by cardiopulmonary resuscitation. A multimodal platform combining laser speckle imaging, ABP, and electroencephalography to monitor CBF, peripheral blood pressure, and brain electrophysiology, respectively, was used. CBF and ABP pulsatility and CVR were assessed during baseline, CA, and multiple time points after resuscitation. Neuroelectrical recovery, a surrogate for neurological outcome, was assessed using quantitative electroencephalography 90 minutes after resuscitation. We found that CBF pulsatility differs significantly from baseline at all experimental time points with sustained deficits during the 2 hours of postresuscitation monitoring, whereas ABP pulsatility was relatively unaffected. Alterations in CBF pulsatility were inversely correlated with changes in CVR, but ABP pulsatility had no association to CVR. Interestingly, despite small changes in ABP pulsatility, higher ABP pulsatility was associated with worse neuroelectrical recovery, whereas CBF pulsatility had no association. Conclusions Our results reveal, for the first time, that CBF pulsatility and CVR are significantly altered in the short-term postresuscitation period after CA. Nevertheless, higher ABP pulsatility appears to be inversely associated with neuroelectrical recovery, possibly caused by impaired cerebral autoregulation and/or more severe global cerebral ischemia

Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64-100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (pvalue ≤ 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia

Neurological prognostication of outcome in patients in coma after cardiac arrest.

Management of coma after cardiac arrest has improved during the past decade, allowing an increasing proportion of patients to survive, thus prognostication has become an integral part of post-resuscitation care. Neurologists are increasingly confronted with raised expectations of next of kin and the necessity to provide early predictions of long-term prognosis. During the past decade, as technology and clinical evidence have evolved, post-cardiac arrest prognostication has moved towards a multimodal paradigm combining clinical examination with additional methods, consisting of electrophysiology, blood biomarkers, and brain imaging, to optimise prognostic accuracy. Prognostication should never be based on a single indicator; although some variables have very low false positive rates for poor outcome, multimodal assessment provides resassurance about the reliability of a prognostic estimate by offering concordant evidence

Electrographic signatures of postanoxic brain injury

After a successful resuscitation from cardiac arrest, most patients remain comatose as a result of postanoxic encephalopathy. More than half of them never regain consciousness, and treatment options to improve their outcome are limited. The aim of the research described in this dissertation was to validate and improve the value of continuous electroencephalography (EEG) for outcome prediction and treatment of postanoxic brain injury. In a prospective cohort study of 850 patients, we confirmed that EEG reaches its maximum value for the prediction of outcome within the first 24 hours after cardiac arrest. The added value of continuous EEG monitoring beyond this period was limited. Generalized suppression (all EEG activity <10 µV) or synchronous patterns with more than 50% suppression reliably predicted a poor outcome at 12h after cardiac arrest or later. Continuous background activity within 12h from cardiac arrest was a strong predictor of good outcome. To make the assessment of postanoxic EEG less time-consuming and more objective, we introduced straightforward quantitative EEG features, based on key aspects of visual assessment for the prediction of outcome. Our measures for background continuity and amplitude fluctuation were at least as sensitive for the prediction of good outcome as visual assessment, at equal reliability. In the subgroup of patients with electrographic seizure activity, we showed that a lack of background continuity of the EEG precludes recovery. During the first 24 hours after cardiac arrest, the most valuable period for the prediction of outcome, patients are usually treated with sedative medication. We showed quantitatively that propofol, a commonly applied sedative drug, changes the postanoxic EEG, but does not affect its reliability for the prediction of outcome. A better understanding of mechanisms that underlie postanoxic EEG patterns could validate associations between EEG and outcome, and offer opportunities for new treatment strategies. By using a computational model, we showed that pathophysiological changes at the synaptic level explain the most commonly observed EEG patterns after cardiac arrest and their evolution. Finally, we present the study protocol of the ongoing, randomized TELSTAR trial on the treatment of electrographic status epilepticus after cardiac arrest

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

Electroencephalography for neurological prognostication after cardiac arrest

This thesis focuses on the prognostic value of electroencephalography(EEG) in comatose patients resuscitated after cardiac arrest (CA), using both simplified continuous EEG monitoring (cEEG) and routine EEG. Background: Comatose survivors are admitted to an intensive care unit (ICU) to support vital functions. Postresuscitation care includes target temperature management (TTM) for 24 hours. The degree of brain injury after CA varies among patients. Withdrawal of life-sustaining therapy due to presumed extensive brain injury is the most common cause of death during the hospital stay. Multiple prognostic tools are used to identify patients with a potential for recovery. Next to the neurological examination, EEG is the most commonly used tool to assess prognosis. However, the value of EEG has been limited by varying classification systems, interrater variability and influence of sedation. Methods: In the “coma project” (2004-2008) consecutive patients at the general ICU in Lund were monitored with simplified cEEG from arrival until 120 hours after CA. Pre-defined cEEG patterns at different time points were correlated to outcome. In the TTM trial (2010-2013) where patients were randomized to 33ºC versus 36ºC, a routine EEG was performed in patients still comatose after rewarming. The EEGs were classified into highly malignant, malignant and benign patterns by four EEG specialists from different countries according to the standardized EEG terminology proposed by the American Clinical Neurophysiology Society. The rationale and study design for this EEG evaluation was published. Results: 95 patients in the “coma project” were monitored with simplified cEEG. A continuous background at start of registration or at normothermia strongly predicted a good outcome. All patients with electrographic status epilepticus (ESE) evolving from a burst-suppression background died without regaining consciousness whereas ESE evolving late from an established continuous background was compatible with good outcome. At 8 selected TTM trial sites, routine EEGs were recorded after rewarming in 103 comatose patients. A highly malignant EEG was identified with substantial interrater agreement and had a specificity of 100% to predict poor outcome for all four EEG specialists. Any malignant EEG feature was identified with moderate interrater agreement but had a low specificity to predict a poor outcome (48%). A benign EEG was found in 1% of the patients with a poor outcome. Conclusions: Simplified cEEG provides early positive and negative prognostic information in comatose patients after cardiac arrest. A highly malignant routine EEG after rewarming reliably predicted a poor outcome. An isolated malignant routine EEG feature was not a reliable predictor whereas a benign routine EEG was highly predictive of good outcome

Cardiac arrest and therapeutic hypothermia: Prognosis and outcome

Abstract Therapeutic hypothermia (TH) is the only treatment available to reduce neurological sequels for unconscious patients following cardiac arrest (CA). TH requires sedation and muscular relaxation, obscuring the clinical neurological examination for estimation of prognosis, and clinical decision making. Continuous amplitude-integrated EEG (aEEG) has been used to predict outcome in neonates suffering from asphyxia. In adults following CA and TH, the novel observation was made that a continuous aEEG-pattern prior to or at normothermia strongly correlated to return of consciousness, while other patterns strongly correlated to continued coma. A status epilepticus aEEG-pattern carried a poor, but not desolate prognosis. Biochemical neuronal-markers (neuron-specific enolase (NSE) and S-100B) have previously been assessed in non-TH CA patients. In TH, an NSE level of 28 μg/l 48h after CA, or an increase of more than 2 μg/l between 24 and 48h were strongly associated to a poor outcome. Five days after the CA, one third of the patients remained in coma. They either had multimodal signs of extensive brain damage (high NSE levels, ischemic changes on MRI or neurophysiological evidence of advanced brain damage (bilateral lack of SSEP)), or showed sustained unconsciousness and a status epilepticus aEEG-pattern. Unconscious patients without these signs of brain injury eventually regained consciousness. Approximately 50% of hypothermia treated patients regained consciousness. Ninety-eight percent of surviving patients had an independent lifestyle six months after the CA. The dominant cognitive problem was a disturbed memory function. Taken together, aEEG appears superior in early neurological prognostication in these patients

Early electroencephalography for outcome prediction of postanoxic coma:A prospective cohort study

OBJECTIVE: To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.METHODS: In a 5-center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five-minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients' actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (&lt;10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1-2) or poor (CPC = 3-5).RESULTS: We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42-0.51) at 12 hours and 0.30 (95% CI = 0.26-0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99-1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46-0.55) and specificity of 0.91 (95% CI = 0.88-0.93); at 24 hours or later, specificity for the prediction of good outcome was &lt;0.90.INTERPRETATION: EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019.</p