Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG.

BACKGROUND: Despite application of the multimodal European Resuscitation Council and European Society of Intensive Care Medicine algorithm, neurological prognosis of patients who remain comatose after cardiac arrest remains uncertain in a large group of patients. In this study, we investigate the additional predictive value of visual and quantitative brain magnetic resonance imaging (MRI) to electroencephalography (EEG) for outcome estimation of comatose patients after cardiac arrest. METHODS: We performed a prospective multicenter cohort study in patients after cardiac arrest submitted in a comatose state to the intensive care unit of two Dutch hospitals. Continuous EEG was recorded during the first 3 days and MRI was performed at 3 ± 1 days after cardiac arrest. EEG at 24 h and ischemic damage in 21 predefined brain regions on diffusion weighted imaging and fluid-attenuated inversion recovery on a scale from 0 to 4 were related to outcome. Quantitative MRI analyses included mean apparent diffusion coefficient (ADC) and percentage of brain volume with ADC < 450 × 10-6 mm2/s, < 550 × 10-6 mm2/s, and < 650 × 10-6 mm2/s. Poor outcome was defined as a Cerebral Performance Category score of 3-5 at 6 months. RESULTS: We included 50 patients, of whom 20 (40%) demonstrated poor outcome. Visual EEG assessment correctly identified 3 (15%) with poor outcome and 15 (50%) with good outcome. Visual grading of MRI identified 13 (65%) with poor outcome and 25 (89%) with good outcome. ADC analysis identified 11 (55%) with poor outcome and 3 (11%) with good outcome. EEG and MRI combined could predict poor outcome in 16 (80%) patients at 100% specificity, and good outcome in 24 (80%) at 63% specificity. Ischemic damage was most prominent in the cortical gray matter (75% vs. 7%) and deep gray nuclei (45% vs. 3%) in patients with poor versus good outcome. CONCLUSIONS: Magnetic resonance imaging is complementary with EEG for the prediction of poor and good outcome of patients after cardiac arrest who are comatose at admission.

Infraslow EEG activity modulates cortical excitability in postanoxic encephalopathy.

Infraslow activity represents an important component of physiological and pathological brain function. We study infraslow activity (<0.1 Hz) in 41 patients with postanoxic coma after cardiac arrest, including the relationship between infraslow activity and EEG power in the 3-30 Hz range, using continuous full-band scalp EEG. In all patients, infraslow activity (0.015-0.06 Hz) was present, irrespective of neurological outcome or EEG activity in the conventional frequency bands. In two patients, low-amplitude (10-30 µV) infraslow activity was present while the EEG showed no rhythmic activity above 0.5 Hz. In 13/15 patients with a good outcome and 20/26 patients with a poor one, EEG power in the 3-30 Hz frequency range was correlated with the phase of infraslow activity, quantified by the modulation index. In 9/14 patients with burst-suppression with identical bursts, bursts appeared in clusters, phase-locked to the infraslow oscillations. This is substantiated by a simulation of burst-suppression in a minimal computational model. Infraslow activity is preserved in postanoxic encephalopathy and modulates cortical excitability. The strongest modulation is observed in patients with severe postanoxic encephalopathy and burst-suppression with identical bursts.

Clinical observation: Rhythmic and periodic EEG patterns in postanoxic coma can possibly be related to propofol discontinuation.

OBJECTIVES: To investigate whether rhythmic/periodic EEG patterns (RPP) appearing after propofol discontinuation are more likely to be related to the elimination phase of propofol, or are an expression of severe brain damage. METHODS: In a retrospective cohort of comatose postanoxic patients, EEG was assessed one hour before (baseline) and on hour after discontinuation of propofol. Presence and duration of RPP were related to (changes in) EEG background pattern and duration of sedation. RESULTS: In eleven (of 36 eligible) patients RPP appeared after propofol discontinuation and disappeared in seven of these patients within one hour. A continuous background pattern at baseline and shorter duration of propofol infusion seemed associated with (earlier) spontaneous disappearance of RPP. In ten patients with RPP at baseline, the EEG did not change, and in one patient it changed into burst-suppression. CONCLUSION: Our findings suggest that RPP after propofol discontinuation could be propofol-related. DISCUSSION: RPP might be related to propofol discontinuation rather than an expression of severe brain damage, especially in case of, and congruent with, a continuous pattern at the time of propofol discontinuation. This opens a new insight in this phenomenon and its transient nature. In clinical practice, we suggest to consider the timing of propofol discontinuation when assessing the EEG signal in postanoxic patients.

MRI markers of brain network integrity relate to neurological outcome in postanoxic coma.

AIM: Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later. METHODS: We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied. RESULTS: Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome. DISCUSSION: Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.

Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma.

OBJECTIVE: Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values. METHODS: We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3 days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1-2) or poor outcome (CPC 3-5) with univariate analyses. With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction. RESULTS: Of 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12 h after cardiac arrest (LRθ 1.87 vs. 1.95 Hz and LDα 91 vs. 82 ms). Adding these measures to a model with classical EEG features increased sensitivity for reliable prediction of poor outcome from 34% to 38% at 12 h after cardiac arrest. CONCLUSION: Poor outcome is associated with lower dynamics of connectivity after cardiac arrest. SIGNIFICANCE: Dynamic functional connectivity analysis may improve EEG based outcome prediction.

EEG functional connectivity contributes to outcome prediction of postanoxic coma.

OBJECTIVE: To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. METHODS: Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as "good" (Cerebral Performance Category [CPC] 1-2) or "poor" (CPC 3-5). RESULTS: We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34-56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0-54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50-77%) at 100% specificity. CONCLUSION: Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. SIGNIFICANCE: Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.

Neuroprotective Treatment of Postanoxic Encephalopathy: A Review of Clinical Evidence.

Postanoxic encephalopathy is the key determinant of death or disability after successful cardiopulmonary resuscitation. Animal studies have provided proof-of-principle evidence of efficacy of divergent classes of neuroprotective treatments to promote brain recovery. However, apart from targeted temperature management (TTM), neuroprotective treatments are not included in current care of patients with postanoxic encephalopathy after cardiac arrest. We aimed to review the clinical evidence of efficacy of neuroprotective strategies to improve recovery of comatose patients after cardiac arrest and to propose future directions. We performed a systematic search of the literature to identify prospective, comparative clinical trials on interventions to improve neurological outcome of comatose patients after cardiac arrest. We included 53 studies on 21 interventions. None showed unequivocal benefit. TTM at 33 or 36°C and adrenaline (epinephrine) are studied most, followed by xenon, erythropoietin, and calcium antagonists. Lack of efficacy is associated with heterogeneity of patient groups and limited specificity of outcome measures. Ongoing and future trials will benefit from systematic collection of measures of baseline encephalopathy and sufficiently powered predefined subgroup analyses. Outcome measurement should include comprehensive neuropsychological follow-up, to show treatment effects that are not detectable by gross measures of functional recovery. To enhance translation from animal models to patients, studies under experimental conditions should adhere to strict methodological and publication guidelines.

Added value of somato-sensory evoked potentials amplitude for prognostication after cardiac arrest.

AIMS: Bilateral absence of cortical somato-sensory evoked potentials (SSEPs) robustly predicts poor outcome after cardiac arrest (CA), but it is uncertain if SSEP amplitudes provide additional information. Here, we examined the prognostic value of cortical SSEP amplitude in comparison with other known outcome predictors. METHODS: We retrospectively determined SSEP amplitudes in a prospective CA registry, identified an amplitude cut-off for worst Cerebral Performance Category (CPC) within three months, and examined correlations of SSEP amplitude with pupillary light reflex (PLR), myoclonus, peak serum neuron specific enolase (NSE), and 24-36 h and 36-72 h EEG (reactivity, epileptiform features). RESULTS: Among 158 patients, 54% awoke. Amplitudes correlated with EEG findings, present PLR, myoclonus, NSE. A cut-off for cortical SSEP ≤ 0.41 µV was 100% specific for poor outcome (95% CI: 96-100%); sensitivity increased marginally vs. SSEPs absence [47% (35-59%) vs 46% (34-58%)] for CPC 4-5. Adding SSEPs ≤0.41 µV to a multimodal prognostic model including EEG, clinical features, and NSE improved prediction for mortality, but not for CPC 3-5 at three months. No statistical correlation between amplitudes and good outcome was observed. SSEP amplitudes correlated inversely with CPC at three months in the overall cohort (r = -0.332; p < 0.0001) but not in the subgroup with present SSEPs (r = -0.102; p = 0.256). CONCLUSION: Decreased SSEPs amplitudes are associated with poor outcome after cardiac arrest; however, adding this to a multimodal prognostic approach including EEG, clinical and blood biomarkers, improves slightly prediction of mortality, but not of poor or good outcome.

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.

Delirium after cardiac arrest: Phenotype, prediction, and outcome.

AIM: To establish incidence, phenotype, long-term functional outcome, and early EEG predictors of delirium after cardiac arrest. METHODS: This is an ad hoc analysis of a prospective cohort study on outcome prediction of comatose patients after cardiac arrest. Patients with recovery of consciousness, who survived until hospital discharge, were subdivided in groups with and without delirium based on psychiatric consultation. Delirium phenotype and medical treatment were retrieved from patient files. All other data were prospectively collected. We used univariate analyses of baseline and early EEG characteristics for identification of possible delirium predictors. Association of delirium with neurological recovery at six months was analyzed with multinomial logistic regression analysis. RESULTS: Of 233 patients, 141 survived until hospital discharge, of whom 47 (33%) were diagnosed with delirium. There were no differences in baseline characteristics between patients with and without delirium. All delirious patients were treated with relatively high dosages of psychopharmaceuticals, mostly haloperidol and benzodiazepine agonists. Prevalent characteristics were disturbed cognition, perception and psychomotor functioning (98%). Half of the patients had language disorders or shouting. Delirium was associated with longer ICU and hospital admission, and more frequent discharge to rehabilitation centre or nursing home. There was a trend towards poorer neurological recovery. EEG measurements within 12 h after cardiac arrest could predict delirium with 91% specificity and 40% sensitivity. DISCUSSION: Delirium is common after cardiac arrest, and probably leads to longer hospitalization and poorer outcome. Optimal treatment is unclear. Early EEG holds potential to identify patients at risk.

Association between somatosensory evoked potentials and EEG in comatose patients after cardiac arrest.

OBJECTIVE: To analyze the association between SSEP results and EEG results in comatose patients after cardiac arrest, including the added value of repeated SSEP measurements. METHODS: Continuous EEG was measured in 619 patients during the first 3-5 days after cardiac arrest. SSEPs were recorded daily in the first 55 patients, and on indication in later patients. EEGs were visually classified at 12, 24, 48, and 72 h after cardiac arrest, and at the time of SSEP. Outcome at 6 m was dichotomized as good (Cerebral Performance Category 1-2) or poor (CPC 3-5). SSEP and EEG results were related to outcome. Additionally, SSEP results were related to the EEG patterns at the time of SSEP. RESULTS: Absent SSEP responses and suppressed or synchronous EEG on suppressed background ≥24 h after cardiac arrest were invariably associated with poor outcome. SSEP and EEG identified different patients with poor outcome (joint sensitivity 39% at specificity 100%). N20 responses were always preserved in continuous traces at >8 Hz. Absent SSEPs did not re-emerge during the first five days. CONCLUSIONS: SSEP and EEG results may diverge after cardiac arrest. SIGNIFICANCE: SSEP and EEG together identify more patients without chance of recovery than one of these alone.

Postmortem histopathology of electroencephalography and evoked potentials in postanoxic coma.

Early EEG patterns and SSEP responses are associated with neurological recovery of comatose patients with postanoxic encephalopathy after cardiac arrest. However, the nature and distribution of brain damage underlying the characteristic EEG and SSEP patterns are unknown. We relate EEG and SSEP findings with results from histological analyses of the brains of eleven non-survivors. With restoration towards continuous rhythms within 24 h after cardiac arrest, no signs of structural neuronal damage were observed. Absent SSEP responses were always accompanied by thalamic damage. Pathological burst suppression patterns were associated with a variable degree of neuronal damage to cortex, cerebellum and hippocampus. In patients with additional thalamic involvement, burst-suppression with identical bursts was observed, a characteristic EEG pattern presumably reflecting residual activity from a relatively isolated and severely compromised cortex.

Prospective Cohort Study Evaluating the Prognostic Value of Simple EEG Parameters in Postanoxic Coma.

We prospectively studied early bedside standard EEG characteristics in 61 acute postanoxic coma patients. Five simple EEG features, namely, isoelectric, discontinuous, nonreactive to intense auditory and nociceptive stimuli, dominant delta frequency, and occurrence of paroxysms were classified yes or no. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) of each of these variables for predicting an unfavorable outcome, defined as death, persistent vegetative state, minimally conscious state, or severe neurological disability, as assessed 1 year after coma onset were computed as well as Synek's score. The outcome was unfavorable in 56 (91.8%) patients. Sensitivity, specificity, PPV, NPV, and AUC of nonreactive EEG for predicting an unfavorable outcome were 84%, 80%, 98%, 31%, and 0.82, respectively; and were all very close to the ones of Synek score>3, which were 82%, 80%, 98%, 29%, and 0.81, respectively. Specificities for predicting an unfavorable outcome were 100% for isoelectric, discontinuous, or dominant delta activity EEG. These 3 last features were constantly associated to unfavorable outcome. Absent EEG reactivity strongly predicted an unfavorable outcome in postanoxic coma, and performed as accurate as a Synek score>3. Analyzing characteristics of some simple EEG features may easily help nonneurophysiologist physicians to investigate prognostic issue of postanoxic coma patient. In this study (a) discontinuous, isoelectric, or delta-dominant EEG were constantly associated with unfavorable outcome and (b) nonreactive EEG performed prognostic as accurate as a Synek score>3.

Single electroencephalographic patterns as specific and time-dependent indicators of good and poor outcome after cardiac arrest.

OBJECTIVE: To evaluate the prognostic value of single EEG patterns recorded at various time-frames in postanoxic comatose patients. METHODS: This retrospective study included 30-min EEGs, classified according to the definitions of continuity of background activity given by the American Clinical Neurophysiology Society. Isoelectric pattern was distinguished from other suppressed activities. Epileptiform patterns were considered separately. Outcome was dichotomised based on recovery of consciousness as good (Glasgow Outcome Scale [GOS] 3-5) or poor (GOS 1-2). RESULTS: We analysed 211 EEGs, categorised according to time since cardiac arrest (within 12h and around 24, 48 and 72h). In each time-frame we observed at least one EEG pattern which was 100% specific to poor or good outcome: at 12h continuous and nearly continuous patterns predicted good outcome and isoelectric pattern poor outcome; at 24h isoelectric and burst-suppression predicted poor outcome; at 48 and 72h isoelectric, burst-suppression and suppression (2-10µV) patterns predicted poor outcome. CONCLUSIONS: The prognostic value of single EEG patterns, defined according to continuity and voltage of background activity, changes until 48-72h after cardiac arrest and in each time-frame there is at least one pattern which accurately predicts good or poor outcome. SIGNIFICANCE: Standard EEG can provide time-dependent reliable indicators of good and poor outcome throughout the first 48-72h after cardiac arrest.

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).