Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS: Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS: One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION: Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Cognition, emotional state, and quality of life of survivors after cardiac arrest with rhythmic and periodic EEG patterns.

AIM: Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outcomes are lacking. We aimed to provide insight into these outcomes at one-year follow-up. METHODS: We assessed outcome of surviving comatose patients after cardiac arrest with RPPs included in the 'treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation' (TELSTAR) trial at one-year follow-up, including the CPC for functional neurological outcome, a cognitive assessment, the hospital anxiety and depression scale (HADS) for emotional outcomes, and the 36-item short-form health survey (SF-36) for quality of life. Cognitive impairment was defined as a score of more than 1.5 SD below the mean on ≥ 2 (sub)tests within a cognitive domain. RESULTS: Fourteen patients were included (median age 58 years, 21% female), of whom 13 had a cognitive impairment. Eleven of 14 were impaired in memory, 9/14 in executive functioning, and 7/14 in attention. The median scores on the HADS and SF-36 were all worse than expected. Based on the CPC alone, 8/14 had a good outcome (CPC 1-2). CONCLUSION: Nearly all cardiac arrest survivors with RPPs during the comatose state have cognitive impairments at one-year follow-up. The incidence of anxiety and depression symptoms seem relatively high and quality of life relatively poor, despite 'good' outcomes according to the CPC.

Myoclonus in comatose patients with electrographic status epilepticus after cardiac arrest: Corresponding EEG patterns, effects of treatment and outcomes.

OBJECTIVE: To clarify the significance of any form of myoclonus in comatose patients after cardiac arrest with rhythmic and periodic EEG patterns (RPPs) by analyzing associations between myoclonus and EEG pattern, response to anti-seizure medication and neurological outcome. DESIGN: Post hoc analysis of the prospective randomized Treatment of ELectroencephalographic STatus Epilepticus After Cardiopulmonary Resuscitation (TELSTAR) trial. SETTING: Eleven ICUs in the Netherlands and Belgium. PATIENTS: One hundred and fifty-seven adult comatose post-cardiac arrest patients with RPPs on continuous EEG monitoring. INTERVENTIONS: Anti-seizure medication vs no anti-seizure medication in addition to standard care. MEASUREMENTS AND MAIN RESULTS: Of 157 patients, 98 (63%) had myoclonus at inclusion. Myoclonus was not associated with one specific RPP type. However, myoclonus was associated with a smaller probability of a continuous EEG background pattern (48% in patients with vs 75% without myoclonus, odds ratio (OR) 0.31; 95% confidence interval (CI) 0.16-0.64) and earlier onset of RPPs (24% vs 9% within 24 hours after cardiac arrest, OR 3.86;95% CI 1.64-9.11). Myoclonus was associated with poor outcome at three months, but not invariably so (poor neurological outcome in 96% vs 82%, p = 0.004). Anti-seizure medication did not improve outcome, regardless of myoclonus presence (6% good outcome in the intervention group vs 2% in the control group, OR 0.33; 95% CI 0.03-3.32). CONCLUSIONS: Myoclonus in comatose patients after cardiac arrest with RPPs is associated with poor outcome and discontinuous or suppressed EEG. However, presence of myoclonus does not interact with the effects of anti-seizure medication and cannot predict a poor outcome without false positives.

Nitrous oxide abuse leading to extreme homocysteine levels and thrombosis in young adults: a case series.

BACKGROUND: Neurologic complications from recreational use of nitrous oxide (N2O), which are attributed to vitamin B12 deficiency, have been well documented. With increasing dosages and frequency of N2O use, an additional association with thromboembolisms is becoming apparent. OBJECTIVES: To assess thrombotic complications of recreational N2O use. METHODS: All medical charts at the largest hospital in Amsterdam were searched for N2O use and subsequent neurologic and/or thrombotic events. For patients with thrombotic events, we extracted data on the risk factors for arterial and venous thrombosis as well as serum vitamin B12 and homocysteine concentrations. RESULTS: Between January 2015 and May 2021, 326 patients who reported recreational use of N2O were identified; of these, 17 (5%) patients presented with severe thrombotic events associated with N2O (71% men; median age, 26 years [range, 18-53 years]), 5 patients presented with arterial thrombosis (3 with acute coronary syndrome, 1 with femoral artery thrombosis, and 1 with middle cerebral artery thrombus), and 12 patients presented with venous thromboembolisms (10 with pulmonary embolisms, 1 with portal vein thrombosis and 1 with cerebral vein thrombosis). Additionally, homocysteine were concentrations severely increased (median, 125 µmol/L [range, 22-253 µmol/L]; reference, <15 µmol/L). Patients reported use of 400 to 6000 g (ie, 50-750 balloons) of N2O in 1 day. Fifty percent of these patients had experienced neurologic symptoms before the thrombotic event. CONCLUSION: We describe an alarming incidence of serious thrombotic events among young adults after excessive recreational use of N2O, accompanied by extremely high homocysteine concentrations. The upward trend in the recreational use of N2O warrants more awareness of its dangers among both users and medical professionals. Furthermore, these findings could reopen the discussion on possible associations between hyperhomocysteinemia and thrombosis mediated through N2O.

Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods.

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.

Effects of targeted temperature management at 33 °C vs. 36 °C on comatose patients after cardiac arrest stratified by the severity of encephalopathy.

OBJECTIVES: To assess neurological outcome after targeted temperature management (TTM) at 33 °C vs. 36 °C, stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24 h. DESIGN: Post hoc analysis of prospective cohort study. SETTING: Five Dutch Intensive Care units. PATIENTS: 479 adult comatose post-cardiac arrest patients. INTERVENTIONS: TTM at 33 °C (n = 270) or 36 °C (n = 209) and continuous EEG monitoring. MEASUREMENTS AND MAIN RESULTS: Outcome according to the cerebral performance category (CPC) score at 6 months post-cardiac arrest was similar after 33 °C and 36 °C. However, when stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24 h after cardiac arrest, the proportion of good outcome (CPC 1-2) in patients with moderate encephalopathy was significantly larger after TTM at 33 °C (66% vs. 45%; Odds Ratios 2.38, 95% CI = 1.32-4.30; p = 0.004). In contrast, with mild encephalopathy, there was no statistically significant difference in the proportion of patients with good outcome between 33 °C and 36 °C (88% vs. 81%; OR 1.68, 95% CI = 0.65-4.38; p = 0.282). Ordinal regression analysis showed a shift towards higher CPC scores when treated with TTM 33 °C as compared with 36 °C in moderate encephalopathy (cOR 2.39; 95% CI = 1.40-4.08; p = 0.001), but not in mild encephalopathy (cOR 0.81 95% CI = 0.41-1.59; p = 0.537). Adjustment for initial cardiac rhythm and cause of arrest did not change this relationship. CONCLUSIONS: Effects of TTM probably depend on the severity of encephalopathy in comatose patients after cardiac arrest. These results support inclusion of predefined subgroup analyses based on EEG measures of the severity of encephalopathy in future clinical trials.

Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest.

BACKGROUND: Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain. METHODS: We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patients were randomly assigned in a 1:1 ratio to a stepwise strategy of antiseizure medications to suppress this activity for at least 48 consecutive hours plus standard care (antiseizure-treatment group) or to standard care alone (control group); standard care included targeted temperature management in both groups. The primary outcome was neurologic outcome according to the score on the Cerebral Performance Category (CPC) scale at 3 months, dichotomized as a good outcome (CPC score indicating no, mild, or moderate disability) or a poor outcome (CPC score indicating severe disability, coma, or death). Secondary outcomes were mortality, length of stay in the intensive care unit (ICU), and duration of mechanical ventilation. RESULTS: We enrolled 172 patients, with 88 assigned to the antiseizure-treatment group and 84 to the control group. Rhythmic or periodic EEG activity was detected a median of 35 hours after cardiac arrest; 98 of 157 patients (62%) with available data had myoclonus. Complete suppression of rhythmic and periodic EEG activity for 48 consecutive hours occurred in 49 of 88 patients (56%) in the antiseizure-treatment group and in 2 of 83 patients (2%) in the control group. At 3 months, 79 of 88 patients (90%) in the antiseizure-treatment group and 77 of 84 patients (92%) in the control group had a poor outcome (difference, 2 percentage points; 95% confidence interval, -7 to 11; P = 0.68). Mortality at 3 months was 80% in the antiseizure-treatment group and 82% in the control group. The mean length of stay in the ICU and mean duration of mechanical ventilation were slightly longer in the antiseizure-treatment group than in the control group. CONCLUSIONS: In comatose survivors of cardiac arrest, the incidence of a poor neurologic outcome at 3 months did not differ significantly between a strategy of suppressing rhythmic and periodic EEG activity with the use of antiseizure medication for at least 48 hours plus standard care and standard care alone. (Funded by the Dutch Epilepsy Foundation; TELSTAR ClinicalTrials.gov number, NCT02056236.).

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.

Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.

OBJECTIVE: Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. METHODS: We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. RESULTS: Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. CONCLUSIONS: These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

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.

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.

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.

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 (<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 <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;86:203-214.

Propofol does not affect the reliability of early EEG for outcome prediction of comatose patients after cardiac arrest.

OBJECTIVE: To quantify the effects of propofol on the EEG after cardiac arrest and to assess their influence on predictions of outcome. METHODS: In a prospective multicenter cohort study, we analyzed EEG recordings within the first 72 h after cardiac arrest. At six time points, EEGs were classified as favorable (continuous background), unfavorable (generalized suppression or synchronous patterns with ≥50% suppression), or intermediate. Quantitative EEG included measures for amplitude, background continuity, dominant frequency, and burst-suppression amplitude ratio (BSAR). The effect of propofol on each measure was estimated using mixed effects regression. RESULTS: We included 496 patients. The EEG after propofol cessation had no additional value over EEG-based outcome predictions during propofol administration at 12 h after cardiac arrest. Propofol was associated with decreased EEG amplitude, background continuity and dominant frequency, and increased BSAR. However, propofol did neither increase the chance of unfavorable EEG patterns (adjusted odds ratio (aOR) 0.95 per increase of 2 mg/kg/h, 95%-CI: 0.81-1.11) nor decrease the chance of favorable EEG patterns (aOR 0.98, 95%-CI: 0.89-1.09). CONCLUSIONS: Propofol induces changes of the postanoxic EEG, but does not affect its value for the prediction of outcome. SIGNIFICANCE: We confirm the reliability of EEG-based outcome predictions in propofol-sedated patients after cardiac arrest.

Outcome Prediction in Postanoxic Coma With Deep Learning.

OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent. DESIGN: Prospective cohort study. SETTING: Medical ICU of five teaching hospitals in the Netherlands. PATIENTS: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Continuous electroencephalogram was recorded during the first 3 days after cardiac arrest. Functional outcome at 6 months was classified as good (Cerebral Performance Category 1-2) or poor (Cerebral Performance Category 3-5). We trained a convolutional neural network, with a VGG architecture (introduced by the Oxford Visual Geometry Group), to predict neurologic outcome at 12 and 24 hours after cardiac arrest using electroencephalogram epochs and outcome labels as inputs. Output of the network was the probability of good outcome. Data from two hospitals were used for training and internal validation (n = 661). Eighty percent of these data was used for training and cross-validation, the remaining 20% for independent internal validation. Data from the other three hospitals were used for external validation (n = 234). Prediction of poor outcome was most accurate at 12 hours, with a sensitivity in the external validation set of 58% (95% CI, 51-65%) at false positive rate of 0% (CI, 0-7%). Good outcome could be predicted at 12 hours with a sensitivity of 48% (CI, 45-51%) at a false positive rate of 5% (CI, 0-15%) in the external validation set. CONCLUSIONS: Deep learning of electroencephalogram signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual electroencephalogram assessment by trained electroencephalogram experts. Our approach offers the potential for objective and real time, bedside insight in the neurologic prognosis of comatose patients after cardiac arrest.

The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.

OBJECTIVE: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive values of the revised Cerebral Recovery Index (rCRI), based on continuous extraction and combination of a large set of evolving quantitative EEG (qEEG) features and machine learning techniques. METHODS: We included 551 subsequent patients from a prospective cohort study on continuous EEG after CA in two hospitals. Outcome at six months was classified as good (Cerebral Performance Category (CPC) 1-2) or poor (CPC 3-5). Forty-four qEEG features (from time, frequency and entropy domain) were selected by the least absolute shrinkage and selection operator (LASSO) method and used in a Random Forests classification system. We trained and evaluated the system with 10-fold cross validation. For poor outcome prediction, the sensitivity at 100% specificity (Se100) and the area under the receiver operator curve (AUC) were used as performance of the prediction model. For good outcome, we used the sensitivity at 95% specificity (Se95). RESULTS: Two hundred fifty-six (47%) patients had a good outcome. The rCRI predicted poor outcome with AUC = 0.94 (95% CI: 0.83-0.91), Se100 = 0.66 (0.65-0.78), and AUC = 0.88 (0.78-0.93), Se100 = 0.60 (0.51-0.75) at 12 and 24 h after CA, respectively. The rCRI predicted good outcome with Se95 = 0.72 (0.61-0.85) and 0.40 (0.30-0.51) at 12 and 24 h after CA, respectively. CONCLUSIONS: Results obtained in this study suggest that with machine learning algorithms and large set of qEEG features, it is possible to efficiently monitor patient outcome after CA. We also demonstrate the importance of selection of optimal performance metric to train a classifier model for outcome prediction. SIGNIFICANCE: The rCRI is a sensitive, reliable predictor of neurological outcome of comatose patients after CA.

The prognostic value of discontinuous EEG patterns in postanoxic coma.

OBJECTIVE: To assess the value of background continuity and amplitude fluctuations of the EEG for the prediction of outcome of comatose patients after cardiac arrest. METHODS: In a prospective cohort study, we analyzed EEGs recorded in the first 72 h after cardiac arrest. We defined the background continuity index (BCI) as the fraction of EEG not spent in suppressions (amplitudes < 10 µV for ≥ 0.5 s), and the burst-suppression amplitude ratio (BSAR) as the mean amplitude ratio between non-suppressed and suppressed segments. Outcome was assessed at 6 months and categorized as "good" (Cerebral Performance Category 1-2) or "poor" (CPC 3-5). RESULTS: Of the 559 patients included, 46% had a good outcome. Combinations of BCI and BSAR resulted in the highest prognostic accuracies. Good outcome could be predicted at 24 h with 57% sensitivity (95% confidence interval (CI): 48-67) at 90% specificity (95%-CI: 86-95). Poor outcome could be predicted at 12 h with 50% sensitivity (95%-CI: 42-56) at 100% specificity (95%-CI: 99-100). CONCLUSIONS: EEG background continuity and the amplitude ratio between bursts and suppressions reliably predict the outcome of postanoxic coma. SIGNIFICANCE: The presented features provide an objective, rapid, and reliable tool to assist in EEG interpretation in the Intensive Care Unit.

Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

BACKGROUND: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. METHODS: A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. RESULTS: Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. CONCLUSIONS: Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.