Duration of cardiopulmonary resuscitation and phenotype of post-cardiac arrest brain injury.

BACKGROUND: Patients resuscitated from cardiac arrest have variable severity of primary hypoxic ischemic brain injury (HIBI). Signatures of primary HIBI on brain imaging and electroencephalography (EEG) include diffuse cerebral edema and burst suppression with identical bursts (BSIB). We hypothesize distinct phenotypes of primary HIBI are associated with increasing cardiopulmonary resuscitation (CPR) duration. METHODS: We identified from our prospective registry of both in-and out-of-hospital CA patients treated between January 2010 to January 2020 for this cohort study. We abstracted CPR duration, neurological examination, initial brain computed tomography gray to white ratio (GWR), and initial EEG pattern. We considered four phenotypes on presentation: awake; comatose with neither BSIB nor cerebral edema (non-malignant coma); BSIB; and cerebral edema (GWR ≤ 1.20). BSIB and cerebral edema were considered as non-mutually exclusive outcomes. We generated predicted probabilities of brain injury phenotype using localized regression. RESULTS: We included 2,440 patients, of whom 545 (23%) were awake, 1,065 (44%) had non-malignant coma, 548 (23%) had BSIB and 438 (18%) had cerebral edema. Only 92 (4%) had both BSIB and edema. Median CPR duration was 16 [IQR 8-28] minutes. Median CPR duration increased in a stepwise manner across groups: awake 6 [3-13] minutes; non-malignant coma 15 [8-25] minutes; BSIB 21 [13-31] minutes; cerebral edema 32 [22-46] minutes. Predicted probability of phenotype changes over time. CONCLUSIONS: Brain injury phenotype is related to CPR duration, which is a surrogate for severity of HIBI. The sequence of most likely primary HIBI phenotype with progressively longer CPR duration is awake, coma without BSIB or edema, BSIB, and finally cerebral edema.

Deep learning of early brain imaging to predict post-arrest electroencephalography.

INTRODUCTION: Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these associations via deep learning. METHODS: We performed a single-center, retrospective study including comatose patients hospitalized after cardiac arrest. We extracted brain CT DICOMs, resized and registered each to a standard anatomical atlas, performed skull stripping and windowed images to optimize contrast of the gray-white junction. We classified initial EEG as generalized suppression, other highly pathological findings or benign activity. We extracted clinical information available on presentation from our prospective registry. We trained three machine learning (ML) models to predict EEG from clinical covariates. We used three state-of-the-art approaches to build multi-headed deep learning models using similar model architectures. Finally, we combined the best performing clinical and imaging models. We evaluated discrimination in test sets. RESULTS: We included 500 patients, of whom 218 (44%) had benign EEG findings, 135 (27%) showed generalized suppression and 147 (29%) had other highly pathological findings that were most commonly (93%) burst suppression with identical bursts. Clinical ML models had moderate discrimination (test set AUCs 0.73-0.80). Image-based deep learning performed worse (test set AUCs 0.51-0.69), particularly discriminating benign from highly pathological findings. Adding image-based deep learning to clinical models improved prediction of generalized suppression due to accurate detection of severe cerebral edema. DISCUSSION: CT and EEG provide complementary information about post-arrest brain injury. Our results do not support selective acquisition of only one of these modalities, except in the most severely injured patients.

Awakening from post anoxic coma with burst suppression with identical bursts.

BACKGROUND: Electroencephalography (EEG) is commonly used after cardiac arrest. Burst suppression with identical bursts (BSIB) has been reported as a perfectly specific predictor of poor outcome but published case series are small. We describe two patients with BSIB who awakened from coma after cardiac arrest. METHODS: We identified two out-of-hospital cardiac arrest (OHCA) patients with coma and BSIB. We determined the etiology of arrest, presenting neurological examination, potential confounders to neurological assessment, neurodiagnostics and time to awakening. We reviewed and interpreted EEGs using 2021 American Clinical Neurophysiology Society guidelines. We quantified identicality of bursts by calculating pairwise correlation coefficients between the first 500 ms of each aligned burst. RESULTS: In case one we present a 62-year-old man with OHCA secondary to septic shock. EEG showed burst suppression pattern, with bursts consisted of high amplitude generalized spike waves in lock-step with myoclonus (inter-burst correlation = 0.86). He followed commands 3 days after arrest, when repeat EEG showed a continuous, variable and reactive background without epileptiform activity. Case two was a 49-year-old woman with OHCA secondary to polysubstance overdose. Initial EEG revealed burst suppression with high amplitude generalized polyspike-wave bursts with associated myoclonus. She followed commands on post-arrest day 4, when repeat EEG showed a continuous, variable and reactive background with frequent runs of bifrontal predominant sharply contoured rhythmic delta activity. CONCLUSION: These cases highlight the perils of prognosticating with a single modality in comatose cardiac arrest patients.

Sensitivity of Continuous Electroencephalography to Detect Ictal Activity After Cardiac Arrest.

Importance: Epileptiform electroencephalographic (EEG) patterns are common after resuscitation from cardiac arrest, are associated with patient outcome, and may require treatment. It is unknown whether continuous EEG monitoring is needed to detect these patterns or if brief intermittent monitoring is sufficient. If continuous monitoring is required, the necessary duration of observation is unknown. Objective: To quantify the time-dependent sensitivity of continuous EEG for epileptiform event detection, and to compare continuous EEG to several alternative EEG-monitoring strategies for post-cardiac arrest outcome prediction. Design, Setting, and Participants: This observational cohort study was conducted in 2 academic medical centers between September 2010 and January 2018. Participants included 759 adults who were comatose after being resuscitated from cardiac arrest and who underwent 24 hours or more of EEG monitoring. Main Outcomes and Measures: Epileptiform EEG patterns associated with neurological outcome at hospital discharge, such as seizures likely to cause secondary injury. Results: Overall, 759 patients were included in the analysis; 281 (37.0%) were female, and the mean (SD) age was 58 (17) years. Epileptiform EEG activity was observed in 414 participants (54.5%), of whom only 26 (3.4%) developed potentially treatable seizures. Brief intermittent EEG had an estimated 66% (95% CI, 62%-69%) to 68% (95% CI, 66%-70%) sensitivity for detection of prognostic epileptiform events. Depending on initial continuity of the EEG background, 0 to 51 hours of monitoring were needed to achieve 95% sensitivity for the detection of prognostic epileptiform events. Brief intermittent EEG had a sensitivity of 7% (95% CI, 4%-12%) to 8% (95% CI, 4%-12%) for the detection of potentially treatable seizures, and 0 to 53 hours of continuous monitoring were needed to achieve 95% sensitivity for the detection of potentially treatable seizures. Brief intermittent EEG results yielded similar information compared with continuous EEG results when added to multivariable models predicting neurological outcome. Conclusions and Relevance: Compared with continuous EEG monitoring, brief intermittent monitoring was insensitive for detection of epileptiform events. Monitoring EEG results significantly improved multimodality prediction of neurological outcome, but continuous monitoring appeared to add little additional information compared with brief intermittent monitoring.

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.

Misleading EEG Lateralization Associated With Midline Shift.

Midline discharges, lateralized periodic discharges, and seizures have been described with ipsilateral lesions that result in midline shift (MLS). Periodic discharges and seizures arising contralateral to a known lesion have not previously been described as a sign of MLS. We present four patients with focal brain lesions, resulting in MLS and epileptiform discharges arising from the contralateral hemisphere. Patient 1 underwent a right anterior temporal lobectomy. On postoperative day 2, computed tomography demonstrated a right to left MLS of 12 mm, and EEG was remarkable for left temporal nonconvulsive status epilepticus. Patient 2 experienced a subarachnoid hemorrhage, which was more prominent on the left. Computed tomography after craniotomy demonstrated left to right MLS of 6 mm, and EEG was remarkable for right lateralized periodic discharges. Patient 3 had a right subdural hematoma and underwent craniotomy for evacuation. On postoperative day 3, computed tomography demonstrated a right MLS of 7 mm, and EEG was remarkable for left temporal nonconvulsive status epilepticus. Patient 4 had traumatic brain hemorrhages with maximal left frontotemporal involvement. Six days after the trauma, computed tomography was significant for left to right MLS of 9 mm, and EEG showed right lateralized periodic discharges. Epileptiform discharges and seizures occurring contralateral to a known lesion may be an indicator of MLS.

Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury.

OBJECTIVE: Hypoxic brain injury is the largest contributor to disability and mortality after cardiac arrest. We aim to identify electroencephalogram (EEG) characteristics that can predict outcome on cardiac arrest patients treated with targeted temperature management (TTM). METHODS: We retrospectively examined clinical, EEG, functional outcome at discharge, and in-hospital mortality for 373 adult subjects with return of spontaneous circulation after cardiac arrest. Poor outcome was defined as a Cerebral Performance Category score of 3-5. Pure suppression-burst (SB) was defined as SB not associated with status epilepticus (SE), seizures, or generalized periodic discharges. RESULTS: In-hospital mortality was 68.6% (N=256). Presence of both unreactive EEG background and SE was associated with a positive predictive value (PPV) of 100% (95% confidence interval: 0.96-1) and a false-positive rate (FPR) of 0% (95% CI: 0-0.11) for poor functional outcome. A prediction model including demographics data, admission exam, presence of status epilepticus, pure SB, and lack of EEG reactivity had an area under the curve of 0.92 (95% CI: 0.87-0.95) for poor functional outcome prediction, and 0.96 (95% CI: 0.94-0.98) for in-hospital mortality. Presence of pure SB (N=87) was confounded by anesthetics use in 83.9% of the cases, and was not an independent predictor of poor functional outcome, having a FPR of 23% (95% CI: 0.19-0.28). CONCLUSIONS: An unreactive EEG background and SE predicted poor functional outcome and in-hospital mortality in cardiac arrest patients undergoing TTM. Prognostic value of pure SB is confounded by use of sedative agents, and its use on prognostication decisions should be made with caution.

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.

Malignant EEG patterns in cardiac arrest patients treated with targeted temperature management who survive to hospital discharge.

BACKGROUND AND PURPOSE: Cardiac arrest patients treated with targeted temperature management (TTM) have improved neurological outcomes, however mortality remains high. EEG monitoring improves detection of malignant EEG patterns (MEPs), however their prevalence in patients surviving to hospital discharge is unknown. DESIGN/METHODS: We examined consecutive cardiac arrest subjects who received TTM and continuous EEG monitoring at one academic center. Only subjects surviving to hospital discharge were included in the analysis. MEPs were defined as seizures, status epilepticus, myoclonic status epilepticus, or generalized periodic discharges. Subjects with suppression-burst (SB) without concomitant MEPs were categorized as having a "pure" SB pattern. Demographic, survival, hospital discharge disposition, and neurological function data were recorded retrospectively. Outcomes were assessed using the Glasgow-Pittsburgh Cerebral Performance Category (CPC). A CPC score of 1-2 was considered "good" neurological function, and a CPC of 3-4 "poor". RESULTS: Of 364 admissions due to cardiac arrest screened, 120 (29.9%) survived to hospital discharge and met inclusion criteria. MEPs and pure SB were observed in 19 (15.8%) and 22 (18.3%) survivors respectively. Two subjects with MEP and eight subjects with pure SB had good neurological function at discharge, however all SB cases were confounded by the use of anesthetic agents. Presence of MEPs was not an independent predictor of poor neurological function (p=0.1). CONCLUSIONS: MEPs are common among cardiac arrest patients treated with induced hypothermia who survive to hospital discharge. Poor neurological function at discharge was not associated with MEPs. Prospective studies assessing the role of EEG monitoring in cardiac arrest prognostication are warranted.

Racial differences in demographics, acute complications, and outcomes in patients with subarachnoid hemorrhage: a large patient series.

OBJECT: Few studies have focused on the impact of racial differences in demographics, clinical characteristics, acute complications, and outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH). The purpose of this study was to examine this issue. METHODS: The authors evaluated prospectively collected data on 1711 adult patients with aneurysmal SAH who were entered into two randomized, double-blind, placebo-controlled trials conducted at neurosurgical centers in North America between 1991 and 1997. Admission characteristics, treatment modalities, in-hospital complications, and 3-month outcomes assessed by application of the Glasgow Outcome Scale were compared using the chi-square test, a t-test, the Wilcoxon rank-sum test, and multiple logistic regressions based on a significance level of 0.05 in 241 African-American, 1342 Caucasian, and 128 other racial minority patients. Caucasian patients were significantly older than patients of other races (p < 0.0001). African-American patients more frequently had a history of hypertension (p < 0.0001) and an elevated blood pressure at the time of admission (p < 0.0001). African-Americans and other racial minorities were more likely to have internal carotid artery aneurysms and Caucasians were more likely to have posterior circulation aneurysms (p = 0.0002). Rates of in-hospital complications were not significantly different except that pulmonary edema occurred more commonly in Caucasians (p = 0.036). After an adjustment was made for significant admission characteristics, the 3-month outcome was not significantly different among the races. CONCLUSIONS: Race was not found to be a prognostic factor for outcome after aneurysmal SAH. The higher SAH mortality rate previously observed in African-American patients is likely a result of a higher incidence of SAH in this group. These findings highlight the importance of primary prevention programs aimed at modifying risk factors for SAH.

Early vasospasm on admission angiography in patients with aneurysmal subarachnoid hemorrhage is a predictor for in-hospital complications and poor outcome.

BACKGROUND AND PURPOSE: Early vasospasm (EVSP), defined here as arterial narrowing seen on diagnostic angiography within the first 48 hours of aneurysmal rupture, is a rarely reported and poorly defined phenomenon in patients with subarachnoid hemorrhage (SAH). The purpose of this study was to characterize EVSP in a large database of such patients. METHODS: We analyzed the relationship of EVSP to clinical characteristics, in-hospital complications, and outcome at 3 months among 3478 patients entered into 4 prospective, randomized, double-blind, placebo-controlled trials of tirilazad conducted in neurosurgical centers around the world between 1991 and 1997. RESULTS: Three hundred thirty-nine (10%) of 3478 patients had EVSP. EVSP was significantly more likely in patients with poor neurological grade on admission, history of SAH, intracerebral hematoma, larger aneurysm, thick SAH on cranial computed tomography, and intraventricular hemorrhage. EVSP was not associated with delayed cerebral vasospasm. After adjustment for differences in admission characteristics, EVSP was associated with cerebral infarction (adjusted odds ratios [OR]=1.51; 95% CI, 1.18 to 1.94; P=0.001), neurological worsening (OR=1.41; 95% CI, 1.10 to 1.81; P=0.007), and unfavorable outcome (OR=1.51; 95% CI, 1.15 to 2.00; P=0.003). In addition, there was a trend for patients with increasingly severe EVSP to have unfavorable outcome (OR=1.84 for mild and OR=2.66 for moderate/severe EVSP). CONCLUSIONS: EVSP was seen in 10% of SAH patients and was predictive of cerebral infarction and neurological worsening as well as unfavorable outcome at 3 months. EVSP was not associated with late vasospasm. EVSP may be as important as delayed vasospasm in predicting complications and long-term morbidity in SAH patients.