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.

Early risk stratification after resuscitation from cardiac arrest.

Emergency clinicians often resuscitate cardiac arrest patients, and after acute resuscitation, clinicians face multiple decisions regarding disposition. Recent evidence suggests that out-of-hospital cardiac arrest patients with return of spontaneous circulation have higher odds of survival to hospital discharge, long-term survival, and improved functional outcomes when treated at centers that can provide advanced multidisciplinary care. For community clinicians, a high volume cardiac arrest center may be hours away. While current guidelines recommend against neurological prognostication in the first hours or days after return of spontaneous circulation, there are early findings suggestive of irrecoverable brain injury in which the patient would receive no benefit from transfer. In this Concepts article, we describe a simplified approach to quickly evaluate neurological status in cardiac arrest patients and identify findings concerning for irrecoverable brain injury. Characteristics of the arrest and resuscitation, initial neurological assessment, and brain computed tomography together can identify patients with high likelihood of irrecoverable anoxic injury. Patients who may benefit from centers with access to continuous electroencephalography are discussed. This approach can be used to identify patients who may benefit from rapid transfer to cardiac arrest centers versus those who may benefit from care close to home. Risk stratification also can provide realistic expectations for recovery to families.

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.

Association of antiepileptic drugs with resolution of epileptiform activity after cardiac arrest.

INTRODUCTION: We tested the impact of antiepileptic drug (AED) administration on post-cardiac arrest epileptiform electroencephalographic (EEG) activity. METHODS: We studied an observational cohort of comatose subjects treated at a single academic medical center after cardiac arrest from September 2010 to January 2018. We aggregated the observed EEG patterns into 5 categories: suppressed; discontinuous background with superimposed epileptiform activity; discontinuous background without epileptiform features; continuous background with epileptiform activity; and continuous background without epileptiform activity. We calculated overall probabilities of transitions between EEG states in a multistate model, then used Aalen's additive regression to test if AEDs or hypothermia are associated with a change in these probabilities. RESULTS: Overall, 828 subjects had EEG-monitoring for 42,840 h with a median of 40 [IQR 23-64] h per subject. Among patients with epileptiform findings on initial monitoring, 50% transitioned at least once to a non-epileptiform, non-suppressed state. By contrast, 19% with non-epileptiform initial activity transitioned to an epileptiform state at least once. Overall, 568 (78%) patients received at least one AED. Among patients with continuous EEG background activity, valproate, levetiracetam and lower body temperature were each associated with an increased probability of transition from epileptiform states to non-epileptiform states, where patients with discontinuous EEG background activity no agent linked to an increased probability of transitioning from epileptiform states. CONCLUSION: After cardiac arrest, the impact of AEDs may depend on the presence of continuous cortical background activity. These data serve to inform experimental work to better define the opportunities to improve neurologic care post-cardiac arrest.

Preliminary experience with point-of-care EEG in post-cardiac arrest patients.

OBJECTIVE: Abnormal electroencephalography (EEG) patterns are common after resuscitation from cardiac arrest and have clinical and prognostic importance. Bedside continuous EEGs are not available in many institutions. We tested the feasibility of using a point-of-care system for EEG acquisition. METHODS: We prospectively enrolled a convenience sample of post-cardiac arrest patients between 9/2015-1/2017. Upon hospital arrival, a limited EEG montage was applied. We tested both continuous EEG (cEEG) and this point-of-care EEG (eEEG). A board-certified epileptologist and a board-certified neurointensivist jointly reviewed all EEGs. Cohen's kappa coefficient evaluated agreement between eEEG and cEEG and Fisher's exact test evaluated their associations with survival to hospital discharge and proximate cause of death. RESULTS: We studied 95 comatose post-cardiac arrest patients. Mean age was 59 (SD17) years. Most (61%) were male, few (N = 22; 23%) demonstrated shockable rhythms, and PCAC IV illness severity was present in 58 (61%). eEEG was interpretable in 57 (60%) subjects. The most common eEEG interpretations were: continuous (21%), generalized suppression (14%), burst-suppression (12%) and burst-suppression with identical bursts (10%). Seizures were detected in 2 eEEG subjects (2%). No patient with seizure or burst-suppression with identical bursts survived. cEEG demonstrated generalized suppression (31%), burst-suppression with identical bursts (27%), continuous (18%) and seizure (4%). The eEEG and cEEG demonstrated fair agreement (kappa = 0.27). Neither eEEG nor cEEG was associated with survival (p = 0.19; p = 0.11) or proximate cause of death (p = 0.14; p = 0.8) CONCLUSIONS: eEEG is feasible, although artifact often precludes interpretation. eEEG is fairly associated with cEEG and may facilitate post-cardiac arrest care.

Differential association of subtypes of epileptiform activity with outcome after cardiac arrest.

BACKGROUND: Epileptiform activity is common after cardiac arrest, although intensity of electroencephalographic (EEG) monitoring may affect detection rates. Prior work has grouped these patterns together as "malignant," without considering discrete subtypes. We describe the incidence of distinct patterns in the ictal-interictal spectrum at two centers and their association with outcomes. METHODS: We analyzed a retrospective cohort of comatose post-arrest patients admitted at two academic centers from January 2011 to October 2014. One center uses routine continuous EEG, the other acquires "spot" EEG at the treating physicians' discretion. We reviewed all available EEG data and classified epileptiform patterns. We abstracted antiepileptic drugs (AEDs) administrations from the electronic medical record. We compared apparent incidence of each pattern between centers, and compared outcomes (awakening from coma, survival to discharge, discharge modified Rankin Scale (mRS) 0-2) across EEG patterns and number of AEDs administered. RESULTS: We included 818 patients. Routine continuous EEG was associated with a higher apparent incidence of polyspike burst-suppression (25% vs 13% P < 0.001). Frequency of other epileptiform findings did not differ. Among patients with any epileptiform pattern, only 2/258 (1%, 95%CI 0-3%) were discharged with mRS 0-2, although 24/258 (9%, 95%CI 6-14%) awakened and 36/258 (14%, 95%CI 10-19%) survived. The proportions that awakened and survived decreased in a stepwise manner with progressively worse EEG patterns (range 38% to 2% and 32% to 7%, respectively). Among patients receiving ≥3 AEDs, only 5/80 (6%, 95%CI 2-14%) awakened and 1/80 (1%, 95%CI 0-7%) had a mRS 0-2. CONCLUSION: We found high rates of epileptiform EEG findings, regardless of intensity of EEG monitoring. The association of distinct ictal-interictal EEG findings with outcome was variable.

Neurostimulant use is associated with improved survival in comatose patients after cardiac arrest regardless of electroencephalographic substrate.

AIM: Identify EEG patterns that predict or preclude favorable response in comatose post-arrest patients receiving neurostimulants. METHODS: We examined a retrospective cohort of consecutive electroencephalography (EEG)-monitored comatose post-arrest patients. We classified the last day of EEG recording before neurostimulant administration based on continuity (continuous/discontinuous), reactivity (yes/no) and malignant patterns (periodic discharges, suppression burst, myoclonic status epilepticus or seizures; yes/no). In subjects who did not receive neurostimulants, we examined the last 24h of available recording. For our primary analysis, we used logistic regression to identify EEG predictors of favorable response to treatment (awakening). RESULTS: In 585 subjects, mean (SD) age was 57 (17) years and 227 (39%) were female. Forty-seven patients (8%) received a neurostimulant. Neurostimulant administration independently predicted improved survival to hospital discharge in the overall cohort (adjusted odds ratio (aOR) 4.00, 95% CI 1.68-9.52) although functionally favorable survival did not differ. No EEG characteristic predicted favorable response to neurostimulants. In each subgroup of unfavorable EEG characteristics, neurostimulants were associated with increased survival to hospital discharge (discontinuous background: 44% vs 7%, P=0.004; non-reactive background: 56% vs 6%, P<0.001; malignant patterns: 63% vs 5%, P<0.001). CONCLUSION: EEG patterns described as ominous after cardiac arrest did not preclude survival or awakening after neurostimulant administration. These data are limited by their observational nature and potential for selection bias, but suggest that EEG patterns alone should not affect consideration of neurostimulant use.

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.

Features of Simultaneous Scalp and Intracranial EEG That Predict Localization of Ictal Onset Zone.

OBJECTIVE: To assess the utility of simultaneous scalp EEG in patients with focal epilepsy undergoing intracranial EEG evaluation after a detailed presurgical testing, including an inpatient scalp video EEG evaluation. METHODS: Patients who underwent simultaneous scalp and intracranial EEG (SSIEEG) monitoring were classified into group 1 or 2 depending on whether the seizure onset zone was delineated or not. Seizures were analyzed using the following 3 EEG features at the onset of seizures latency, location, and pattern. RESULTS: The criteria showed at least one of the following features when comparing SSIEEG: prolonged latency, absence of anatomical congruence, lack of concordance of EEG pattern in 11.11% (1/9) of the patients in group 1 and 75 % (3/4) of the patients in group 2. These 3 features were not present in any of the 5 patients who had Engel class I outcome compared with 1 of the 2 patients (50%) who had seizure recurrence after resective surgery. The mean latency of seizure onset in scalp EEG compared with intracranial EEG of patients in group 1 was 17.48 seconds (SD = 16.07) compared with 4.33 seconds (SD = 11.24) in group 2 ( P = .03). None of the seizures recorded in patients in group 1 had a discordant EEG pattern in SSIEEG. CONCLUSION: Concordance in EEG features like latency, location, and EEG pattern, at the onset of seizures in SSIEEG is associated with a favorable outcome after epilepsy surgery in patients with intractable focal epilepsy. SIGNIFICANCE: Simultaneous scalp EEG complements intracranial EEG evaluation even after a detailed inpatient scalp video EEG evaluation and could be part of standard intracranial EEG studies in patients with intractable focal epilepsy.

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.

Clinically distinct electroencephalographic phenotypes of early myoclonus after cardiac arrest.

OBJECTIVE: We tested the hypothesis that there are readily classifiable electroencephalographic (EEG) phenotypes of early postanoxic multifocal myoclonus (PAMM) that develop after cardiac arrest. METHODS: We studied a cohort of consecutive comatose patients treated after cardiac arrest from January 2012 to February 2015. For patients with clinically evident myoclonus before awakening, 2 expert physicians reviewed and classified all EEG recordings. Major categories included: Pattern 1, suppression-burst background with high-amplitude polyspikes in lockstep with myoclonic jerks; and Pattern 2, continuous background with narrow, vertex spike-wave discharges in lockstep with myoclonic jerks. Other patterns were subcortical myoclonus and unclassifiable. We compared population characteristics and outcomes across these EEG subtypes. RESULTS: Overall, 401 patients were included, of whom 69 (16%) had early myoclonus. Among these patients, Pattern 1 was the most common, occurring in 48 patients (74%), whereas Pattern 2 occurred in 8 patients (12%). The remaining patients had subcortical myoclonus (n = 2, 3%) or other patterns (n = 7, 11%). No patients with Pattern 1, subcortical myoclonus, or other patterns survived with favorable outcome. By contrast, 4 of 8 patients (50%) with Pattern 2 on EEG survived, and 4 of 4 (100%) survivors had favorable outcomes despite remaining comatose for 1 to 2 weeks postarrest. INTERPRETATION: Early PAMM is common after cardiac arrest. We describe 2 distinct patterns with distinct prognostic significances. For patients with Pattern 1 EEGs, it may be appropriate to abandon our current clinical standard of aggressive therapy with conventional antiepileptic therapy in favor of early limitation of care or novel neuroprotective strategies. Ann Neurol 2016;80:175-184.

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.

The role of voice therapy in the treatment of dyspnea and dysphonia in a patient with a vagal nerve stimulation device.

Vagal nerve stimulators (VNS) are implanted to treat medically refractory epilepsy and depression. The VNS stimulates the vagus nerve in the left neck. Laryngeal side effects are common and include dysphagia, dysphonia, and dyspnea. The current case study represents a patient with severe dyspnea and dysphonia, persisting even with VNS deactivation. The case demonstrates the use of voice and respiratory retraining therapy for the treatment of VNS-induced dysphonia and dyspnea. It also highlights the importance of a multidisciplinary approach, including laryngology, neurology, and speech-language pathology, in the treatment of these challenging patients.

Somatosensory evoked potentials help prevent positioning-related brachial plexus injury during skull base surgery.

OBJECTIVE: Evaluate the use of somatosensory evoked potentials (SSEP) monitoring to detect positioning-related brachial plexus injury during skull base surgery. STUDY DESIGN: Prospective cohort observational study. SETTING: University Hospital. SUBJECTS AND METHODS: Patients undergoing skull base surgery had a focused neurologic exam of the brachial plexus performed before and after surgery. Under stable anesthesia, brachial plexus SSEP values were obtained before and after surgical positioning. Significant SSEP changes required a readjustment of arm or neck positions. SSEPs were assessed every 30 minutes. If changes were noted, position was readjusted and SSEPs were reassessed until surgical completion. Demographic data, neurologic exams, SSEP latency, and amplitude values were recorded. Persistent changes were correlated with postoperative neurologic findings. RESULTS: Sixty-five patients, 15 to 77 years old, were studied. Six patients (9.2%) developed SSEP amplitude changes after positioning (average amplitude decrease 72.8%). One patient had a significant latency increase. The sensitivity of SSEP for detection of injury was 57%, while specificity was 94.7%. The average body mass index (BMI) of patients with normal and abnormal SSEPs was 28.7 ± 5.6 versus 29.2 ± 8.0, respectively. Average BMI of patients with postoperative symptoms regardless of SSEP findings was 33.8 ± 4.3. Two patients who had persistent SSEP changes after positioning had BMIs of 40.1 and 31.2 kg/m(2), respectively. Improvement in neurologic findings occurred in all patients after surgery. CONCLUSIONS: This study demonstrates that upper extremity nerve stress can be detected in real time using SSEP and may be of value in protecting patients from nerve injury undergoing lateral skull base surgery.