Standardized EEG interpretation accurately predicts prognosis after cardiac arrest.
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ABSTRACT: Objective
To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society.Methods
In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3-5 until 180 days.Results
Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome.Conclusions
Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome.
SUBMITTER: Westhall E
PROVIDER: S-EPMC4836886 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC9468095 | biostudies-literature
Project description:Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.
| S-EPMC9068683 | biostudies-literature
Project description:ObjectivesTo assess interrater variability and prognostic value of simple EEG features based on the recent American Clinical Neurophysiology Society (ACNS) classification in post cardiac arrest comatose patients.MethodsAll patients admitted for a resuscitated cardiac arrest in a university hospital were prospectively included in the study. EEG interpretation was made by 3 independent neurophysiologists (2 senior and 1 junior) blind to the outcome. Kappa score and prognostic performances were estimated for each EEG pattern and discrepancies were analyzed.Results122 cardiac arrest patients were admitted of whom 48 went through a full neurologic evaluation. Eighty-one percent had an unfavorable outcome. Burst suppression, paroxystic seizure activity, and non-reactive EEG were strongly associated with an unfavorable evolution. Kappa score between the senior neurophysiologists was excellent or substantial while it was only fair or slight between the junior and senior neurophysiologists. Reactivity, discontinuity and electrographic seizure were patterns particularly subject to discrepancy.ConclusionsBedside EEG is an excellent tool for predicting outcome of post-anoxic coma through simple EEG features. However, the interrater variability emphasizes the need to be well trained for the standardized methods of evaluating EEG parameters.SignificanceA second look at complex patterns seems mandatory. The development of automated tools could help to improve the reliability of EEG interpretation.
| S-EPMC6389540 | biostudies-literature
Project description:Ewing's sarcoma is a high-grade malignancy bone and soft tissue tumor that most commonly occurs in children and adolescents. Although the overall prognosis of Ewing's sarcoma has improved, the 5-year survival rate has not improved significantly. The study aimed to determine the risk factors independently associated with the prognosis of Ewing's sarcoma and to construct a nomogram to predict patient survival. Patients diagnosed with Ewing's sarcoma were collected from the Surveillance, Epidemiology, and End Results program database between 2004 and 2015 and further divided into training and validation cohort. Univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors. The nomogram was used to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). Finally, the nomogram was verified internally and externally through the training and validation cohorts, and the predictive capability was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve and compared with that of the 7th TNM stage. A total of 1120 patients were divided into training (n = 713) and validation (n = 407) cohorts. Based on the multivariate analysis of the training cohort, a nomogram that integrated age, tumor size, primary site, N stage, and M stage was constructed (P < 0.05). The predicted C-indexes of OS and CSS of the training cohort were 0.744 (95% CI 0.717-0.771) and 0.743 (95% CI 0.715-0.770), respectively. However, the TNM stage had a C-index of 0.695 (95% CI 0.666-0.724) and 0.698 (95% CI 0.669-0.727) for predicting OS and CSS, respectively. The nomogram showed higher C-indexes than those in the TNM stage. Furthermore, the internal and external calibration curves showed good consistency between the predicted and observed values. Age, tumor size, primary site, N stage, and M stage are independent risk factors affecting the OS and CSS in Ewing's sarcoma patients. Compared with the 7th TNM staging, the nomogram consisting of these factors was more accurate for risk assessment and survival prediction in patients with Ewing's sarcoma, thus providing a novel reliable tool for risk assessment and survival prediction in Ewing's sarcoma patients.
| S-EPMC8608824 | biostudies-literature
Project description:ObjectiveEEG is commonly used to predict prognosis in post anoxic coma. We investigated if stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) add prognostic information after cardiac arrest.MethodsIn the multicenter Targeted Temperature Management trial, routine-EEGs were prospectively recorded after rewarming (≥36 h). Presence and subtype of SIRPIDs and main EEG-pattern (benign, malignant, highly malignant) were retrospectively reported according to a standardised classification. Patients were followed up after 180 days. Poor outcome was defined as severe neurological disability or death (Cerebral Performance Category 3-5).ResultsOf 142 patients, 71% had poor outcome and 14% had SIRPIDs. There was no significant difference in outcome between patients with and without SIRPIDs, even when subgrouped according to underlying main EEG-pattern. Comparing subtypes of SIRPIDs, 82% of patients with stimulus-induced periodic discharges had poor outcome compared to 44% of patients with stimulus-induced rhythmic delta activity, but the difference was not significant.ConclusionsIn EEGs performed ≥36 h after cardiac arrest, SIRPIDs cannot be used to reliably predict poor outcome. Whether certain subtypes of SIRPIDs indicate worse prognosis needs further investigation.SignificanceCategorising the main EEG-pattern has important prognostic implications, but assessment of late appearing SIRPIDs does not seem to add prognostic information.
| S-EPMC8350459 | biostudies-literature
Project description:BackgroundA hospital's risk-standardized survival rate (RSSR) for in-hospital cardiac arrest has emerged as an important metric to benchmark and incentivize hospital resuscitation quality. We examined whether hospital performance on the RSSR metric was stable or dynamic year-over-year and whether low-performing hospitals were able to improve survival outcomes over time.Methods and resultsWe used data from 84 089 adult patients with an in-hospital cardiac arrest from 166 hospitals with continuous participation in Get With The Guidelines-Resuscitation from 2012 to 2017. A 2-level hierarchical regression model was used to compute RSSRs during a baseline (2012-2013) and two follow-up periods (2014-2015 and 2016-2017). At baseline, hospitals were classified as top-, middle-, and bottom-performing if they ranked in the top 25%, middle 50%, and bottom 25%, respectively, on their RSSR metric during 2012 to 2013. We compared hospital performance on RSSR during follow-up between top, middle, and bottom-performing hospitals' at baseline. During 2012 to 2013, 42 hospitals were identified as top-performing (median RSSR, 31.7%), 82 as middle-performing (median RSSR, 24.6%), and 42 as bottom-performing (median RSSR, 18.7%). During both follow-up periods, >70% of top-performing hospitals ranked in the top 50%, a substantial proportion remained in the top 25% of RSSR during 2014 to 2015 (54.6%) and 2016 to 2017 (40.4%) follow-up periods. Likewise, nearly 75% of bottom-performing hospitals remained in the bottom 50% during both follow-up periods, with 50.0% in the bottom 25% of RSSR during 2014 to 2015 and 40.5% in the bottom 25% during 2016 to 2017. While percentile rankings were generally consistent over time at ≈45% of study hospitals, ≈1 in 5 (21.4%) bottom-performing hospitals showed large improvement in percentile rankings over time and a similar proportion (23.7%) of top-performing hospitals showed large decline in percentile rankings compared with baseline.ConclusionsHospital performance on RSSR during baseline period was generally consistent over 4 years of follow-up. However, 1 in 5 bottom-performing hospitals had large improvement in survival over time. Identifying care and quality improvement innovations at these sites may provide opportunities to improve in-hospital cardiac arrest care at other hospitals.
| S-EPMC7539545 | biostudies-literature
Project description:BackgroundAnoxic coma following cardiac arrest is a common problem with ethical, social, and legal consequences. Except for unfavorable somatosensory-evoked potentials (SSEP) results, predictors of unfavorable outcome with a 100% specificity and a high sensitivity are lacking. The aim of the current research was to construct a clinical and EEG scoring system that predicts early cortical response (N20) to somatosensory evoked potentials and 6-months outcome in comatose patients after cardiac arrest.MethodsWe retrospectively reviewed the records of all consecutive patients who suffered cardiac arrest outside our hospital and were subsequently admitted to our facility from November 2002 to July 2006. We scored each case based on early clinical and EEG factors associated with unfavorable SSEPs, and we assessed the ability of this score to predict SSEP results and outcome.ResultsSixty-six patients qualified for inclusion in the cohort. Among them, 34 (52%) had unfavorable SSEP results. At day three, factors independently associated with unfavorable SSEPs were: absence of corneal (14 points) and pupillary (21 points) reflexes, myoclonus (25 points), extensor or absent motor response to painful stimulation (28 points), and malignant EEG (11 points). A score >40 points had a sensitivity of 85%, a specificity of 84%, and a positive predictive value (PPV) of 85% to predict unfavorable SSEP results. A score >88 points had a PPV of 100%, but a sensitivity of 18%. Overall, this score had an area under ROC curves of 0.919. In addition, at day three, a score > 69 points had a PPV of 100% with a sensitivity of 32% to predict death or vegetative state.ConclusionA scoring system based on a combination of clinical and EEG findings can predict the absence of early cortical response to SSEPs. In settings without access to SSEPs, this score may help decision-making in a subset of comatose survivors after a cardiac arrest.
| S-EPMC2630986 | biostudies-literature
Project description:ObjectivesHypothermia improves outcomes after cardiac arrest (CA), while hyperthermia worsens injury. EEG recovers through periodic bursting from isoelectricity after CA, the duration of which is associated with outcome in normothermia. We quantified burst frequency to study the effect of temperature on early EEG recovery after CA.MethodsTwenty-four rats were divided into three groups, based on 6h of hypothermia (T=33 degrees C), normothermia (T=37 degrees C), or hyperthermia (T=39 degrees C) immediately post-resuscitation from 7-min asphyxial CA. Temperature was maintained using surface cooling and re-warming. Neurological recovery was defined by 72-h neurological deficit score (NDS).ResultsBurst frequency was higher during the first 90min in rats treated with hypothermia (25.6+/-12.2min(-1)) and hyperthermia (22.6+/-8.3min(-1)) compared to normothermia (16.9+/-8.5min(-1)) (p<0.001). Burst frequency correlated strongly with 72-h NDS in normothermic rats (p<0.05) but not in hypothermic or hyperthermic rats. The 72-h NDS of the hypothermia group (74, 61-74; median, 25-75th percentile) was significantly higher than the normothermia (49, 47-61) and hyperthermia (43, 0-50) groups (p<0.001).ConclusionsIn normothermic rats resuscitated from CA, early EEG burst frequency is strongly associated with neurological recovery. Increased bursting followed by earlier restitution of continuous EEG activity with hypothermia may represent enhanced recovery, while heightened metabolic rate and worsening secondary injury is likely in the hyperthermia group. These factors may confound use of early burst frequency for outcome prediction.
| S-EPMC2570264 | biostudies-literature
Project description:PurposeWe aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.MethodsThis was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.ResultsEighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.ConclusionsThe specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.
| S-EPMC6731130 | biostudies-literature
Project description:BackgroundAlthough the prognosis of patients treated at specialized facilities has improved, the relationship between the number of patients treated at hospitals and prognosis is controversial and lacks constancy in those with out-of-hospital cardiac arrest (OHCA). This study aimed to clarify the effect of annual hospital admissions on the prognosis of adult patients with OHCA by analyzing a large cohort.MethodsThe effect of annual hospital admissions on patient prognosis was analyzed retrospectively using data from the Japanese Association for Acute Medicine OHCA registry, a nationwide multihospital prospective database. This study analyzed 3632 of 35,754 patients hospitalized for OHCA of cardiac origin at 86 hospitals. The hospitals were divided into tertiles based on the volume of annual admissions. The effect of hospital volume on prognosis was analyzed using logistic regression analysis with multiple imputation. Furthermore, three subgroup analyses were performed for patients with return of spontaneous circulation (ROSC) before arrival at the emergency department, patients admitted to critical care medical centers, and patients admitted to extracorporeal membrane oxygenation-capable hospitals.ResultsFavorable neurological outcomes 30 days after OHCA for patients overall showed no advantage for medium- and high-volume centers over low-volume centers; Odds ratio (OR) 0.989, (95% Confidence interval [CI] 0.562-1.741), OR 1.504 (95% CI 0.919-2.463), respectively. However, the frequency of favorable neurological outcomes in OHCA patients with ROSC before arrival at the emergency department at high-volume centers was higher than those at low-volume centers (OR 1.955, 95% CI 1.033-3.851).ConclusionHospital volume did not significantly affect the prognosis of adult patients with OHCA. However, transport to a high-volume hospital may improve the neurological prognosis in OHCA patients with ROSC before arrival at the emergency department.
| S-EPMC9261001 | biostudies-literature