Dataset Information

Duration and clinical features of cardiac arrest predict early severe cerebral edema.

ABSTRACT:

Background

Severe brain edema appears early after cardiopulmonary resuscitation (CPR) in a subset of patients and portends a poor prognosis. We tested whether clinical features of patients or resuscitation during out-of-hospital cardiac arrest (OHCA) are associated with early, severe cerebral edema.

Method/research design

We reviewed pre-hospital and hospital records for comatose patients surviving to hospital admission after OHCA who had computed tomography (CT) of brain at the time of hospital admission available for inspection. We measured the gray-white ratio (GWR) of X-ray attenuation between the caudate nucleus and posterior limb of the internal capsule, defining severe cerebral edema as GWR < 1.20. We calculated associations between severe cerebral edema and patient or resuscitation variables.

Results

Between 2010 and 2019, 1340 subjects were admitted of whom 296 (22%) showed severe cerebral edema on initial CT. Subjects with severe edema had lower survival (5/296, 2% vs. 377/1044, 36%). Severe edema was independently associated with total CPR duration, total dose of epinephrine, younger age, non-shockable arrest rhythms, fewer total number of rescue shocks, rearrest after initial return of pulses, and non-cardiac arrest etiology. Prevalence of severe cerebral edema increased from 2% among subjects with 0-10 min of CPR to 31% among subjects with >40 min of CPR.

Conclusion

CPR duration along with easily measurable clinical and resuscitation characteristics predict early severe cerebral edema after OHCA. Future interventional trials should consider targeting or preventing cerebral edema after prolonged hypoxic-ischemic brain injury especially in patients with high risk clinical features.

SUBMITTER: Esdaille CJ

PROVIDER: S-EPMC7733324 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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.

Project description:BackgroundIllness severity scores are commonly employed in critically ill patients to predict outcome. To date, prior scores for post-cardiac arrest patients rely on some event-related data. We developed an early, novel post-arrest illness severity score to predict survival, good outcome and development of multiple organ failure (MOF) after cardiac arrest.MethodsRetrospective review of data from adults treated after in-hospital or out-of-hospital cardiac arrest in a single tertiary care facility between 1/1/2005 and 12/31/2009. In addition to clinical data, initial illness severity was measured using serial organ function assessment (SOFA) scores and full outline of unresponsiveness (FOUR) scores at hospital or intensive care unit arrival. Outcomes were hospital mortality, good outcome (discharge to home or rehabilitation) and development of multiple organ failure (MOF). Single-variable logistic regression followed by Chi-squared automatic interaction detector (CHAID) was used to determine predictors of outcome. Stepwise multivariate logistic regression was used to determine the independent association between predictors and each outcome. The Hosmer-Lemeshow test was used to evaluate goodness of fit. The n-fold method was used to cross-validate each CHAID analysis and the difference between the misclassification risk estimates was used to determine model fit.ResultsComplete data from 457/495 (92%) subjects identified distinct categories of illness severity using combined FOUR motor and brainstem subscales, and combined SOFA cardiovascular and respiratory subscales: I. Awake; II. Moderate coma without cardiorespiratory failure; III. Moderate coma with cardiorespiratory failure; and IV. Severe coma. Survival was independently associated with category (I: OR 58.65; 95% CI 27.78, 123.82; II: OR 14.60; 95% CI 7.34, 29.02; III: OR 10.58; 95% CI 4.86, 23.00). Category was also similarly associated with good outcome and development of MOF. The proportion of subjects in each category changed over time.ConclusionsInitial illness severity explains much of the variation in cardiac arrest outcome. This model provides prognostic information at hospital arrival and may be used to stratify patients in future studies.