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Neurophysiological and neuroradiological test for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest: Prospective multicentre prognostication data.

ABSTRACT: The data presented here are related to our research article entitled "Neurophysiology and neuroimaging accurately predict poor neurological outcome within 24 hours after cardiac arrest: a prospective multicentre prognostication study (ProNeCA)" [1]. We report a secondary analysis on the ability of somatosensory evoked potentials (SEPs), brain computed tomography (CT) and electroencephalography (EEG) to predict poor neurological outcome at 6 months in 346 patients who were comatose after cardiac arrest. Differently from the related research article, here we included cerebral performance category (CPC) 3 among poor outcomes, so that the outcomes are dichotomised as CPC 1-2 (absent to mild neurological disability: good outcome) vs. CPC 3-5 (severe neurological disability, persistent vegetative state, or death: poor outcome). The accuracy of the index tests was recalculated accordingly. A bilaterally absent/absent-pathological amplitude (AA/AP) N20 SEPs wave, a Grey Matter/White Matter (GM/WM) ratio <1.21 on brain CT and an isoelectric or burst suppression EEG predicted poor outcome with 49.6%, 42.2% and 29.8% sensitivity, respectively, and 100% specificity. The distribution of positive results of the three predictors did not overlap completely in the population of patients with poor outcome, so that when combining them the overall sensitivity raised to 61.2%.

SUBMITTER: Scarpino M

PROVIDER: S-EPMC6864134 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Neurophysiological and neuroradiological test for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest: Prospective multicentre prognostication data.

Scarpino Maenia M Lolli Francesco F Lanzo Giovanni G Carrai Riccardo R Spalletti Maddalena M Valzania Franco F Lombardi Maria M Audenino Daniela D Celani Maria Grazia MG Marrelli Alfonso A Contardi Sara S Peris Adriano A Amantini Aldo A Sandroni Claudio C Grippo Antonello A

Data in brief 20191104

The data presented here are related to our research article entitled "Neurophysiology and neuroimaging accurately predict poor neurological outcome within 24 hours after cardiac arrest: a prospective multicentre prognostication study (ProNeCA)" [1]. We report a secondary analysis on the ability of somatosensory evoked potentials (SEPs), brain computed tomography (CT) and electroencephalography (EEG) to predict poor neurological outcome at 6 months in 346 patients who were comatose after cardiac ...[more]

PMID: 31763408

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Project description:Background We assessed the prognostic accuracy of the standardized electroencephalography (EEG) patterns (“highly malignant,” “malignant,” and “benign”) according to the EEG timing (early vs. late) and investigated the EEG features to enhance the predictive power for poor neurologic outcome at 1 month after cardiac arrest. Methods This prospective, multicenter, observational, cohort study using data from Korean Hypothermia Network prospective registry included adult patients with out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM) and underwent standard EEG within 7 days after cardiac arrest from 14 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. Early EEG was defined as EEG performed within 72 h after cardiac arrest. The primary outcome was poor neurological outcome (Cerebral Performance Category score 3–5) at 1 month. Results Among 489 comatose OHCA survivors with a median EEG time of 46.6 h, the “highly malignant” pattern (40.7%) was most prevalent, followed by the “benign” (33.9%) and “malignant” (25.4%) patterns. All patients with the highly malignant EEG pattern had poor neurologic outcomes, with 100% specificity in both groups but 59.3% and 56.1% sensitivity in the early and late EEG groups, respectively. However, for patients with “malignant” patterns, 84.8% sensitivity, 77.0% specificity, and 89.5% positive predictive value for poor neurologic outcome were observed. Only 3.5% (9/256) of patients with background EEG frequency of predominant delta waves or undetermined had good neurologic survival. The combination of “highly malignant” or “malignant” EEG pattern with background frequency of delta waves or undetermined increased specificity and positive predictive value, respectively, to up to 98.0% and 98.7%. Conclusions The “highly malignant” patterns predicted poor neurologic outcome with a high specificity regardless of EEG measurement time. The assessment of predominant background frequency in addition to EEG patterns can increase the prognostic value of OHCA survivors. Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-021-03823-y.

Project description:BACKGROUND:Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family's intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM. METHODS:Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34?°C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00?am within 24?h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2?weeks after ROSC, patients were divided into good outcome (GOS 1-2) and poor outcome (GOS 3-5) groups. RESULTS:Seventy-six patients were recruited and allocated to the good (n?=?22) or poor (n?=?54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (?1), and multiscale entropy (MSE) index significantly differed between the groups (p?=?0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p?=?0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (?1) as significant predictors for poor outcome (OR?=?0.436, p?=?0.006 and OR?=?0.709, p?=?0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (?1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (?1) for the good outcome group predicted poor outcome with sensitivity?=?61% and specificity?=?100%. CONCLUSIONS:The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

Dataset Information

Neurophysiological and neuroradiological test for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest: Prospective multicentre prognostication data.

Publications

Neurophysiological and neuroradiological test for early poor outcome (Cerebral Performance Categories 3-5) prediction after cardiac arrest: Prospective multicentre prognostication data.

Similar Datasets

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