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Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest.

ABSTRACT: PURPOSE:To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). METHODS:Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72-96 h) post-arrest and available 6-month outcome were included. Poor outcome was defined as Cerebral Performance Category 3-5. Variations of the ERC/ESICM algorithm were explored within the same cohort. RESULTS:The ERC/ESICM algorithm identified poor outcome patients with 38.7% sensitivity (95% CI 33.1-44.7) and 100% specificity (95% CI 98.8-100) in a cohort of 585 patients. An alternative cut-off for serum neuron-specific enolase, an alternative EEG-classification and variations of the GCS-M had minor effects on the sensitivity without causing false positive predictions. The highest overall sensitivity, 42.5% (95% CI 36.7-48.5), was achieved when prognosticating patients irrespective of GCS-M score, with 100% specificity (95% CI 98.8-100) remaining. CONCLUSION:The ERC/ESICM algorithm and all exploratory multimodal variations thereof investigated in this study predicted poor outcome without false positive predictions and with sensitivities 34.6-42.5%. Our results should be validated prospectively, preferably in patients where withdrawal of life-sustaining therapy is uncommon to exclude any confounding from self-fulfilling prophecies.

SUBMITTER: Moseby-Knappe M

PROVIDER: S-EPMC7527324 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest.

Moseby-Knappe Marion M Westhall Erik E Backman Sofia S Mattsson-Carlgren Niklas N Dragancea Irina I Lybeck Anna A Friberg Hans H Stammet Pascal P Lilja Gisela G Horn Janneke J Kjaergaard Jesper J Rylander Christian C Hassager Christian C Ullén Susann S Nielsen Niklas N Cronberg Tobias T

Intensive care medicine 20200603 10

<h4>Purpose</h4>To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM).<h4>Methods</h4>Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological e ...[more]

PMID: 32494928

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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.

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Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest.

Publications

Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest.

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