PO-03-199 EARLY NEUROLOGICAL PROGNOSTICATION USING AN ALGORITHM BASED ON SPECTRAL VARIABLES OF VENTRICULAR FIBRILLATION FROM THE EKG OF PATIENTS WITH SUDDEN CARDIAC DEATH (AWAKE)

Ventricular fibrillation (VF) is one of the leading rhythm alterations documented at time medical assistance in patients with sudden cardiac death (SCD). There an urgent need to improve current diagnostic tools early predict neurological outcome admitted alive intensive care units. To investigate predictive value algorithm based on spectral variables VF waveforms and compare its performance state-of-the-art prognostic tools. Multicenter study which 5 participant hospitals recruited acute units (ACCU) after return spontaneous circulation VF-related SCD. All available ECG tracings before first DC shock were invited participate. digitized analyzed using a custom MATLAB script obtain variables. These entered spectral-based outcome. Cerebral was assessed by Performance Category (CPC). The primary endpoint favorable (FNO) hospital discharge. included 168 678 ACCU from 2016 2022. main cause exclusion non-VF (n=329, 48%). Ninety-five (57%) comatose status (Glasgow <9). Acute or chronic ischemic heart disease SCD (n=116, 69%) (Table 1). FNO (CPC 1-2) discharge 136 (81%). Twenty-nine died during hospitalization (17%). A model predicted area under curve (AUC) 0.849 (CI95% 0.77-0.93). This similar obtained peak neuron-specific enolase (NSE) (0.847; CI95% 0.74-0.95) (Figure 1A). NSE only 28% 24h admission 1B). Other showed lower yield: accuracy for brain imaging somatosensory evoked potentials 0.76 0.65-0.88) 0.5 0.22-0.78), respectively. rarely predicts admission, no withdraw drugs waiting period patients.