In-hospital and long term neurological outcome prognostication using an algorithm based on the analysis of ventricular fibrillation from the EKG of patients with sudden cardiac death (AWAKE)

Abstract Background Sudden cardiac death (SCD) is a leading cause of mortality and carries a dismal neurological outcome (NO) among patients admitted alive to acute cardiac care units (ACCU). A big share of SCD are found in ventricular fibrillation (VF). VF waveform is dynamic and it can be characterized by spectral analysis at the very time of ACCU admission. Purpose To validate in a multicenter cohort an algorithm based on spectral variables of VF waveform to early predict neurological outcome at hospital discharge and follow-up, and to compare its performance with currently established prognostic tools. Methods Multicenter study in which 5 hospitals recruited patients, resuscitated from SCD due to VF whose EKG was available, and admitted to ACCU with expertise in SCD. EKG tracings were scanned and analyzed with a custom MATLAB script delivering spectral variables to be entered in an algorithm to predict NO. NO was assessed by Cerebral Performance Category (CPC) during admission and after a minimum 6-month follow-up period. The primary endpoint was favorable neurological outcome (FNO, defined as CPC 1 and 2) during hospital admission, and the secondary endpoint was FNO at long-term follow-up. Statistical data were analyzed with SPSS and EPIDAT. Results We included 168 patients out of 678 screened in participant hospitals among admissions from 2007 to 2022. The main cause of exclusion was a rhythm different from VF at the time of medical assistance (n=329, 48%) or VF EKG unavailable (n=151, 22%). Ninety-five patients (57%) were admitted in comatose status (Glasgow