Predictors of In-Hospital Mortality after Recovered Out-of-Hospital Cardiac Arrest in Patients with Proven Significant Coronary Artery Disease: A Retrospective Study

Abstract

Abstract Introduction Recovered Out-of-Hospital Cardiac Arrest (rOHCA) population is heterogenous. Few studies focused on outcomes in the rOHCA subgroup with proven significant coronary artery disease (SigCAD). We aimed to characterize this subgroup and study the determinants of in-hospital mortality. Methods Retrospective study of consecutive rOHCA patients submitted to coronary angiography. Only patients with SigCAD were included. Results 60 patients were studied, 85% were male, mean age was 62.6 ± 12.1 years. In-hospital mortality rate was 43.3%. Patients with diabetes and history of stroke were less likely to survive. Significant univariate predictors of in-hospital mortality were further analysed separately, according to whether they were present at hospital admission or developed during hospital evolution. At hospital admission, initial non-shockable rhythm, low-flow time>12min, pH<7.25mmol/L and lactates >4.75mmol/L were the most relevant predictors and therefore included in a score tested by Kaplan-Meyer. Patients who had 0/4 criteria had 100% chance of survival till hospital discharge, 1/4 had 77%, 2/4 had 50%, 3/4 had 25%. Patients with all 4 criteria had 0% survival. During in-hospital evolution, a pH<7.35 at 24h, lactates>2mmol/L at 24h, anoxic brain injury and persistent hemodynamic instability proved significant. Patients who had 0/4 of these in-hospital criteria had 100% chance of survival till hospital discharge, 1/4 had 94%, 2/4 had 47%, 3/4 had 25%. Patients with all 4 criteria had 0% survival. Contrarily, CAD severity and ventricular dysfunction didn’t significantly correlate to the outcome. Conclusion Classic prehospital variables retain their value in predicting mortality in the specific group of OHCA with SigCAD. In-hospital evolution variables proved to add value in mortality prediction. Combining these simple variables in risk scores might help refining prognostic prediction in these patients’s subset.