Out-of-hospital cardiac arrest (OHCA) is a major health concern, with an incidence of approximately 55 per 100,000 person-years in the United States. Pulseless electrical activity (PEA) is a cardiac rhythm observed in 20-30% of OHCA cases and it consists on a regular electrical activity presenting disassociation with cardiac mechanical contractions. Discriminating those PEA with favorable prognosis is crucial to decide pre/post resuscitation therapy. A machine learning model is proposed to assist rescuers to predict evolution of PEA. The ECG and the transthoracic impedance recorded using defibrillation pads were integrated in the model, together with the invasive blood pressure. A total of 238 PEA segments were extracted from 49 patients. A Random Forest model was trained with 25 features extracted from the three signals to discriminate between the PEA with favorable prognosis (return of spontaneous circulation). The optimal model showed median (interquartile range) values of 88.9(14.2)% for Area Under the Curve, 94.1(21.7)% for Sensitivity, 68.1(30.6)% for Specificity, 66.4(29.5)% for Positive Predictive Value, and 87.5(21.5)% for Negative Predictive Value.Clinical relevance- The study concludes that adding IBP based features to models traditionally based on ECG and TTI enhances PEA prognosis prediction during OHCA.