AI-Driven Prediction of ROSC Using EtCO2 Trends and Response Dynamics Following Vasopressor Administration

Capnography is widely used during resuscitation to monitor ventilation and perfusion. Prior studies have shown that out-of-hospital cardiac arrest (OHCA) outcomes are associated with end-tidal capnography (EtCO2) trends. However, the use of artificial intelligence (AI) to predict return of spontaneous circulation (ROSC) based on EtCO2 trends remains unexplored. We aimed to develop an AI model to predict ROSC using EtCO2 trends and identify influential features. We conducted a secondary analysis of the Pragmatic Airway Resuscitation Trial (PART), including previously calculated EtCO2 slope and post-vasopressor response features, along with patient demographics. An AI-based Random Forest classifier was trained to predict ROSC, and feature importance scores were extracted. The model achieved 83% accuracy, indicating strong performance in identifying both ROSC and non-ROSC cases. Of the top five features ranked by the model, four were related to EtCO2 trends and response to vasopressor administration. AI-driven EtCO2 algorithms may strongly influence resuscitation decisions.