Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest.

Area of Science:

• Artificial Intelligence in Medicine
• Cardiology
• Emergency Medicine

Background:

• Electrocardiogram (ECG) signals in cardiac arrest offer insights into cardiac rhythm and resuscitation success.
• Predicting return of spontaneous circulation (ROSC) and understanding ECG characteristics in out-of-hospital cardiac arrest (OHCA) are crucial for improving patient outcomes.

Purpose of the Study:

• To develop a convolutional neural network (CNN) model for predicting ROSC in OHCA patients.
• To identify novel ECG phenotypes associated with different ROSC probabilities and rhythms.

Main Methods:

• A retrospective cohort study analyzed ECG data from 3,452 adult OHCA patients.
• A 1D CNN was developed to predict ROSC within 2 minutes and classify ECG rhythms.
• K-means clustering was used to identify ECG phenotypes from CNN feature representations.

Main Results:

• The CNN model achieved high accuracy in predicting ROSC (AUC 0.921) and shockable rhythms (AUC 0.983).
• Five distinct ECG phenotypes were identified, each associated with varying ROSC probabilities.
• Phenotypes ranged from shockable rhythms with high ROSC (30.4%) to asystole with low ROSC.

Conclusions:

• A CNN-based model accurately predicts ROSC and shockable rhythms from OHCA ECGs.
• Novel ECG phenotypes were identified, offering potential for individualized prehospital resuscitation strategies.
• This approach can enhance clinical decision-making and improve outcomes for OHCA patients.