Cardiac arrest & resuscitation
Development of the CASPER-Score: A bedside tool integrating artificial intelligence (AI)-based electrocardiogram (ECG) analysis with key clinical predictors to identify relevant coronary artery stenosis in patients after out-of-hospital cardiac arrest
Authors' conclusion
The CASPER-Score may improve early risk stratification and support decision-making regarding coronary angiography in OHCA patients.
Study context
- Population
- Out-of-hospital cardiac arrest (OHCA) patients admitted to the ICU 01/2019–12/2021 who underwent coronary angiography within 7 days
- Participants
- 204 patients
- Outcome
- Relevant coronary artery stenosis (≥50% diameter stenosis of left main or ≥70% of all other vessels)
Methodological detailssingle-centre, prediction model development
- Single-centre
- Prediction model development
Main finding
As reported in the abstract
Relevant coronary artery stenosis
Relevant coronary artery stenosis present
157
77%
CASPER-Score (range 0-8 points) discrimination, AUC
0.86
Observational data: associations, not effects.
Secondary outcomes
- Underwent coronary revascularization
- 127 (62.3%)
- Model predicting revascularization, AUC (moderate discrimination)
- 0.74
Original publication
Development of the CASPER-Score: A bedside tool integrating AI-based ECG analysis with key clinical predictors to identify relevant coronary artery stenosis in patients after out-of-hospital cardiac arrest. Resuscitation. Published 7 July 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →