Coronary disease & ACSDiagnostic or prognostic study
Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence
- development and validation of an explainable artificial intelligence approach
Authors' conclusion
Explainable artificial intelligence enabled rapid integration of clinical parameters from cardiac catheterization procedures to accurately predict IMH in STEMI before revascularization. The proposed approach could guide clinical decisions in real-time. This could inform future strategies aimed at reducing the incidence of IMH.
Study context
- Population
- STEMI patients from the MIRON-PREDICT clinical study, enrolled between June 2023 and December 2024STEMI patients enrolled · model development 252 · validation 36
- Participants
- 288 patients
- Comparison
- Point-based 3-variable model (Superposable Neural Network-informed) against cardiac magnetic resonance imaging 48 to 72 hours post-percutaneous coronary intervention as the reference for intramyocardial hemorrhage (IMH)
- developmentn = 252
- validation cohortn = 36
Performance
As reported in the abstract
IMH identified
142/288
Further findings
Model performance for identifying patients at risk of IMH before reperfusion
accuracy
sensitivity
specificity
Coronary collaterals and total occlusion
- Coronary collaterals present
- 144/288
- Total occlusion
- 131/288
Sum ST-score
- Substantially higher in IMH-positive than IMH-negative patients
Model performance for identifying patients at risk of IMH before reperfusion
- Final 3-variable model: presence of coronary collaterals, degree of coronary artery occlusion, and sum ST-score
Original publication
Summarised from the published abstract; every number is checked against it