aceDevelopment and Prospective Validation of an Interpretable Machine Learning Model Integrating Cardiac MRI Extracellular Volume for 1-Year MACE Prediction Post-STEMI
Rationale and objectives Extracellular volume (ECV) quantified by cardiac magnetic resonance (CMR) is a key indicator of myocardial fibrosis and strongly linked to outcomes after ST-segment elevation myocardial infarction (STEMI). This research focused on the development and validation of an interpretable machine learning (ML) model that incorporates ECV to predict 1-year major adverse cardiac events (MACE) in patients with STEMI following percutaneous coronary intervention. Materials and methods MACE prediction…
A random forest model combining clinical variables with cardiac MRI extracellular volume predicted 1-year major adverse cardiac events in STEMI patients, with explanations that clinicians can inspect.
The authors describe the model only as a proof-of-concept tool, so it is not shown to be ready for routine clinical use.
Evidence
- Peer-reviewedAcademic Radiology2026-10-14
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Truvace Impact Record TRV-2026-2000, v1: “Development and Prospective Validation of an Interpretable Machine Learning Model Integrating Cardiac MRI Extracellular Volume for 1-Year MACE Prediction Post-STEMI.” Truvace, 2026-10-20. /record/TRV-2026-2000 (accessed at citation time). sha256 c86cb351c24cfccb…
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