Using Cardiac MRI to Predict Outcomes in Patients With STEMI

This prospective, multicenter observational study aims to evaluate the prognostic value of a comprehensive set of cardiac magnetic resonance (CMR) imaging parameters in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). The study integrates advanced artificial intelligence (AI) techniques to extract and analyze high-dimensional imaging features from multiple CMR sequences-including cine, strain mapping, and functional sequences-going beyond traditional measures such as infarct size or microvascular obstruction. The primary objective is to identify novel prognostic markers from routinely acquired CMR images that reflect myocardial structure, function, and mechanical deformation (strain), and to assess their association with long-term clinical outcomes. In addition to standard parameters, the study includes a detailed evaluation of left and right ventricular systolic and diastolic volumes, ejection fractions, and biventricular strain components (including longitudinal, circumferential, and radial strain), as well as left and right atrial volumes, emptying fractions, and reservoir/conduit/booster strain indices. Approximately 1000 STEMI patients will undergo CMR scanning within one week after PCI. The imaging data will be subjected to AI-based feature extraction and dimensionality reduction algorithms to uncover latent patterns associated with adverse outcomes. Patients will be followed for up to three years for the occurrence of major adverse cardiovascular events (MACE), including cardiovascular death, recurrent myocardial infarction, and heart failure hospitalization. The central hypothesis is that comprehensive CMR functional and strain-derived parameters, when analyzed using AI-driven models, offer independent and incremental prognostic value beyond conventional clinical risk factors. This study seeks to establish a data-driven, multimodal imaging framework for personalized risk stratifi