Using Cardiac MRI to Predict Outcomes in Patients With STEMI

Summary

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 stratification in STEMI patients, potentially enabling more precise post-infarction management strategies.

No investigational treatment is involved. All imaging and clinical data are collected as part of routine care and analyzed retrospectively for outcome prediction.

Conditions

• Myocardial Infarction (MI)
• ST Segment Elevation Myocardial Infarction (STEMI)
• Magnetic Resonance Imaging (MRI)
• Heart Ventricles
• Artificial Intelligence (AI)
• Prognosis
• Ventricular Dysfunction

Interventions

DIAGNOSTIC_TEST

Cardiac Magnetic Resonance Imaging (CMR)

Cine CMR imaging was performed with steady-state free precession covering short axis continuously from the mitral annulus to the apical level in the 2-, 3- and 4- chamber views using the following parameters: repetition time (TR) = 3.73 ms, echo time (TE) = 1.87 ms, flip angle = 60°, slice thickness 8.0 mm. Cine images of all included patients were acquired prior to contrast administrations. Late gadolinium enhancement images (LGE) images were obtained 10-15 minutes after intravenous injection of gadolinium (0.1 mmol/kg at 3ml/s) at end-diastolic phase on the short axis (TR=6.09 ms; TE= 3.0 ms; flip angle 60°; thickness 8.0 mm) with breath-hold phase-sensitive segmented inversion recovery (PSIR) fast field echo sequence. T2-weighted sequence was performed using turbo spin-echo (TSE)-sequence (TR=1714-2000 ms; TE=8.04 ms; slice thickness 8.0 mm) to estimate myocardial edema. Images were analyzed on freely available validated cardiovascular image analysis software CVI42 (Circle Cardiovas

Sponsors & Collaborators

• Chinese PLA General Hospital

  lead OTHER

Eligibility

Min Age

18 Years

Max Age

80 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2014-01-01

Primary Completion

2025-12-30

Completion

2025-12-30

Countries

• China

Study Locations