# Cardiac Magnetic Resonance Tissue Characterization of Ischemic and Non-Ischemic Myocardium to Predict Left Ventricular Functional Recovery and Outcomes after Multivessel Coronary Revascularization > **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $739,298 ## Abstract PROJECT SUMMARY / ABSTRACT Ischemic left ventricular systolic dysfunction (iLVSD) is a leading cause of heart failure (HF) and death, for which risk is greatest in patients with multivessel coronary artery disease (CAD). Coronary revascularization (revasc) provides a potential means to improve iLVSD, but response is highly variable. “Viability” imaging (to differentiate infarcted from salvageable myocardium) had been widely touted as a tool to predict revasc response, but its utility has been challenged by recent clinical trials. One reason for observed lack of utility of imaging to predict LV functional recovery after revasc may stem from imaging approaches used to define viability: No prior trial has accounted for contractile dysfunction, hypoperfusion, or non-ischemic substrate in seemingly viable regions – each of which can be uniquely discerned by cardiac MRI (CMR). Our group has conducted single center studies showing transmural extent of infarction to be a powerful predictor of improved LV function after revasc; we have also shown hypoperfusion to predict adverse remodeling and prognosis. Technical research by members of our group has developed a new late gadolinium enhancement (LGE) CMR approach that uses blood suppression (“dark-blood”) to better discriminate between infarct and blood-pool – potentially further enhancing viability as- sessment. It is unknown if differential impact of revasc strategy on LV contractile recovery varies in relation to infarct or perfusion phenotype, if non-ischemic substrate (extracellular volume fraction, epicardial or mid-wall LGE) modifies revasc response, and if LV contractile recovery parallels improved prognosis. To address these critical knowledge gaps, this observational study will leverage the newly initiated STICH3C trial - a prospective multicenter trial comparing percutaneous (PCI) vs. surgical (CABG) revasc for patients with iLVSD and mul- tivessel/left main CAD. Perfusion CMR will be performed pre- (<1 month) and post-revasc (12 months) in 200 STICH3C patients and analyzed via a centralized core lab. Our central hypothesis is that infarct transmurality (LGE) and hypoperfusion will predict LV contractile response (EF) and prognosis (QOL, HF, mortality) after re- vasc, for which treatment effect of CABG vs. PCI on LV recovery will increase in proportion to viable but hy- poperfused myocardium on CMR. Aim 1 will test infarct transmurality and hypoperfusion for prediction of LV recovery (improved ejection fraction, strain) after revasc; Aim 2 will evaluate if non-ischemic tissue substrate modifies likelihood of LV contractile recovery; Aim 3 will test prognostic impact of infarction, hypoperfusion, and non-ischemic substrate after revasc, with focus on residual myocardial tissue properties as predictors of persis- tent LV dysfunction, impaired quality of life, and clinical events (HF readmission, mortality). Our team provides complementary expertise in key project relevant areas - translational CMR... ## Key facts - **NIH application ID:** 10917421 - **Project number:** 5R01HL170566-02 - **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV - **Principal Investigator:** Mario FL Gaudino - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $739,298 - **Award type:** 5 - **Project period:** 2023-09-01 → 2028-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10917421 ## Citation > US National Institutes of Health, RePORTER application 10917421, Cardiac Magnetic Resonance Tissue Characterization of Ischemic and Non-Ischemic Myocardium to Predict Left Ventricular Functional Recovery and Outcomes after Multivessel Coronary Revascularization (5R01HL170566-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10917421. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*