ArticleFrontiers in cardiovascular medicine2026
Cardiac MRI-guided therapeutics in ischemic cardiomyopathy: impact on heart-team consensus and outcomes.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aims: Our study aims to evaluate the influence of myocardial viability on therapeutic decision-making using cardiac MRI (CMR) and assess agreement and variability in heart-team therapeutic recommendations across different heart teams. Methodology: We included patients who underwent coronary angiography and CMR between January 2022 and January 2024 at a tertiary cardiac center. The revascularization strategy was independently determined by heart teams from four different centers, both before and after a review of CMR. The impact of CMR on therapeutic decisions was evaluated with a follow-up for long-term outcomes, while decision concordance before and after CMR was assessed using Cohen's kappa statistics. We calculated a novel residual viable syntax score by incorporating the myocardial viability into the residual syntax score. Results: Sixty-four patients were recruited. Indications for coronary angiography included ST elevation myocardial infarction (23, 36%), NSTEMI (15, 23.4%), unstable angina (7, 10.9%), and chronic coronary syndrome with limiting symptoms (19, 29.7%). Fifty-five (86%) patients had moderate to severe LV dysfunction. A proportion of 32.8% of the patients underwent revascularization in the study population in the form of coronary artery bypass grafting or percutaneous coronary intervention, while the rest were managed medically. Incorporation of CMR findings improved the consistency of treatment decisions across heart teams (Δ Conclusion: CMR influenced heart-team revascularization recommendations and was associated with greater agreement in therapeutic decisions. These findings, including exploratory clinical-outcome analyses, require confirmation in larger studies that use cluster-aware statistical methods.
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