About the Study: Transthyretin amyloid cardiomyopathy results from abnormal protein deposits in the heart and can lead to progressive heart failure. As effective therapies become available, clinicians need better ways to identify which patients face the greatest risk of death or heart failure hospitalization.
In this multicenter cohort study of 850 patients with transthyretin amyloid cardiomyopathy, a machine learning–based time-to-event model showed better risk discrimination for all-cause mortality and heart failure hospitalization than the National Amyloidosis Centre and Mayo Clinic staging systems. The model performed well across multiple patient groups and treatment settings, suggesting potential value for more individualized prognosis in contemporary clinical care.
Meeting Information: ESC 2026
This paper will be presented during European Society of Cardiology Congress in Munich, Germany.
Local Embargo Time: 13:45 (1:45 P.M.) CEST.
ESC Presentation: The paper will be featured during the Symposium "How imaging-based digital twins, artificial intelligence, and robotics will guide interventions" on the Digital Health Stage.
Related Content: This paper has an accompanying editorial . Explore JAMA+ AI for editor-curated research, commentary, and multimedia from across JAMA Network.
