Predictors of Mortality by an Artificial Intelligence Enhanced Electrocardiogram Model for Cardiac Amyloidosis
Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.
Authors
- Angela A. Dispenzieri (ORCID: https://orcid.org/0000-0001-8780-9512)
- Daniel D. Borgeson
- Dennis H. Murphree (ORCID: https://orcid.org/0000-0002-7800-455X)
- Paul A. Friedman (ORCID: https://orcid.org/0000-0001-5052-2948)
- Jennifer M. Amadio (ORCID: https://orcid.org/0000-0002-6826-5596)
- Suraj Kapa (ORCID: https://orcid.org/0000-0003-2283-4340)
- Francisco Lopez‐Jimenez
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Grace Lin (ORCID: https://orcid.org/0000-0002-8087-1150)
- Surendra Dasari (ORCID: https://orcid.org/0000-0002-7972-3556)
- Martha Grogan
- Eli Muchtar (ORCID: https://orcid.org/0000-0003-2210-2174)
- Omar F. AbouEzzeddine (ORCID: https://orcid.org/0000-0003-3256-3660)
- Morie A. Gertz
Institutions
- Mayo Clinic (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- ESC Heart Failure
- Published
- 2024-08-31
- DOI
- https://doi.org/10.1002/ehf2.15061
- Citations
- 7
- Primary Topic
- Amyloidosis: Diagnosis, Treatment, Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.64
Funders
- Henry J. Predolin Foundation for Research in Leukemia
- Jabbs Foundation
- National Cancer Institute