Prognostic Implications of Machine Learning Algorithm–Supported Diagnostic Classification of Myocardial Injury Using the Fourth Universal Definition of Myocardial Infarction
The substantial and persistent rate of late cardiac events highlights the need to establish an evidence base for the therapeutic management of "non-T1MI" diagnostic classifications and suggests opportunity to improve late outcomes using existing and emerging therapies.
Authors
- Ehsan Khan (ORCID: https://orcid.org/0000-0002-4544-4559)
- Johan Verjans (ORCID: https://orcid.org/0000-0002-8336-6774)
- Derek P. Chew (ORCID: https://orcid.org/0000-0003-3593-296X)
- Kristina Lambrakis (ORCID: https://orcid.org/0000-0002-6221-0244)
- Shaun G. Goodman (ORCID: https://orcid.org/0000-0001-8068-2440)
- Adam J. Nelson (ORCID: https://orcid.org/0000-0003-0990-2548)
- Louise Cullen (ORCID: https://orcid.org/0000-0001-6611-8229)
- Joey Gerlach
- Tom Briffa
- Zhibin Liao (ORCID: https://orcid.org/0000-0001-9965-4511)
Institutions
- St. Michael's Hospital (CA)
- Flinders University (AU)
- The University of Western Australia (AU)
- Royal Brisbane and Women's Hospital (AU)
- Australian Institute of Business (AU)
- Canadian VIGOUR Centre (CA)
- SA Health (AU)
- Monash Health (AU)
- South Australian Health and Medical Research Institute (AU)
- Monash University (AU)
- The University of Adelaide (AU)
Publication Details
- Journal
- Heart Lung and Circulation
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1016/j.hlc.2024.11.023
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00