Diagnostic performance of the Myocardial-Ischaemic-Injury index machine-learning algorithm in patients with an initial indeterminate troponin
Among emergency department patients with an initial indeterminate troponin measure, the MI<sup>3</sup> machine-learning algorithm had high AUC and specificity for 30-day MI.
Authors
- Simon A. Mahler (ORCID: https://orcid.org/0000-0003-2199-4078)
- Anna C. Snavely (ORCID: https://orcid.org/0000-0001-6916-4255)
- C. Chet Miller (ORCID: https://orcid.org/0000-0002-5585-2767)
- Christian J. Hunter (ORCID: https://orcid.org/0000-0003-4744-463X)
- Robert H. Christenson (ORCID: https://orcid.org/0000-0003-2612-9083)
- Laurel Jackson (ORCID: https://orcid.org/0000-0002-0491-9079)
- Jason P. Stopyra (ORCID: https://orcid.org/0000-0001-7457-3969)
- Michael Supples (ORCID: https://orcid.org/0000-0001-5999-4554)
- Nicklaus P. Ashburn (ORCID: https://orcid.org/0000-0002-3466-8813)
Institutions
- University of Maryland, Baltimore (US)
- University of Maryland Medical System (US)
- Abbott (United States) (US)
- Wake Forest University (US)
Publication Details
- Journal
- Open Heart
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1136/openhrt-2025-003767
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Abbott Laboratories