Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning‐Derived “PRAISE Score”
The PRAISE score has a comprehensive ability to identify with high specificity those patients at risk for arrhythmic events during hospitalization for ACS.
Authors
- Lídia Aparecida Rossi (ORCID: https://orcid.org/0000-0002-4141-861X)
- Domenico D’Amario (ORCID: https://orcid.org/0000-0003-3774-8330)
- Ailia Giubertoni (ORCID: https://orcid.org/0009-0005-4081-2470)
- Fabrizio D’Ascenzo (ORCID: https://orcid.org/0000-0002-6646-9317)
- Giuseppe Patti (ORCID: https://orcid.org/0000-0002-5404-3968)
- Leonardo Grisafi (ORCID: https://orcid.org/0000-0001-5277-9907)
- Gaetano Maria De Ferrari
- Luca Cumitini
- Paola Abbiati
Institutions
- Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT)
- Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
- Azienda Ospedaliera Ospedale Maggiore (IT)
- University of Turin (IT)
Publication Details
- Journal
- Clinical Cardiology
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/clc.70035
- Citations
- 2
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 0.80