Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic Data
Machine learning, newly integrating echocardiographic data, identified 5 distinct CS phenotypes, each with unique clinical/echocardiographic features and mortality risks. These insights could support personalized treatment strategies in CS patients, pending further validation.
Authors
- Arianna Francesconi
- Géza Hálasz (ORCID: https://orcid.org/0000-0001-7220-123X)
- Paolo Soda (ORCID: https://orcid.org/0000-0003-2621-072X)
- Serafina Valente (ORCID: https://orcid.org/0000-0002-0808-3512)
- Andrea Stefanini (ORCID: https://orcid.org/0000-0002-3073-2865)
- Carlotta Sorini Dini (ORCID: https://orcid.org/0000-0003-0080-5434)
- Nicolò Ghionzoli (ORCID: https://orcid.org/0000-0002-5629-2175)
- Maria Concetta Pastore (ORCID: https://orcid.org/0000-0002-7223-141X)
- Domenico Gabrielli (ORCID: https://orcid.org/0000-0003-3392-0527)
- Carlotta Sciaccaluga (ORCID: https://orcid.org/0000-0002-1965-1773)
- Francesca Maria Righini (ORCID: https://orcid.org/0000-0003-4462-0853)
- Marco Marini (ORCID: https://orcid.org/0000-0003-2165-556X)
- Hatem Soliman-Aboumarie (ORCID: https://orcid.org/0000-0002-3254-6820)
- Valerio Guarrasi (ORCID: https://orcid.org/0000-0002-1860-7447)
- V Di Piazza
- Matteo Cameli (ORCID: https://orcid.org/0000-0003-3872-8964)
- Elvira Delcuratolo
Institutions
- University of Siena (IT)
- Università Campus Bio-Medico (IT)
- Harefield Hospital (GB)
- Ospedali Riuniti di Ancona (IT)
- IRCCS San Camillo Hospital (IT)
- Nini Hospital (LB)
- Carlo Forlanini Hospital (IT)
- Umeå University (SE)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102257
- Primary Topic
- Mechanical Circulatory Support Devices
- Type
- article
- Field-Weighted Citation Impact
- 0.00