Phenotyping cardiogenic shock: an insight from the gulf cardiogenic shock registry

BackgroundCardiogenic shock (CS) is a life-threatening condition characterized by clinical heterogeneity and high mortality. A “one-size-fits-all” approach to management may be suboptimal. We aimed to identify distinct clinical phenotypes of CS using an unsupervised machine learning approach and to...

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Principais autores:	Ahmed Elmahrouk, Amin Daoulah, Ahmed Jamjoom, Nooraldaem Yousif, Wael Almahmeed, Prashanth Panduranga, Abdulrahman Arabi, Omar Kanbr, Hatem M. Aloui, Mohammed Alshehri, Badr Alzahrani, Shaber Seraj, Adnan Hussien, Waleed Alharbi, Mohammed A. Qutub, Mokhtar Kahin, Abdullah Alenezi, Mohamed Ajaz Ghani, Taher Hassan, Rajesh Rajan, Said Al Maashani, Abdulwali Abohasan, Mohammed Balghith, Ziad Dahdouh, Abdulrahman M. Alqahtani, Ibrahim A. M Abdulhabeeb, Mohammed Al Jarallah, Mubarak Abdulhadi Aldossari, Harvey Anthony, Mohammed Awad Ashour Awad Ashour, Tarique Shahzad Chachar, Hassan Khan, Abeer M. Shawky, Youssef Elmahrouk, Amir Lotfi, Amr Arafat
Format:	Artigo
Jezik:	Inglês
Izdano:	Frontiers Media S.A. 2026-03-01
Serija:	Frontiers in Artificial Intelligence
Teme:	cardiogenic shock cluster analysis machine learning multi-organ failure phenotyping prognosis
Online dostop:	https://www.frontiersin.org/articles/10.3389/frai.2026.1744896/full
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