Phenotyping cardiogenic shock: an insight from the gulf cardiogenic shock registry
In a large, contemporary registry of CS patients, an unsupervised machine learning approach successfully identified four distinct and prognostically significant phenotypes. These data-driven phenotypes, characterized by unique clinical and biomarker profiles, provide a novel framework for risk stratification that moves beyond traditional classification systems and may facilitate the development of personalized therapeutic strategies for cardiogenic shock.
Authors
- Mohammed A. Balghith
- Ahmed Abdullah Jamjoom (ORCID: https://orcid.org/0000-0002-7232-9580)
- Mohamed Ajaz Ghani (ORCID: https://orcid.org/0000-0001-8929-7901)
- Adnan Fathey Hussien (ORCID: https://orcid.org/0009-0009-4949-4682)
- Youssef Elmahrouk (ORCID: https://orcid.org/0000-0002-7756-7537)
- Prashanth Panduranga (ORCID: https://orcid.org/0000-0002-0603-117X)
- Amin Daoulah (ORCID: https://orcid.org/0000-0001-7269-1195)
- Hassan Khan (ORCID: https://orcid.org/0000-0001-9259-5432)
- Ziad Dahdouh
- Abeer M Shawky (ORCID: https://orcid.org/0000-0001-8246-2468)
- Tarique Shahzad Chachar (ORCID: https://orcid.org/0000-0002-5293-5241)
- Badr Mujeb Alzahrani
- Sultan Mohammed Al Jarallah
- Nooraldaem Yousif (ORCID: https://orcid.org/0000-0001-9760-1600)
- Mubarak Aldossari (ORCID: https://orcid.org/0000-0003-2892-1605)
- Abdulwali Abohasan
- Waleed Alharbi (ORCID: https://orcid.org/0000-0002-6170-1076)
- Omar Kanbr (ORCID: https://orcid.org/0009-0003-8307-1014)
- Shaber Seraj (ORCID: https://orcid.org/0000-0002-3836-2897)
- Mokhtar Abdirahman Kahin (ORCID: https://orcid.org/0009-0001-3588-7145)
- AbdulRahman Arabi
- Mohammed A. Qutub
- Abdullah Al‐Enezi (ORCID: https://orcid.org/0009-0002-8481-2120)
- Taher Hassan (ORCID: https://orcid.org/0000-0001-5188-1002)
- Hatem Aloui (ORCID: https://orcid.org/0000-0001-9901-8590)
- Wael Almahmeed
- Ahmed F. Elmahrouk
- Rajesh Rajan
- Ibrahim A. M. Abdulhabeeb
- Harvey Anthony
- Amir Lotfi
- Mohammed Awad Ashour Awad Ashour
- Abdulrahman M. Alqahtani
- Said Al Maashani
- Mohammed Alshehri
- Amr Arafat
Institutions
- Ministry of Defence (GB)
- Cleveland Clinic (US)
- Ministry of National Defense (KR)
- Prince Sultan University (SA)
- University of Massachusetts Chan Medical School (US)
- King Abdulaziz University (SA)
- King Saud bin Abdulaziz University for Health Sciences (SA)
- Tanta University (EG)
- King Faisal Specialist Hospital & Research Centre (SA)
- King Saud Medical City (SA)
- Sultan Qaboos Hospital (OM)
- King Fahd Medical City (SA)
- King Saud University (SA)
- Amiri Hospital (KW)
- Bugshan Hospital (SA)
- Chest Diseases Hospital (KW)
- National Heart Hospital (BG)
- General Hospital of Drama (GR)
- Christian Health Association of Nigeria (NG)
- Qatif Central Hospital (SA)
- Al-Azhar University (ID)
- Saitama International Medical Center (JP)
- King Abdulaziz Hospital (SA)
- Hamad bin Khalifa University (QA)
- San Diego Cardiac Center (US)
- Islamic University of Madinah (SA)
- King's College Hospital (GB)
- Elrazi University
- Hamad Medical Corporation (QA)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-03-03
- DOI
- https://doi.org/10.3389/frai.2026.1744896
- Primary Topic
- Mechanical Circulatory Support Devices
- Type
- article
- Field-Weighted Citation Impact
- 0.00