Phenotyping Cardiogenic Shock: An Insight from the Gulf Cardiogenic Shock Registry

Ahmed Elmahrouk^1,2*Amin Daoulah¹Ahmed Jamjoom¹Nooraldaem Yousif³Wael Almahmeed⁴Prashanth Panduranga⁵Abdulrahman Arabi⁶Omar Kanbr⁷Hatem M. Aloui⁸Mohammed Alshehri⁹Badr Alzahrani¹⁰Shaber Seraj¹¹Adnan Hussien¹²Waleed Alharbi¹³Mohammed A. Qutub¹⁴Mokhtar Kahin¹⁵Abdullah Alenezi¹⁶Mohamed Ajaz Ghani¹⁷Taher Hassan¹⁸Rajesh Rajan¹⁹Said Al Maashani²⁰Abdulwali Abohasan²¹Mohammed Balghith²²Ziad Dahdouh²³Abdulrahman M. Alqahtani²⁴Ibrahim A.M Abdulhabeeb²⁵Mohammed Al Jarallah¹⁹Mubarak Abdulhadi Aldossari⁸Harvey Anthony²⁶Mohammed Awad Ashour Awad Ashour⁶Tarique Shahzad Chachar³Hassan Khan⁴Abeer M. Shawky^27,28Youssef Elmahrouk²Amir Lotfi²⁹Amr Arafat^10,2,30

• ¹King Faisal Specialist Hospital & Research Centre - Jeddah, Jeddah, Saudi Arabia
• ²Tanta University Faculty of Medicine, Tanta, Egypt
• ³Mohammed bin Khalifa bin Salman Al Khalifa Specialist Cardiac Centre, Awali, Bahrain
• ⁴Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
• ⁵National Heart Center, Royal Hospital, Muscat, Oman
• ⁶Hamad Medical Corporation, Doha, Qatar
• ⁷Elrazi University, Khartoum, Sudan
• ⁸King Saud Medical City, Riyadh, Saudi Arabia
• ⁹Prince Khaled Bin Sultan Cardiac Center, Khamis Mushait, Saudi Arabia
• ¹⁰Prince Sultan Cardiac Center, Riyadh, Saudi Arabia
• ¹¹University of Massachusetts Chan Medical School TH Chan School of Medicine, Worcester, United States
• ¹²King's College Hospital London, Jeddah, Jeddah, Saudi Arabia
• ¹³King Saud University, Riyadh, Saudi Arabia
• ¹⁴King Abdulaziz University, Jeddah, Saudi Arabia
• ¹⁵International Medical Center, Jeddah, Saudi Arabia
• ¹⁶Chest Diseases Hospital, Kuwait City, Kuwait
• ¹⁷17Department of Cardiology, Madinah Cardiac Center, Almadinah, Saudi Arabia
• ¹⁸Department of Cardiology, Bugshan General Hospital, Jeddah, Saudi Arabia
• ¹⁹Al-Amiri Hospital, Kuwait City, Kuwait
• ²⁰Salalah Heart Center, Sultan Qaboos Hospital, Salalah, Oman
• ²¹Central Hospital Hafr Albatin, Hafr Albatin, Saudi Arabia
• ²²King Saud bin Abdulaziz University for Health Sciences College of Public Health and Health Informatics, Riyadh, Saudi Arabia
• ²³King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
• ²⁴King Salman Heart Center, King Fahad Medical City, RIYADH, Saudi Arabia
• ²⁵King Abdulaziz Specialist Hospit, Aljawf, Saudi Arabia
• ²⁶The Royal Hospital National Heart Center, Muscat, Oman
• ²⁷Faculty of Medicine, Al-Azhar University, CAIRO, Egypt
• ²⁸Dr Erfan and Bagedo General Hospital, Jeddah, Saudi Arabia
• ²⁹University of Massachusetts Chan Medical School, Worcester, United States
• ³⁰Health Research Center, Ministry of Defense Health Services, RIYADH, Saudi Arabia

Background: Cardiogenic 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 characterize their associated mortality and SCAI stages. Methods: We conducted a retrospective analysis of 1,513 patients with CS from the Gulf registry. An unsupervised machine learning methodology was employed, using agglomerative hierarchical clustering on seven key continuous variables (Age, Ejection Fraction, Mean Arterial Pressure, Lactate, pH, Creatinine, and Alanine Transaminase) to identify patient subgroups. The optimal number of clusters was determined using a combination of quantitative metrics and clinical interpretability. The identified phenotypes were then validated against external outcomes, including in-hospital mortality and SCAI Shock Stage. Results: Four distinct clinical phenotypes were identified. Phenotype 1 ("Compensated Low-Risk," n=492, 32.5%) had the lowest mortality rate (22.4%). Phenotype 2 ("Metabolic Dysfunction," n=418, 27.6%) was characterized by severe left ventricular dysfunction and had a mortality of 41.9%. Phenotype 3 ("Multi-organ Failure," n=204, 13.5%) presented with severe metabolic, renal, and hepatic derangement and had the highest mortality (78.4%). Phenotype 4 ("Elderly Decompensated," n=399, 26.4%) included older patients with moderate metabolic dysfunction and had a mortality of 60.7%. A steep mortality gradient was observed across the phenotypes (p<0.001), and the distribution of SCAI shock stages differed significantly, aligning with the risk profile of each cluster. Conclusion: 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.