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

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," 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.