Phenotyping Cardiogenic Shock.

Area of Science:

• Cardiology
• Machine Learning in Medicine
• Clinical Phenotyping

Background:

• Cardiogenic shock (CS) is a complex syndrome with variable patient presentations and outcomes.
• Existing approaches lack precise patient stratification for effective management.
• Distinct patient phenotypes within CS may influence prognosis and treatment response.

Purpose of the Study:

• To apply machine learning to identify and validate distinct patient phenotypes in cardiogenic shock.
• To test the hypothesis that these phenotypes correlate with specific clinical characteristics and in-hospital mortality.
• To explore the potential for phenotype-guided clinical trial enrollment and treatment strategies.

Main Methods:

• Analysis of data from 1959 patients with CS across two international registries (CSWG and DRR).
• Utilized consensus k-means clustering algorithm to identify patient clusters in the CSWG-MI cohort.
• Validated identified clusters in independent CSWG-HF and DRR cohorts, categorizing by SCAI staging.

Main Results:

• Machine learning identified three reproducible CS phenotypes: non-congested (I), cardiorenal (II), and cardiometabolic (III).
• In-hospital mortality varied significantly across phenotypes (e.g., 10-28% for cluster I, 32-45% for cluster II, 52-56% for cluster III).
• The cardiometabolic shock cluster exhibited the highest risk for advanced shock stages (D/E) and mortality, irrespective of etiology.

Conclusions:

• Machine learning successfully identified and validated three distinct, reproducible cardiogenic shock phenotypes.
• These phenotypes are associated with specific clinical profiles and mortality risks.
• The identified phenotypes offer a basis for targeted clinical trial design and personalized therapeutic approaches in CS.