Explainable Machine Learning Identifies Red Blood Cell Distribution Width as a Key Prognostic Biomarker in Cardiogenic Shock

Cardiogenic shock (CS) is a life-threatening syndrome characterized by severe pump failure and systemic hypoperfusion, with persistently high mortality. Red blood cell distribution width (RDW) has been associated with adverse outcomes in several cardiovascular conditions, but its prognostic role in CS, particularly for long-term mortality and machine learning-based risk stratification, remains incompletely defined. We aimed to investigate the association between RDW and mortality in patients with CS and to evaluate its value in interpretable machine learning models for 1-year mortality prediction.