Introduction
Sepsis remains one of the most significant challenges in modern medicine, characterized by high mortality rates and extreme clinical heterogeneity. For years, the medical community has sought to move beyond the one-size-fits-all approach to sepsis management. Recent advances in machine learning have proposed four distinct clinical subtypes—alpha (α), beta (β), gamma (γ), and delta (δ)—based on routinely available electronic health record (EHR) data. However, a critical question remains: are these subtypes stable, or are they merely snapshots of a rapidly evolving physiological state? A landmark study by Kennedy et al., published in EBioMedicine, explores the ‘fuzzy’ nature of these classifications and the profound implications for patient trajectories and precision treatment.
