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.
Highlights
High Trajectory Instability
Approximately 82% of patients with community-acquired sepsis changed their clinical subtype within the first 48 hours of hospital presentation, regardless of their initial classification.
Prevalence of Classification Uncertainty
Most patients in the alpha, beta, and gamma subtypes (64-70%) reside in the ‘margin’ stratum, meaning their clinical features do not strongly align with a single core phenotype.
Treatment Response Modulation
Classification uncertainty is not just a statistical nuance; it modified the effect of randomized treatments on 365-day mortality in the ProCESS trial, suggesting that ‘core’ and ‘margin’ patients respond differently to interventions.
Background: The Challenge of Sepsis Heterogeneity
Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Its clinical presentation varies wildly, ranging from mild respiratory distress to refractory septic shock. Previous research identified four phenotypes: alpha (minimal organ dysfunction), beta (older patients with chronic illness and renal dysfunction), gamma (inflammation and pulmonary dysfunction), and delta (liver dysfunction and shock). While these subtypes provided a framework for understanding risk, their temporal stability and the certainty of their assignment had not been rigorously quantified until now.
Study Design and Methodology
The researchers conducted a retrospective analysis of 35,691 adult patients with community-acquired sepsis according to Sepsis-3 criteria. The data were drawn from multiple EHR databases and the ProCESS randomized controlled trial.
Defining Core and Margin Strata
To measure classification uncertainty, the team used model-derived membership probabilities. Patients were assigned to a ‘core’ stratum if their probability of belonging to a specific subtype was 90% or higher. Those with a probability below 90% were placed in the ‘margin’ stratum.
Endpoints
The study focused on two primary outcomes:
1. The change in subtype over the first 48 hours of hospitalization.
2. Risk-adjusted 365-day mortality, specifically examining how subtype and margin/core status affected treatment response in the ProCESS trial data.
Key Findings
The Fluidity of Sepsis Phenotypes
The most striking finding was the lack of subtype stability. Among the 35,691 patients (mean age 68 years; 51% male), 82% transitioned to a different subtype within 48 hours. This suggests that sepsis phenotypes are highly dynamic and influenced by both the underlying disease progression and the clinical interventions provided in the early hours of care.
Classification Uncertainty is the Norm
The majority of patients did not fit perfectly into a ‘core’ subtype.
– Alpha-type: 70% in the margin stratum.
– Beta-type: 66% in the margin stratum.
– Gamma-type: 64% in the margin stratum.
– Delta-type: This was the outlier, with only 18% in the margin stratum, indicating that the delta phenotype (shock and liver dysfunction) is the most distinct and recognizable clinical state.
Predicting Trajectory Shifts
Patients in the margin strata were significantly more likely to change subtypes. For instance, patients in the margin delta subtype had a seven-fold increase in the odds of changing subtypes compared to those in the alpha core subtype (Odds Ratio, 7.13; 95% CI, 5.16-9.85). This high-risk transition group represents a critical window for clinical vigilance.
Impact on Treatment Response
In the ProCESS trial, the effect of randomized treatment on 365-day mortality was modified by the subtype margin strata (interaction p = 0.026). This implies that a patient’s proximity to the ‘core’ of a phenotype determines how they will respond to specific protocols, such as early goal-directed therapy. Ignoring this ‘fuzziness’ in classification could lead to misleading results in clinical trials.
Expert Commentary
This study challenges the notion that sepsis subtypes are static labels. Instead, it suggests that sepsis is a continuum, and patients drift between states. The high percentage of patients in the ‘margin’ suggests that current clustering models, while useful, may oversimplify the biological reality of the disease.
From a biological plausibility standpoint, the relative stability of the delta subtype is noteworthy. The delta phenotype typically involves severe physiological derangement—hypotension, high lactate, and hepatic failure—which may represent a ‘point of no return’ or a more fixed biological state compared to the more metabolic or inflammatory fluctuations seen in the alpha or gamma types.
Limitations
The study is limited by its retrospective nature and reliance on EHR data, which can be subject to recording biases. Furthermore, the 48-hour window, while critical for emergency medicine, does not capture the full recovery or decline phase of sepsis. Future research should integrate multi-omics data (genomics, proteomics) with these clinical ‘fuzzy’ classifications to see if molecular markers provide more stable anchors for subtype assignment.
Conclusion
The findings by Kennedy et al. represent a paradigm shift in how we view sepsis precision medicine. Clinicians must recognize that a patient’s subtype at admission is a moving target. The ‘fuzzy’ classification approach provides a more honest representation of clinical uncertainty and offers a new metric—margin vs. core status—to identify patients at higher risk for clinical instability. Moving forward, clinical trials must account for these dynamic trajectories to truly tailor treatments to the individual patient.
Funding and References
This research was supported by the National Institutes of Health and the National Institute of General Medical Sciences (R35GM119519).
References
1. Kennedy JN, Iyer S, Nauka PC, et al. Fuzzy classification of sepsis subtypes and implications for trajectory and treatment. EBioMedicine. 2026;124:106125. doi:10.1016/j.ebiom.2026.106125.
2. Seymour CW, Gesten F, Prescott HC, et al. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. N Engl J Med. 2017;376(23):2235-2244.
3. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810.
