Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A...

Background: Septic shock is a heterogeneous syndrome with diverse clinical presentations and pathophysiology, yet current management guidelines largely treat it as a homogenous entity. Early risk stratification relies on lactate and different predictive scoring systems, which may not capture the underlying heterogeneity in host responses. Aim: To identify discrete subphenotypes of septic shock using admission demographics and laboratory parameters, and to evaluate their relationship with in-hospital outcomes. Methods: We conducted a retrospective multicenter cohort study of 10,462 adult patients with ICD-10-defined septic shock admitted to intensive care units between 2014 and 2015. We used Two-Step Cluster Analysis using log-likelihood distance and the Bayesian Information Criterion to identify two distinct phenotypes. We compared clusters on baseline characteristics, in-hospital outcomes including mortality, days on mechanical ventilation, vasopressor use, acute kidney injury (AKI), AKI requiring renal replacement therapy (RRT), and ICU and hospital lengths of stay. Results: We identified two clusters (Cluster 1, n = 5355 and Cluster 2, n = 5107) in our study. Cluster 1 showed greater biochemical severity at presentation, including higher median lactate (2.40 vs. 2.20 mmol L^-1; p < 0.001), serum creatinine (1.39 vs. 1.20 mg dL^-1; p < 0.001), blood urea nitrogen (28 vs. 25 mg dL^-1; p < 0.001), and neutrophil-to-lymphocyte ratio (11.12 vs. 10.38; p < 0.001), and a higher mean SOFA score (7.05 ± 3.85 vs. 6.76 ± 3.87; p < 0.001). Despite this, Cluster 1 required mechanical ventilation more frequently (46.1% vs. 42.2%; p < 0.001) and had a higher incidence of AKI (58.1% vs. 55.6%; p = 0.009), including more stage 3 AKI (17.2% vs. 15.2%; p < 0.001) and dialysis (6.6% vs. 5.2%; p = 0.005), yet experienced similar in-hospital mortality (15.4% vs. 15.8%; p = 0.615) and comparable ICU (2.18 vs. 2.26 days; p = 0.254) and hospital lengths of stay (6.63 vs. 6.80 days; p = 0.251). Conclusions: Two septic shock phenotypes were identified, one with marked early organ dysfunction (Cluster 1) and another with milder initial derangements (Cluster 2), yet both showed convergent short-term mortality and lengths of stay despite divergent support needs. These results challenge reliance on single-parameter severity markers and underscore the need for phenotype-guided risk stratification and personalized management strategies in septic shock.