Introduction: Targeted therapies for sepsis have failed to show benefit due to high variability among subjects. We sought to demonstrate different phenotypes of septic shock based solely on clinical features and show that these relate to outcome.
Methods: A retrospective analysis was performed of a 1,023-subject cohort with early septic shock from the ProCESS trial. Twenty-three clinical variables at baseline were analyzed using hierarchical clustering, with consensus clustering used to identify and validate the ideal number of clusters in a derivation cohort of 642 subjects from 20 hospitals. Clusters were visualized using heatmaps over 0, 6, 24, and 72 h. Clinical outcomes were 14-day all-cause mortality and organ failure pattern. Cluster robustness was confirmed in a validation cohort of 381 subjects from 11 hospitals.
Results: Five phenotypes were identified, each with unique organ failure patterns that persisted in time. By enrollment criteria, all patients had shock. The two high-risk phenotypes were characterized by distinct multi-organ failure patterns and cytokine signatures, with the highest mortality group characterized most notably by liver dysfunction and coagulopathy while the other group exhibited primarily respiratory failure, neurologic dysfunction, and renal dysfunction. The moderate risk phenotype was that of respiratory failure, while low-risk phenotypes did not have a high degree of additional organ failure.
Conclusions: Sepsis phenotypes with distinct biochemical abnormalities may be identified by clinical characteristics alone and likely provide an opportunity for early clinical actionability and prognosis.
Keywords: hierarchical clustering; machine learning; phenotypes; sepsis; septic shock.
Copyright © 2022 Aldewereld, Zhang, Urbano, Parker, Swigon, Banerjee, Gómez and Clermont.