Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes

Abstract

Purpose:The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.Methods: This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper-and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.Results: All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. Conclusion:Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

“…At best, we found only a moderate relationship between SRS gene-expression endotypes and plasma cytokine clusters, which reflects the findings from a recently published study 48 that also found limited overlap between clinical, inflammatory and gene-expression phenotypes. This study found a similar relationship between ARDS inflammatory phenotypes and SRS as we found between our sepsis cytokine clusters and SRS, likely reflecting different underlying mechanisms and processes contributing to the observed patient clusters.…”

“…46 47 However, lack of heterogeneity of treatment effect in this study does not preclude plasma cytokine-based subphenotyping providing predictive enrichment for other treatments highlights the importance of collecting biological samples alongside clinical trials to investigate these relationships. At best, we found only a moderate relationship between SRS gene-expression endotypes and plasma cytokine clusters, which reflects the findings from a recently published study 48 that also found limited overlap between clinical, inflammatory and geneexpression phenotypes. This study found a similar relationship between ARDS inflammatory phenotypes and SRS as we found between our sepsis cytokine clusters and SRS, likely reflecting different underlying mechanisms and processes contributing to the observed patient clusters.…”

Biological basis of critical illness subclasses: from the bedside to the bench and back again

“…4 a, activation (green) and inactivation (red) of intra-cellular signaling pathways identified through gene expression studies are shown according to critical illness syndrome and the subclassification scheme used; evidently some approaches are more congruent than others. In a recent study van Amstel et al compared class outputs of several critical illness subtyping schemes within the same set of patients and identified relatively low to moderate overlap between clinical, biomarker, and transcriptomic data-based approaches 59 . Furthermore, several studies have identified that integrated subclassification approaches, for example those that combine biomarker and transcriptomic approaches, may be more informative than using a single approach alone [59 – 61 .…”

“…In a recent study van Amstel et al compared class outputs of several critical illness subtyping schemes within the same set of patients and identified relatively low to moderate overlap between clinical, biomarker, and transcriptomic data-based approaches 59 . Furthermore, several studies have identified that integrated subclassification approaches, for example those that combine biomarker and transcriptomic approaches, may be more informative than using a single approach alone [59 – 61 . Moreover, it is established that nearly half of the patients switch between gene-expression endotypes assigned on day 1 by day 3 among adults 62 and children 63 .…”

Identification and validation of sepsis subphenotypes using time-series data

“…A summary of previous studies on sepsis subtypes is presented in Supplementary Material: Table S1 . Considering the complex and rapidly evolving characteristics of sepsis, cluster analysis based on multi-systematic and comprehensive time-series data of easily accessible clinical indicators is more consistent with the characteristics of sepsis, providing better classification [ 17 , 18 ].…”