Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to an infection [1]. The global burden of sepsis is estimated to be 49 million cases annually [2] of which half require intensive care unit (ICU) treatment [3]. Sepsis accounts for more than 11 million deaths [4], and significant long-term morbidity amongst survivors [5]. Despite progress in the understanding of sepsis pathophysiology, no specific treatment has proven successful [6, 7]. Amid calls for precision therapy, a greater understanding of the heterogeneity of sepsis is needed and a focus of clinicians, trialists, and funders [6, 8‐10].

Recent approaches to measure sepsis heterogeneity used unsupervised computational methods on clinical, biomarker or gene expression data from observational studies or clinical trial datasets. Examples include subtypes from the Sepsis Endotyping in Emergency Care (SENECA) study [11], acute respiratory distress syndrome (ARDS) subphenotypes from the ARDSnet randomized trials [12, 13], and Molecular Diagnosis and Risk Stratification of Sepsis (MARS) and sepsis response signatures (SRS) endotypes based on transcriptomic data of whole blood leucocytes [14, 15]. At present, more than 100 sepsis subtypes are proposed, without awareness of overlap (or clinical implications), partly caused by the lack of data in a single cohort to compute each of the subtypes [16]. It is unknown whether each new subtype strategy is of added value for the patient or merely ‘reinventing the wheel’, by being comparable to already existing subtypes. This is essential information to inform predictive enrichment in future trials [10].

To address this knowledge gap, we sought to determine the concordance (i.e., agreement) between different subtype labels, outcomes, and biologic pathways of critically ill sepsis patients classified by previously proposed sepsis subtyping methods. We hypothesized that subtype strategies derived from different data sources signify unrelated subtypes, due to the complex nature of sepsis and fundamental differences between clinical, biomarker, and transcriptomic data. However, we also hypothesized that partial overlap will be present in the subtypes with most unfavorable outcome, mainly driven by disease severity. Moreover, we hypothesized that concordance was high when subtypes were based on the same data source.

We used a prior systematic review [16] to identify candidate subtype labels for critically ill sepsis patients (supplemental Table E1). We chose four subtype strategies shown to be reproducible, associated with differential treatment effects in prior work [11, 17‐20], and reflective of multiple domains. These are a mainly clinical based domain [11], a combination of clinical and biological domains [21] and a transcriptomic domain [14, 15, 20]. To assign subtypes, we analyzed a previously selected cohort from the MARS study making use of clinical data, host response biomarkers, and transcriptomic data from whole blood leukocytes, all collected within 24 h after admission [14]. For each patient, a subtype label was applied for each of the four strategies. See online supplementary methods for information on host response biomarker measurements and blood leukocyte gene expression analysis.

In this secondary analysis of the MARS prospective cohort study, we describe a comparison of sepsis subtypes, based on clinical, biomarker or transcriptomic data in critically ill patients with sepsis. Subtypes could be assigned successfully, but the distribution of the SENECA subtypes and mortality in SRS subtypes was notably different from the original cohorts. Concordance between SENECA, ARDS, MARS and SRS subtypes was moderate to low, concluding that a different set of patients was identified in each subtype strategy. Host response biomarkers and transcriptomic data showed the most similarity between Mars2 and SRS1. When patients were assigned multiple subtypes, this created subgroups of patient with a broad variability in characteristics and in-hospital mortality.

Compared to prior reports, SENECA subtypes of sepsis were similar in their clinical characteristics [11]. However, the distribution of the SENECA subtypes in this critically ill population was different from those reported in the original Emergency Department (ED) population. For example, the rates of α and β were < 10%, far lower than 20–30% in broader study cohorts. Even though the distribution of SENECA subtypes was dependent on case mix differences, the clinical characteristics of the individual SENECA subtype members were consistent with prior work [11]. Notably, we found no clear relationship between the distribution of SENECA subtypes nor clinical characteristics with any of the transcriptomic subtypes. For example, we did not observe concordance between δ patients and Mars1, even though both subtypes tend toward higher organ dysfunction and greater mortality, which was not in line with our hypothesis. However, host response biomarker aberrations were most similar between γ-type and Mars4, and to a lesser extent between δ-type and Mars2. This could be driven in part by disease severity, as δ-type and Mars2 had the highest SOFA score.

The most overlap in the distribution of sepsis subtypes was between subtype strategies derived from the same data source (MARS and SRS subtypes). In addition, there was also a moderate association between ARDS subtypes and all other subtypes. In the absence of concordance between clinical and molecular classifiers, it may be that biomarkers form a key link between clinical and transcriptomic approaches to subtyping sepsis. Furthermore, biologic subtypes also show a difference in gene expression profiles and prior work has shown that SENECA subtypes are modifiable when new biomarker data is added [27, 28]. Although there is no ground truth to subtype labels or gold standard to compare, the inclusion of biologic features such as plasma protein biomarkers may be feasible, and unveil subtypes suitable for evaluation in clinical trials [29].

These findings suggest that multiple subtype strategies reflect different, perhaps complimentary pieces to the subtyping story. Similar work in traumatic injury and oncology reveals that combinations of clinical, biomarker, and transcriptomic subtypes will reveal important ensembles, however small, that are prognostic or predictive of treatment response and have added value compared to a single subtype [30, 31]. As a proposed solution to investigate the complimentary nature of the subtypes, we explored sepsis subtype ensembles as combinations. In our exploratory analysis and the examples given, we show that combining subtypes seem to give a more precise estimate of the outcome compared to a single subtype. Nevertheless, there are many challenges with this approach. First, this stratification approach requires large sample sizes. Dividing the homogeneous subgroups into more homogeneous subgroups will quickly lead to loss of power and low-grade evidence if the sample size is not adjusted. Second, feasibility of acquiring the subtypes is important. Considering a bed-side approach, it could be more realistic to identify SENECA subtypes over ARDS or MARS and SRS subtypes, regarding the time and effort that it costs. However, routinely available clinical variables only indirectly reflect the underlying injury process and biomarker data is essential in identifying subtypes with heterogeneity of treatment effect [32]. Third, as data layers increase, so does the need for dimension reduction, to encounter overlap, duplicity and overfitting. In this study, the γ-type is, amongst others, characterized by high clinical inflammatory markers such as CRP and an upregulated immune system in the transcriptomic data; however, host response inflammatory biomarkers such as IL-6 and IL-8 are lower compared to the δ-type. This indicates a mismatch in the data relationship, creating a challenge in the integration of omics and non-omics data. In the long run, the ultimate goal should be the same for every method: to stratify patients into more homogenous subsets with an identifiable and verifiable treatable trait [33]. Taken together, the ensembles explored here demonstrate feasibility of existing subtype labels to jointly identify unique patients. It is unknown though how these subsets will identify treatable traits.

This study has several limitations. First, SENECA subtypes were applied using a reduced feature set compared to the original work [11]. This limitation is previously shown to have modest, if any, impact on classification and subtype characteristics. Second, the prospective enrollment of the MARS population occurred prior to updated clinical practice guidelines and this analysis is performed in a single cohort. This could limit generalizability. Third, not all published sepsis subtypes were feasible to include in this analysis. We selected four highly cited approaches that reflected different features, analytic methods, and were potentially linked to treatment. Fourth, this result may not apply to low-income and middle-income countries or patients cared for in non-ICU settings.

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.