Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

Abstract

Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care.

Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs).

Design, settings, and participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737).

Exposures: All clinical and laboratory variables in the electronic health record.

Main outcomes and measures: Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs.

Results: The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm).

Conclusions and relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Seymour reported receiving personal fees from Edwards Inc and Beckman Coulter Inc. Dr Gomez reported receiving grants from TES Pharma. Dr Huang reported receiving nonfinancial support (procalcitonin assays) from Biomerieux and grants from Thermofisher for microbiome research. Dr Vodovotz reported being the cofounder and a stakeholder in Immunetrics Inc and having a provisional patent application pending. Dr Yende reported receiving personal fees from Atox Bio and grants from Bristol-Myers Squibb. Dr Angus reported receiving personal fees from and serving as a consultant to Ferring Pharmaceuticals, Bristol-Myers Squibb, Bayer AG, and Beckman Coulter Inc; owning stock in Alung Technologies; and having patent applications pending for selepressin (compounds, compositions, and methods for treating sepsis) and proteomic biomarkers of sepsis in elderly patients. No other disclosures were reported.

Figures

Figure 1.. Chord Diagrams Showing Abnormal Clinical Variables by Phenotype

In A, the ribbons connect from an individual phenotype to an organ system if the group mean is greater or lesser than the overall mean for the entire cohort. For example, the δ phenotype (light blue) is more likely to have members with abnormal cardiovascular and hepatic dysfunction (ribbons connect with these portions of the circle) vs β phenotype members (light purple) who are more likely to have kidney dysfunction and other abnormal variables (eg, increased age, comorbidity). In B-E, each phenotype is highlighted separately and the ribbons connect to the different patterns of clinical variables and organ system dysfunctions on the top of the circle.

Figure 2.. Comparison of Variables That Contribute to Clinical Phenotypes in the SENECA Derivation Cohort (n = 20 189)

In all panels, the variables are standardized such that all means are scaled to 0 and SDs to 1. A value of 1 for the standardized variable value (x-axis) signifies that the mean value for the phenotype was 1 SD higher than the mean value for both phenotypes shown in the graph as a whole. ALT indicates alanine transaminase; AST, aspartate transaminase; Bands, also known as premature neutrophil count; BUN, blood urea nitrogen; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; GCS, Glasgow Coma Scale; INR, international normalized ratio; Pao2, partial pressure of oxygen; SENECA, Sepsis Endotyping in Emergency Care; SBP, systolic blood pressure; WBC, white blood cell.

Figure 3.. Inflammatory Cytokines Across Phenotypes

Ratio of IL-6 was calculated as the cytokine value standardized by the median value for the α phenotype in each study (referent) illustrated on a log scale. All comparisons within data sets across phenotypes were significant (P < .001). Errors bars indicate the upper bound of the interquartile range of the biomarker standardized by the median value for the α phenotype. Across multiple cohorts and randomized trials, inflammatory cytokines IL-6, IL-10, and TNF measured at baseline were greater in the γ phenotype (pink) and δ phenotype (blue) compared with the α phenotype (green), suggesting a predominantly hyperinflammatory response. TNF indicates tumor necrosis factor.

Figure 4.. Ratio of Additional Biomarkers in Heatmap

Heatmap shows the ratio of the median biomarker value for various markers of the sepsis host response grouped by those reflecting coagulation, endothelium, inflammation, and renal injury. Orange represents a greater median biomarker value for that phenotype compared with the median for the entire study, whereas colors in the tan to brown range represent lower median biomarker values compared with the median for the entire study. Empty cells are those for which the biomarker was not measured. The factor V, factor IX, plasminogen, protein C, and protein S biomarkers were reversed on the scale to coordinate the color map. The IL-1b and IL-12 biomarkers are not shown due to having less than 0.5-fold changes. ACCESS indicates A Controlled Comparison of Eritoran in Severe Sepsis; COL-4, collagen type 4; GenIMS, Genetic and Inflammatory Markers of Sepsis; ICAM, intercellular adhesion molecule 1; IGFBP-7, insulin-like growth factor–binding protein 7; KIM-1, kidney injury molecule 1; PAI-1, plasminogen activator inhibitor 1; ProCESS, Protocol-Based Care for Early Septic Shock; PROWESS, Activated Protein C Worldwide Evaluation in Severe Sepsis; TAT, thrombin-antithrombin; TIMP-2, tissue inhibitor of metalloproteinase 2; TNF, tumor necrosis factor; VCAM, vascular cell adhesion molecule.

Figure 5.. Short-term Mortality by Phenotype

All panels show significant differences in mortality by phenotype (log-rank P < .001). In the SENECA derivation and validation cohorts, in the GenIMS cohort, and in the 3 randomized clinical trials, clinical phenotypes are associated with short-term mortality. This suggests that phenotypes are generalizable and prognostic across data sets with different severity, temporality, and definitions of sepsis and septic shock. ACCESS indicates A Controlled Comparison of Eritoran in Severe Sepsis; GenIMS, Genetic and Inflammatory Markers of Sepsis; ProCESS, Protocol-Based Care for Early Septic Shock; PROWESS, Activated Protein C Worldwide Evaluation in Severe Sepsis; SENECA, Sepsis Endotyping in Emergency Care. aThe cumulative mortality data are only for unique patients in the SENECA derivation cohort (16 652 of 20 189 total patients) and in the SENECA validation cohort (31 160 of 43 086 total patients).

Figure 6.. Sensitivity of Clinical Trial Results to the Relative Frequency of Phenotypes in Monte Carlo Simulation

For each trial (ACCESS, PROWESS, and ProCESS), panel A shows the actual distribution of the 4 phenotypes in that trial (horizontal bar graph) and the observed proportion of trials concluding no difference (neutral), harm, or benefit in simulation (vertical stacked bar graph). Each simulation represents 10 000 iterations using sampling with replacement. Panel B shows how simulated trial results vary when the case mix is changed to the distributions shown in the top set of graphs by varying α (panel B) and δ (panel C). ACCESS indicates A Controlled Comparison of Eritoran in Severe Sepsis; EGDT, early goal-directed therapy; HBN, harm, benefit, or neutral; ProCESS, Protocol-Based Care for Early Septic Shock; PROWESS, Activated Protein C Worldwide Evaluation in Severe Sepsis; SENECA, Sepsis Endotyping in Emergency Care.