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

Educational Objective
To understand that clinical phenotypes of the sepsis syndrome may be associated with different responses to therapies.
1 Credit CME
Key Points

Question  Are clinical sepsis phenotypes identifiable at hospital presentation correlated with the biomarkers of host response and clinical outcomes and relevant for understanding the heterogeneity of treatment effects?

Findings  In this retrospective analysis using data from 63 858 patients in 3 observational cohorts, 4 novel sepsis phenotypes (α, β, γ, and δ) with different demographics, laboratory values, and patterns of organ dysfunction were derived, validated, and shown to correlate with biomarkers and mortality. In the simulations using data from 3 randomized clinical trials involving 4737 patients, the outcomes related to the treatments were sensitive to changes in the distribution of these phenotypes.

Meaning  Four novel clinical phenotypes of sepsis were identified that correlated with host-response patterns and clinical outcomes and may help inform the design and interpretation of clinical trials.


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.

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Article Information

Corresponding Author: Christopher W. Seymour, MD, MSc, University of Pittsburgh School of Medicine, Keystone Bldg, 3520 Fifth Ave, Ste 100, Pittsburgh, PA 15261 (

Accepted for Publication: April 24, 2019.

Published Online: May 19, 2019. doi:10.1001/jama.2019.5791

Author Contributions: Dr Seymour had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Seymour, Kennedy, Chang, Clermont, Cooper, Gomez, Opal, van der Poll, Vodovotz, Yealy, Angus.

Acquisition, analysis, or interpretation of data: Seymour, Kennedy, Wang, Chang, Elliott, Xu, Berry, Huang, Kellum, Mi, Talisa, Visweswaran, Vodovotz, Weiss, Yende.

Drafting of the manuscript: Seymour, Kennedy, Wang, Berry, van der Poll, Vodovotz, Yealy, Angus.

Critical revision of the manuscript for important intellectual content: Seymour, Kennedy, Chang, Elliott, Xu, Berry, Clermont, Cooper, Gomez, Huang, Kellum, Mi, Opal, Talisa, Visweswaran, Vodovotz, Weiss, Yende, Angus.

Statistical analysis: Seymour, Kennedy, Wang, Chang, Elliott, Xu, Berry, Mi, Talisa, Weiss, Angus.

Obtained funding: Seymour.

Administrative, technical, or material support: Seymour, Kennedy, van der Poll, Weiss, Yealy, Angus.

Supervision: Seymour, Opal, Vodovotz, Yealy, Angus.

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.

Funding/Support: Drs Seymour, Gomez, Huang, Kellum, Visweswaran, Vodovotz, and Angus were supported in part by grants R35GM119519, P50GM076659, R34GM102696, R01GM101197, GM107231, R01LM012095, K08GM117310-01A1, and GM61992 from the National Institutes of Health. The GenIMS Study was funded by grant R01 GM61992 from the National Institute of General Medical Sciences with additional support from GlaxoSmithKline for enrollment and clinical data collection.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: Dr Angus is Associate Editor of JAMA, but he was not involved in any of the decisions regarding review of the manuscript or its acceptance.

Meeting Presentation: Presented in part at the international conference of the American Thoracic Society; May 19, 2019; Dallas, Texas.

Additional Contributions: We acknowledge the significant contribution of the patients, families, researchers, clinical staff, and sponsors for the cohort and randomized trial data included in this study. We acknowledge the Biostatistics and Data Management Core at the Clinical Research, Investigation, and Systems Modeling of Acute Illness Center in the Department of Critical Care Medicine at the University of Pittsburgh for preparing the SENECA, GenIMS, ACCESS, ProCESS, and PROWESS trial datasets. We acknowledge Eisai Medical Research Inc for providing the ACCESS trial dataset, and Eli Lilly Inc for providing the PROWESS trial dataset. We acknowledge Gordon Bernard, MD (Vanderbilt University, Nashville, Tennessee) and Anthony C. Gordon, MD (Imperial College, London, England) for their detailed review of the manuscript.

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