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

• Published in: JAMA : the journal of the American Medical Association Vol. 321; no. 20; pp. 2003 - 2017
• Main Authors: Seymour, Christopher W, Kennedy, Jason N, Wang, Shu, Chang, Chung-Chou H, Elliott, Corrine F, Xu, Zhongying, Berry, Scott, Clermont, Gilles, Cooper, Gregory, Gomez, Hernando, Huang, David T, Kellum, John A, Mi, Qi, Opal, Steven M, Talisa, Victor, van der Poll, Tom, Visweswaran, Shyam, Vodovotz, Yoram, Weiss, Jeremy C, Yealy, Donald M, Yende, Sachin, Angus, Derek C
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 28.05.2019
• Subjects: Algorithms; Biomarkers; Biomarkers - blood; Caring for the Critically Ill Patient; Clinical outcomes; Clinical trials; Cluster Analysis; Clustering; Computer simulation; Correlation; Data analysis; Datasets; Datasets as Topic; Derivation; Electronic health records; Electronic medical records; Genotype & phenotype; Heterogeneity; Hospital Mortality; Humans; Learning algorithms; Liver diseases; Machine Learning; Medical research; Mortality; Online First; Organ Dysfunction Scores; Original Investigation; Patients; Phenotype; Phenotypes; Renal function; Reproducibility; Reproducibility of Results; Retrospective Studies; Sepsis; Sepsis - classification; Sepsis - mortality; Sepsis - therapy; Septic shock; Therapeutic applications; Therapy; Uniqueness
• ISSN: 0098-7484; 1538-3598; 1538-3598
• DOI: 10.1001/jama.2019.5791

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.