Revising Host Phenotypes of Sepsis Using Microbiology

Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown.Methods: We conducted a secondary analysis of the Recom...

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Autores principales:	Huiying Zhao, Jason N. Kennedy, Shu Wang, Emily B. Brant, Gordon R. Bernard, Kimberley DeMerle, Chung-Chou H. Chang, Derek C. Angus, Christopher W. Seymour
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	phenotype latent class analysis host pathogen sepsis Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/935d1902d25a4411aeaada1eeb6e5876
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spelling	oai:doaj.org-article:935d1902d25a4411aeaada1eeb6e58762021-11-05T06:26:43ZRevising Host Phenotypes of Sepsis Using Microbiology2296-858X10.3389/fmed.2021.775511https://doaj.org/article/935d1902d25a4411aeaada1eeb6e58762021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.775511/fullhttps://doaj.org/toc/2296-858XBackground: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown.Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables (“host model”) and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, “host-pathogen model”). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves.Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780).Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.Huiying ZhaoHuiying ZhaoHuiying ZhaoJason N. KennedyJason N. KennedyShu WangEmily B. BrantEmily B. BrantGordon R. BernardKimberley DeMerleChung-Chou H. ChangDerek C. AngusDerek C. AngusChristopher W. SeymourChristopher W. SeymourChristopher W. SeymourFrontiers Media S.A.articlephenotypelatent class analysishostpathogensepsisMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	phenotype latent class analysis host pathogen sepsis Medicine (General) R5-920
spellingShingle	phenotype latent class analysis host pathogen sepsis Medicine (General) R5-920 Huiying Zhao Huiying Zhao Huiying Zhao Jason N. Kennedy Jason N. Kennedy Shu Wang Emily B. Brant Emily B. Brant Gordon R. Bernard Kimberley DeMerle Chung-Chou H. Chang Derek C. Angus Derek C. Angus Christopher W. Seymour Christopher W. Seymour Christopher W. Seymour Revising Host Phenotypes of Sepsis Using Microbiology
description	Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown.Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables (“host model”) and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, “host-pathogen model”). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves.Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780).Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.
format	article
author	Huiying Zhao Huiying Zhao Huiying Zhao Jason N. Kennedy Jason N. Kennedy Shu Wang Emily B. Brant Emily B. Brant Gordon R. Bernard Kimberley DeMerle Chung-Chou H. Chang Derek C. Angus Derek C. Angus Christopher W. Seymour Christopher W. Seymour Christopher W. Seymour
author_facet	Huiying Zhao Huiying Zhao Huiying Zhao Jason N. Kennedy Jason N. Kennedy Shu Wang Emily B. Brant Emily B. Brant Gordon R. Bernard Kimberley DeMerle Chung-Chou H. Chang Derek C. Angus Derek C. Angus Christopher W. Seymour Christopher W. Seymour Christopher W. Seymour
author_sort	Huiying Zhao
title	Revising Host Phenotypes of Sepsis Using Microbiology
title_short	Revising Host Phenotypes of Sepsis Using Microbiology
title_full	Revising Host Phenotypes of Sepsis Using Microbiology
title_fullStr	Revising Host Phenotypes of Sepsis Using Microbiology
title_full_unstemmed	Revising Host Phenotypes of Sepsis Using Microbiology
title_sort	revising host phenotypes of sepsis using microbiology
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/935d1902d25a4411aeaada1eeb6e5876
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Revising Host Phenotypes of Sepsis Using Microbiology

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