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Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis
BACKGROUND: Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC). AIMS: To identify the latent phenotypes of patients with SIC. STUDY DESIGN: Retrospective cohort study. METHODS: We obtained data of patients with...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Galenos Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339839/ https://www.ncbi.nlm.nih.gov/pubmed/37265179 http://dx.doi.org/10.4274/balkanmedj.galenos.2023.2023-4-6 |
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author | Cai, Dan Greco, Massimiliano Wu, Qin Cheng, Yisong |
author_facet | Cai, Dan Greco, Massimiliano Wu, Qin Cheng, Yisong |
author_sort | Cai, Dan |
collection | PubMed |
description | BACKGROUND: Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC). AIMS: To identify the latent phenotypes of patients with SIC. STUDY DESIGN: Retrospective cohort study. METHODS: We obtained data of patients with SIC from the Medical Information Mart for Intensive Care IV database. SIC subphenotypes were identified by latent class analysis (LCA) and K-means clustering. Clinical and laboratory variables were obtained in patients who met the diagnostic criteria for SIC. The baseline characteristics of the patients and the association between the heterogeneity of anticoagulant therapy and clinical outcomes (28-day and in-hospital mortality) were compared between the subphenotypes. RESULTS: We identified 4,993 patients with SIC. The LCA and K-means clustering analysis robustly identified three subphenotypes of SIC. Class 1 patients (n = 1,808) had the lowest blood cell counts (leukocytes, erythrocytes, and platelets). Class 2 patients (n = 1,157) had severe coagulopathy with a high prothrombin time and international normalized ratio, multiple-organ dysfunction, high lactate, sequential organ failure assessment score, and mortality. Class 3 (n = 2,028) were older, had more comorbidities, a higher fibrinogen concentration, and lower plasma and platelet infusion rates. After variable adjustments, heparin therapy reduced the 28-day mortality (odds ratio [OR] 0.39, 0.30-0.49, p < 0.001) and in-hospital mortality (OR 0.42, 0.33-0.53, p < 0.001) only in class 2. CONCLUSION: Three SIC subphenotypes were defined using clinical findings and laboratory variables. The effects of heparin treatment differ between the subphenotypes. This finding will facilitate the identification of target patients with SIC who should receive anticoagulant therapy. |
format | Online Article Text |
id | pubmed-10339839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Galenos Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-103398392023-07-14 Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis Cai, Dan Greco, Massimiliano Wu, Qin Cheng, Yisong Balkan Med J Original Article BACKGROUND: Recent studies have shown that anticoagulant therapy has heterogeneous treatment effects on patients with sepsis-induced coagulopathy (SIC). AIMS: To identify the latent phenotypes of patients with SIC. STUDY DESIGN: Retrospective cohort study. METHODS: We obtained data of patients with SIC from the Medical Information Mart for Intensive Care IV database. SIC subphenotypes were identified by latent class analysis (LCA) and K-means clustering. Clinical and laboratory variables were obtained in patients who met the diagnostic criteria for SIC. The baseline characteristics of the patients and the association between the heterogeneity of anticoagulant therapy and clinical outcomes (28-day and in-hospital mortality) were compared between the subphenotypes. RESULTS: We identified 4,993 patients with SIC. The LCA and K-means clustering analysis robustly identified three subphenotypes of SIC. Class 1 patients (n = 1,808) had the lowest blood cell counts (leukocytes, erythrocytes, and platelets). Class 2 patients (n = 1,157) had severe coagulopathy with a high prothrombin time and international normalized ratio, multiple-organ dysfunction, high lactate, sequential organ failure assessment score, and mortality. Class 3 (n = 2,028) were older, had more comorbidities, a higher fibrinogen concentration, and lower plasma and platelet infusion rates. After variable adjustments, heparin therapy reduced the 28-day mortality (odds ratio [OR] 0.39, 0.30-0.49, p < 0.001) and in-hospital mortality (OR 0.42, 0.33-0.53, p < 0.001) only in class 2. CONCLUSION: Three SIC subphenotypes were defined using clinical findings and laboratory variables. The effects of heparin treatment differ between the subphenotypes. This finding will facilitate the identification of target patients with SIC who should receive anticoagulant therapy. Galenos Publishing 2023-07-12 /pmc/articles/PMC10339839/ /pubmed/37265179 http://dx.doi.org/10.4274/balkanmedj.galenos.2023.2023-4-6 Text en ©Copyright 2023 by Trakya University Faculty of Medicine https://creativecommons.org/licenses/by-nc-nd/4.0/The Balkan Medical Journal published by Galenos Publishing House. |
spellingShingle | Original Article Cai, Dan Greco, Massimiliano Wu, Qin Cheng, Yisong Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title | Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title_full | Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title_fullStr | Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title_full_unstemmed | Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title_short | Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis |
title_sort | sepsis-induced coagulopathy subphenotype identification by latent class analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339839/ https://www.ncbi.nlm.nih.gov/pubmed/37265179 http://dx.doi.org/10.4274/balkanmedj.galenos.2023.2023-4-6 |
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