ArticleBalkan medical journal2023
Sepsis-induced Coagulopathy Subphenotype Identification by Latent Class Analysis
Article in Balkan medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 2 citations in OpenAlex.
- Correlation between sub-phenotypes of coagulopathy and prognosis in sepsis patients in ICU using longitudinal group trajectory modeling: a retrospective analysis.European journal of medical research · 2026Article
- Longitudinal sepsis-induced coagulopathy score trajectories and mortality in relation to early prophylactic heparin: a multicentre cohort study.Frontiers in cellular and infection microbiology · 2026Article
- Uncovering distinct clinical phenotypes in disseminated intravascular coagulation through machine learning-enabled cluster analysis.Frontiers in molecular biosciences · 2026Article
- [Inflammatory phenotypes in sepsis and the role of vitamin C on the corresponding phenotypes].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Utility of plasma suPAR to identify AKI and sepsis associated AKI in critically ill children.iScience · 2024Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, 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.
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