ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Uncovering Distinct Clinical Phenotypes in Disseminated Intravascular Coagulation through Machine Learning-Enabled Cluster Analysis
1. the 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, China
2. Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, China
3. Nanchang Key Laboratory of Thrombosis and Hemostasis, Nanchang, China, Nanchang, China
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Abstract
Background: Disseminated intravascular coagulation (DIC) is a critical condition encountered in the intensive care unit (ICU), characterized by multiple etiologies and variable outcomes. Distinguishing between DIC phenotypes poses a significant challenge. This study aims to apply unsupervised machine learning (ML) algorithms to stratify DIC patients, thereby enabling more personalized treatment approaches. Methods: We conducted a retrospective analysis of patients diagnosed with DIC upon admission to the ICU at a comprehensive teaching tertiary hospital in China, spanning from May 2015 to November 2022. We applied an unsupervised machine learning approach for consensus clustering using the R package ConsensusClusterPlus to identify clinical phenotypes in 134 patients with DIC. The analysis incorporated the key variables: Thrombin-Antithrombin Complex (TAT), Plasmin-α₂-Plasmin Inhibitor Complex (PIC), tissue plasminogen activator-inhibitor complex (tPAIC), and thrombomodulin (TM). The elbow method, cumulative distribution function (CDF) plot, and consensus matrix were employed to ascertain the optimal number of clusters. Logistic regression (LR) analysis was used to investigate the association between the identified phenotypes and clinical endpoints. Results: The consensus cluster analysis delineated two distinct subtypes: a mild coagulation dysfunction subtype (n=79) and a severe coagulation dysfunction subtype (n=55). Notable differences were observed in both variables included in the analysis (e.g., thrombin-antithrombin complex [TAT], P<0.05) and those not utilized for model training (e.g., heart rate [HR] P<0.05 and systolic blood pressure [SBP] P<0.05). Logistic regression revealed that the severe coagulation dysfunction subtype was significantly associated with increased odds of 7-day (OR 4.71; 95% CI 2.23-9.98; P<0.001), 28-day (OR 2.29; 95% CI 1.11-4.72; P=0.024). Conclusion: The study identified two clusters with distinct laboratory profiles and mortality risk
Summary
Keywords
Cluster analysis, Disseminated Intravascular Coagulation, machine learning, phenotypes, stratification
Received
05 September 2025
Accepted
18 February 2026
Copyright
© 2026 Zeng, Zeng, Lin, Zhong, He and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jingchun Song
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