ArticleFrontiers in molecular biosciences2026
Uncovering distinct clinical phenotypes in disseminated intravascular coagulation through machine learning-enabled cluster analysis.
Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 Consensus Cluster Plus to identify clinical phenotypes in 134 patients with DIC. The analysis incorporated the key variables: Thrombin-Antithrombin Complex (TAT), Plasmin-α 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], Conclusion: The study identified two clusters with distinct laboratory profiles and mortality risk.
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