ArticleJournal of thoracic disease2025
Identification of disease subtypes associated with prognosis in patients with intermediate-high-risk pulmonary embolism based on hierarchical cluster analysis.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- From Clusters to Outcomes: Machine Learning-Based Phenotyping in Intermediate-High-Risk Acute Pulmonary Embolism.Pulmonary circulation · 2026Article
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6 authors.
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Abstract
Background: Pulmonary embolism (PE) continues to be one of the leading causes of cardiovascular mortality. The wide range of reported mortality rates reflects that PE is a heterogeneous disease with different clinical characteristics. European Society of Cardiology (ESC) guidelines stratify PE into high, intermediate and low-risk groups, yet intermediate-high-risk PE lacks tailored management. This study seeks to enhance risk stratification through a retrospective analysis utilizing machine learning algorithms on intermediate-high-risk PE cases. Methods: This study retrospectively identified a cohort of 79 patients with intermediate-high-risk PE from two clinical centers. We performed an unsupervised stratified cluster analysis using the average method, based on 11 standardized continuous variables [age, sex, chronic lung disease, chronic heart disease, diabetes, Pulmonary Embolism Severity Index (PESI) score, mean arterial pressure, oxygen index, B-type brain natriuretic peptide (BNP), D-dimer, and treatment]. We compared baseline characteristics, laboratory data, and clinical outcomes among the resulting clusters using one-way analysis of variance. Results: Three distinct clusters were identified through agglomerative hierarchical clustering, utilizing 10 clinical variables: cluster 1 (6 cases), a dominant cluster 2 (67 cases), and cluster 3 (6 cases). The analysis showed statistically significant differences in age, chronic lung disease, chronic heart disease and diabetes among clusters (P<0.05). The three clusters showed significant differences in length of stay in the intensive care unit (ICU), total length of stay, hospitalization expenses, 1- and 3-year PE mortality (P Conclusions: In this study, hierarchical clustering discovered disease subtypes related to prognosis by clustering the characteristics of intermediate-high-risk PE patients at admission. Machine learning-based classification may contribute to risk stratification for patients with intermediate-high-risk PE.
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