Cluster analysis of clinical, angiographic, and laboratory parameters in patients with ST-segment elevation myocardial infarction.
Cluster
Machine learning
STEMI
Short-term outcome
Journal
Lipids in health and disease
ISSN: 1476-511X
Titre abrégé: Lipids Health Dis
Pays: England
ID NLM: 101147696
Informations de publication
Date de publication:
04 Jun 2024
04 Jun 2024
Historique:
received:
07
01
2024
accepted:
30
04
2024
medline:
5
6
2024
pubmed:
5
6
2024
entrez:
4
6
2024
Statut:
epublish
Résumé
ST-segment elevation myocardial infarction (STEMI) represents the most harmful clinical manifestation of coronary artery disease. Risk assessment plays a beneficial role in determining both the treatment approach and the appropriate time for discharge. Hierarchical agglomerative clustering (HAC), a machine learning algorithm, is an innovative approach employed for the categorization of patients with comparable clinical and laboratory features. The aim of the present study was to investigate the role of HAC in categorizing STEMI patients and to compare the results of these patients. A total of 3205 patients who were diagnosed with STEMI at the university hospital emergency clinic between 2015 and 2023 were included in the study. The patients were divided into 2 different phenotypic disease clusters using the HAC method, and their outcomes were compared. In the present study, a total of 3205 STEMI patients were included; 2731 patients were in cluster 1, and 474 patients were in cluster 2. Mortality was observed in 147 (5.4%) patients in cluster 1 and 108 (23%) patients in cluster 2 (chi-square P value < 0.01). Survival analysis revealed that patients in cluster 2 had a significantly greater risk of death than patients in cluster 1 did (log-rank P < 0.001). After adjustment for age and sex in the Cox proportional hazards model, cluster 2 exhibited a notably greater risk of death than did cluster 1 (HR = 3.51, 95% CI = 2.71-4.54; P < 0.001). Our study showed that the HAC method may be a potential tool for predicting one-month mortality in STEMI patients.
Identifiants
pubmed: 38835073
doi: 10.1186/s12944-024-02128-7
pii: 10.1186/s12944-024-02128-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
166Informations de copyright
© 2024. The Author(s).
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