CompletedNCT06183944

Data Clustering Study With Artificial Intelligence and Phenotyping of Patients Who Presented With Acute Pulmonary Embolism

Sponsor: Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer

Trial Details

Condition	Pulmonary Embolism
Intervention	Hierarchical clustering methods
Status	Completed
Participants	2,500
Est. Completion	1 July 2026
Last Updated	7 July 2026

Primary Outcome

Primary: Identify homogeneous groups of patients based on their medical characteristics at diagnosis, and then compare their evolution at 6 months.

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * Age ≥ 18 years; * Patient with acute pulmonary embolism in CHITS (hospitalised or not). Exclusion Criteria: * Sub-segmental pulmonary embolisms ; * Patient opposition.

Trial Sites

Countries

France

View Registry Entry

Related Analysis

French Registry Study Uses AI Clustering to Define Pulmonary Embolism Phenotypes — Implications for Risk Stratification

French researchers are using AI-driven clustering to identify distinct pulmonary embolism phenotypes in 2,500 patients. This novel approach could revolutionise PE risk stratification and personalised treatment decisions.

Read full article →

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