Data Clustering Study With Artificial Intelligence and Phenotyping of Patients With Acute
The aim will be to identify clinically relevant phenotypes in patients with acute pulmonary embolism. Hierarchical clustering methods combined with unsupervised learning (machine learning) will be used to obtain groups of patients who are homogeneous at diagnosis. Evaluating their prognosis at 6 months (recurrence or c
| Condition(s) | Pulmonary Embolism |
|---|---|
| Status | Recruiting |
| Study type | Observational |
| Summary | The aim will be to identify clinically relevant phenotypes in patients with acute pulmonary embolism. Hierarchical clustering methods combined with unsupervised learning (machine learning) will be used to obtain groups of patients who are homogeneous at diagnosis. Evaluating their prognosis at 6 months (recurrence or chronic thromboembolic pulmonary hypertension), account the first 3 months of anticoagulant treatment, would provide an aid to medical decision-making. This research will include a retrospective and a prospective parts. The retrospective part will include patients who have been admitted to CHITS for acute pulmonary embolism since 2019. For the prospective part, it is planned to include patients with same characteristics over the years 2024 and 2025. More than 2,500 patients ar |
| Who can participate | Inclusion Criteria: * Age ≥ 18 years; * Patient with acute pulmonary embolism in CHITS (hospitalised or not). Exclusion Criteria: * Sub-segmental pulmonary embolisms ; * Patient opposition. |
| Ages | 18 Years |
| Sex | All |
| Lead sponsor | Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer |
| Locations | Toulon, France |
| Start date | 2023-12-11 |
| NCT ID | NCT06183944 |
| Official listing | https://clinicaltrials.gov/study/NCT06183944 |