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 are expected to be included.
This research will have no impact on current patient care. Data from consultations and various examinations carried out as part of care will be collected for six months post-diagnosis in order to meet the research objectives.
Age ≥ 18 years;
Patient with acute pulmonary embolism in CHITS (hospitalised or not).
Sub-segmental pulmonary embolisms ;
Patient opposition.
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 (around 1900 patients). For the prospective part, it is planned to include patients with same characteristics over the years 2024 and 2025.
Cohort
Other
Hierarchical clustering methods
Other interventionHierarchical clustering methods will be used to form homogeneous groups of patients based on their data at diagnosis: presence or absence of symptoms, clinical and biological data, and presence or absence of favouring factors. Patient evolution at 6 months can fall into categories: stable, aggravation or progress, which are determined by events such as recurrence, hemorrhage, functional sequelae or death.
Primary: Identify homogeneous groups of patients based on their medical characteristics at diagnosis, and then compare their evolution at 6 months.
Hierarchical clustering methods will be used to form homogeneous groups of patients based on their data at diagnosis: presence or absence of symptoms, clinical and biological data, and presence or absence of favouring factors. Patient evolution at 6 months can fall into categories: stable, aggravation or progress, which are determined by events such as recurrence, hemorrhage, functional sequelae or death.
Secondary: Determine factors predictive of 6-month progression within the first three months of treatment.
A priori, groups defined for primary objective will be maintained. Factors considered during the first three months of treatment will include: clinical and biological data, presence or absence of symptoms, favorable factors or complications.
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