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

NCT06183944 | Recruiting

• 1 other identifier: 2023-CHITS-016
• Study Type: observational
• Target: 2,500
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Pulmonary Embolism

Keywords

Acute Pulmonary Embolism; Clustering with unsupervised learning; Therapeutic decision tool

Outcome Measures

Primary Outcomes (1)

• 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.

  6 months

Secondary Outcomes (1)

• Secondary: Determine factors predictive of 6-month progression within the first three months of treatment.

  3 months

Study Arms (1)

Patient with acute pulmonary embolism

Patient with acute pulmonary embolism in Centre Hospitalier Intercommunal Toulon La Seyne sur Mer, hospitalised or not since 2019

Other: Hierarchical clustering methods

Interventions

Hierarchical clustering methods OTHER

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.

Patient with acute pulmonary embolism

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Age ≥ 18 years;
• Patient with acute pulmonary embolism in CHITS (hospitalised or not).

You may not qualify if:

• Sub-segmental pulmonary embolisms ;
• Patient opposition.

Sponsors & Collaborators

• Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer: lead

Study Sites (1)

centre hospitalier intercommunal Toulon La Seyne sur Mer - Internal and vascular medicine

Toulon, 83100, France

RECRUITING

Related Publications (4)

• Gal J, Bailleux C, Chardin D, Pourcher T, Gilhodes J, Jing L, Guigonis JM, Ferrero JM, Milano G, Mograbi B, Brest P, Chateau Y, Humbert O, Chamorey E. Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer. Comput Struct Biotechnol J. 2020 Jun 3;18:1509-1524. doi: 10.1016/j.csbj.2020.05.021. eCollection 2020.

  PMID: 32637048 BACKGROUND

• Gallo A, Valerio L, Barco S. The 2019 European guidelines on pulmonary embolism illustrated with the aid of an exemplary case report. Eur Heart J Case Rep. 2021 Jan 4;5(2):ytaa542. doi: 10.1093/ehjcr/ytaa542. eCollection 2021 Feb.

  PMID: 33598618 BACKGROUND

• Duffett L, Castellucci LA, Forgie MA. Pulmonary embolism: update on management and controversies. BMJ. 2020 Aug 5;370:m2177. doi: 10.1136/bmj.m2177.

  PMID: 32759284 BACKGROUND

• Yu T, Shen R, You G, Lv L, Kang S, Wang X, Xu J, Zhu D, Xia Z, Zheng J, Huang K. Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study. Front Cardiovasc Med. 2022 Sep 16;9:990788. doi: 10.3389/fcvm.2022.990788. eCollection 2022.

  PMID: 36186967 BACKGROUND

MeSH Terms

Conditions

Pulmonary Embolism

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Embolism; Embolism and Thrombosis; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Jean-Noël POGGI, MD

  Centre Hospitalier Intercommunal Toulon La Seyne sur Mer

  STUDY DIRECTOR

Central Study Contacts

Jean-Philippe Suppini

CONTACT

04 94 14 55 25; recherche.promotion@ch-toulon.fr

Sophie Lafond

CONTACT

04 83 77 20 62; sophie.lafond@ch-toulon.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 13, 2023
• First Posted: December 28, 2023
• Study Start: December 11, 2023
• Primary Completion (Estimated): July 1, 2026
• Study Completion (Estimated): July 1, 2026
• Last Updated: April 13, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)