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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Pulmonary Embolism
• Intervention / Treatment: Other: Hierarchical clustering methods

Detailed Description

Context :

Artificial Intelligence : clustering and unsupervised learning:

Artificial Intelligence (AI) is a field that combines computer science with data sets, with the aim of enabling a machine to imitate the cognitive abilities of human being. Machine learning (ML) and its sub-domain deep learning, which uses layers of neurons, are two major sub-domains of AI. The difference lies in training of each algorithm. Supervised learning, which involves training a model on known input and output data to predict future outputs, and unsupervised learning involves the discovery of hidden patterns and intrinsic underlying structures in the input data.

The aim of clustering methods is to group a set of individuals into homogeneous classes. Non-hierarchical methods can be used to classify massive data but require to fixe in advance the number of classes. Hierarchical methods, which are more time-consuming to compute, consist of a series of nested partitions represented by a clustering tree. The optimal number of classes can be determined a posteriori by reading the tree. In presence of a large number of individuals, it is common to combine non-hierarchical and hierarchical techniques. When classes are not clearly known in advance, clustering methods are use with unsupervised learning (ML) [1]. Datasets are generally divided into three disjoint datasets: training data, used to train the chosen algorithm(s); validation data, used to check performance of result; and test data, used only at the end of the process.

Venous thromboembolic disease:

Venous thromboembolic disease (VTE) is a common pathology whose incidence is imperfectly known, but increases with age, reaching 1% in subjects over 75 years old. In France, it is estimated that every year over 100,000 people develop VTE, which is responsible for between 5,000 and 10,000 deaths. Deep vein thrombosis (DVT) and pulmonary embolism (PE) are the two main types of VTE. DVT corresponds to partial or total occlusion of a deep vein by a thrombus, most often localized in the lower limbs. PE is defined as partial or total occlusion of the pulmonary arteries or their branches. The main risk of DVT is the occurrence of PE, which can be life threatening. Other VTE-specific complications and possible adverse outcomes include thromboembolic recurrence (either DVT or PE), chronic thromboembolic pulmonary hypertension and post-thrombotic syndrome in DVT. Current management of VTE is mainly based on anticoagulant therapy. The duration of treatment varies according to the estimated risk of recurrence if treatment is withdrawn, essentially depending on whether or not there is a prior major risk factor [2]. In this subgroup of PE patients, in the absence of major risk factors, risk of recurrence is considered intermediate and varies according to whether the event is a first episode or a recurrence, and whether there are obstructive pulmonary sequelae or not [3]. More recently, the therapeutic strategy has become more complex, with inclusion of minor risk factors that modulate duration of treatment without relevant evidence. Moreover, regardless of the duration of treatment, the dosage of anticoagulation beyond the sixth month is uncertain for Direct Oral Anticoagulants.

Hypotheses :

The aim will be to use the database 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.

An analysis of the six-month evolution of homogeneous patient groups with acute pulmonary embolism, constructed using clustering methods with unsupervised learning has never been conducted before. This innovative project within a large-scale hospital infrastructure is likely to offer doctors a decision-making aid, and patients a scientifically-validated form of therapeutic management.

Material and Methods :

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 (approximately 765 patients). If individual information is not available or they object to the processing of their data for 25% of the patients, a large volume of data on over 2,500 patients could potentially be analysed in this trial. This research will have no impact on current patient care. Data from consultations and various examinations carried out as part of the care will be collected for six months post-diagnosis to meet the research objectives.

Unsupervised clustering methods used in this study combine hierarchical and non-hierarchical methods. Following the hierarchical ascending clustering, Ward's index is used to determine the number of groups of interest. The centroids of these groups are then considered to initialize a partitioning algorithm, such as the k-means algorithm. Once most medically relevant groups have been determined, six-month evolution (stable, aggravation or progress) are compared. Factors influencing progression during the first three months of treatment can also be included in a statistic model, depending on their ability to predict aggravation. All these explorations should provide a basis for medical decision-making.

• Study Type: Observational
• Enrollment (Estimated): 2500

Contacts and Locations

• Study Contact: Name: Sophie Lafond; Phone Number: +33 04 83 77 20 62; Email: sophie.lafond@ch-toulon.fr
• Study Contact Backup: Name: Jean-Philippe Suppini; Phone Number: +33 04 94 14 55 25; Email: recherche.promotion@ch-toulon.fr

Study Locations

• France
  • Toulon, France, 83100
    • Recruiting
    • centre hospitalier intercommunal Toulon La Seyne sur Mer - Internal and vascular medicine
    • Contact: Jean-Noël POGGI, MD; Phone Number: +33 04 94 14 57 87; Email: jeannoel.poggi@ch-toulon.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

• Sub-segmental pulmonary embolisms ;
• Patient opposition.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	3 months

Collaborators and Investigators

• Sponsor: Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer
• Collaborators: Centre Antoine Lacassagne
• Investigators: Study Director: Jean-Noël POGGI, MD, Centre Hospitalier Intercommunal Toulon La Seyne sur Mer

Publications and helpful links

General Publications

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

Study record dates

Study Major Dates

• Study Start (Actual): December 11, 2023
• Primary Completion (Estimated): July 1, 2026
• Study Completion (Estimated): July 1, 2026

Study Registration Dates

• First Submitted: December 13, 2023
• First Submitted That Met QC Criteria: December 26, 2023
• First Posted (Actual): December 28, 2023

Study Record Updates

• Last Update Posted (Actual): April 13, 2026
• Last Update Submitted That Met QC Criteria: April 10, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Acute Pulmonary Embolism; Clustering with unsupervised learning; Therapeutic decision tool
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Respiratory Tract Diseases; Lung Diseases; Embolism and Thrombosis; Embolism; Pulmonary Embolism
• Other Study ID Numbers: 2023-CHITS-016

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No