Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model

NCT ID: NCT06270615

Last Updated: 2025-02-18

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1584 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-07-01
• Study Completion Date: 2024-06-24

Brief Summary

Management of post-traumatic severe hemorrhage remains a challenge to any trauma care system. Studying integrated and innovative tools designed to predict the risk of early severe hemorrhage (ESH) and resource needs could offer a promising option to improve clinical decisions and then shorten the time of intervention in the context of pre-hospital severe trauma. As evidence seems to be lacking to address this issue, this ambispective validation study proposes to assess on an independent cohort the predictive performance of a newly developed machine learning-based model, as well as the feasibility of its clinical deployment under real-time healthcare conditions.

Detailed Description

Background: Hemorrhagic shock remains the leading cause of early preventable death in severely injured patients. When a severe hemorrhage occurs shortly after serious trauma, thus defining an early severe hemorrhage (ESH), its management becomes highly challenging. In this context, improving clinical decisions and shortening the time of intervention, known as a critical endpoint, may require designing innovative tools for early detection as well as studying their integration within the routine healthcare process.

Objective: Part of the TRAUMATRIX project led by the Traumabase Group in partnership with Capgemini Invent and several research centers (Ecole polytechnique, CNRS, EHESS), this study aims to externally validate a recently developed machine learning-based predictive model for ESH in trauma patients. This model, previously trained on a high-quality trauma database named Traumabase, offers a specific ability to handle missing values.

Materials and Methods: At least 1500 adult trauma patients from 8 French trauma centers will be included for a six-24 month period with a retrospective and prospective sample. ESH will stand as our primary outcome, defined as any of the following events occurring within the first hours of trauma management: any packed red blood cell (RBC) transfusion in the resuscitation room, or transfusion exceeding 4 RBCs within the first 6 hours, or emergency hemostatic intervention (surgery or interventional radiology), or death in an unambiguous setting of uncontrolled, objectified hemorrhage. Data of interest will be collected in two phases: (1) from the prehospital phase of the trauma management, where the variables needed to calculate the algorithmic prediction of ESH (10 inputs) as well as the clinical prediction from the attending trauma leader receiving in the resuscitation room a pre-alert call from the dispatch center, will be recorded in real-time using a dedicated user-friendly smartphone interface developed by the Capgemini Invent teams; (2) from a delayed phase where a classic inclusion in the Traumabase® will be performed to retrieve the component variables of the ESH composite endpoint, and a feedback survey will be sent to the trauma teams involved in the study to collect additional informative data. The prospective data collected, we will compare to a retrospective cohort predictive performance of two systems, namely the clinical trauma expert versus our machine learning-based predictive model.

Conditions

Wounds and Injuries; Traumatic Shock; Hemorrhagic Shock

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

Prehospital severe trauma patients

Every severe trauma patient 18 years of age or older to be admitted to a participating center excluding those already diagnosed with active hemorrhage from computed tomography findings and those with prior traumatic cardiac arrest

Ambispective validation of machine learning-based predictive model

Intervention Type: OTHER

Retrospective and prospective validation of a machine learning model to predict major haemorrhage in trauma patients compared to clinician prediction

Interventions

Ambispective validation of machine learning-based predictive model

Retrospective and prospective validation of a machine learning model to predict major haemorrhage in trauma patients compared to clinician prediction

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• every severe trauma adult patient to be admitted to a participating center

Exclusion Criteria

• patients already diagnosed with active hemorrhage from computed tomography findings;
• patients with prior traumatic cardiac arrest
• patient under 18 years of age
• opposition of patient or relative

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Traumabase Group

UNKNOWN

Sponsor Role: collaborator

Capgemini Invent

UNKNOWN

Sponsor Role: collaborator

Ecole polytechnique

UNKNOWN

Sponsor Role: collaborator

EHESS (Ecole des hautes études en sciences sociales)

UNKNOWN

Sponsor Role: collaborator

CNRS (Centre national de la recherche scientifique)

UNKNOWN

Sponsor Role: collaborator

Assistance Publique - Hôpitaux de Paris

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Beaujon Hospital AP-HP, Anesthesia-Intensive Care Department

Clichy, , France

Grenoble Alpes University Hospital

La Tronche, , France

Bicêtre Hospital AP-HP, Anesthesia-Intensive Care Department

Le Kremlin-Bicêtre, , France

Lille University Hospital, Anaesthesia and Intensive Care Unit

Lille, , France

Pitié-Salpêtrière Hospital AP-HP, Anesthesia-Intensive Care Department

Paris, , France

Georges-Pompidou European Hospital AP-HP, Anesthesia-Intensive Care Department

Paris, , France

University Hospitals Strasbourg, Anaesthesia, Intensive Care and Peri-Operative Medicine Department

Strasbourg, , France

University Hospital of Toulouse, Polyvalent Intensive Care

Toulouse, , France

Countries

France

Other Identifiers

CRCBDD1712

Identifier Type: -

Identifier Source: org_study_id