Clinical trial NCT06270615: Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model
Official title: External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Wounds and Injuries
Traumatic Shock
Hemorrhagic Shock
Keywords: Predictive model
Ambispective validation
Machine learning
Missing values
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
Collaborators: name: Traumabase Group
name: Capgemini Invent
name: Ecole polytechnique
name: EHESS (Ecole des hautes études en sciences sociales)
name: CNRS (Centre national de la recherche scientifique)
First posted: 2024-02-21
Enrollment: 1584
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Every severe trauma adult patient 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

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.

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

Primary outcomes:
measure: Fβ-score, with β = 4
timeFrame: 18 months
Secondary outcomes:
measure: Common binary classification metrics
timeFrame: 18 months
Other outcomes:
Locations:
city: Clichy
country: France
facility: Beaujon Hospital AP-HP, Anesthesia-Intensive Care Department
city: La Tronche
country: France
facility: Grenoble Alpes University Hospital
city: Le Kremlin-Bicêtre
country: France
facility: Bicêtre Hospital AP-HP, Anesthesia-Intensive Care Department
city: Lille
country: France
facility: Lille University Hospital, Anaesthesia and Intensive Care Unit
city: Paris
country: France
facility: Pitié-Salpêtrière Hospital AP-HP, Anesthesia-Intensive Care Department
city: Paris
country: France
facility: Georges-Pompidou European Hospital AP-HP, Anesthesia-Intensive Care Department
city: Strasbourg
country: France
facility: University Hospitals Strasbourg, Anaesthesia, Intensive Care and Peri-Operative Medicine Department
city: Toulouse
country: France
facility: University Hospital of Toulouse, Polyvalent Intensive Care
Study officials:
References: