Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model

Recruiting

• Conditions: Traumatic Shock; Wounds and Injuries; Hemorrhagic Shock
• Interventions: Other: Ambispective validation of machine learning-based predictive model

• Registration Number: NCT06270615
• Lead Sponsor: Assistance Publique - Hôpitaux de Paris

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.

Study Timeline

• Study Start: 2022-07-01
• Status Verified: 2024-02
• Study First Submitted: 2024-02-14
• Study First Submitted QC: 2024-02-14
• Last Update Submitted: 2024-02-14
• Study First Posted: 2024-02-21
• Last Update Posted: 2024-02-21
• Primary Completion: 2024-06
• Study Completion: 2024-06

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1500

Inclusion Criteria

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

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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

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Prehospital severe trauma patients	Ambispective validation of machine learning-based predictive model	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

Primary Outcome Measures

Name	Time	Method
Fβ-score, with β = 4	18 months	A configurable single-score metric for evaluating a binary classification model. The parameter β allows placing more emphasis on false-negative prediction error.; The formula for Fβ-score is given below (TP true positives, FN false negatives, FP false positives): Fβ= ((1+β\^2 ).TP)/((1+β\^2 ).TP+ β\^2.FN+FP)

Secondary Outcome Measures

Name	Time	Method
Common binary classification metrics	18 months	Sensitivity Se, Specificity Sp, Accuracy Acc, Positive Predictive Value PPV, Negative Predictive Value NPV

Trial Locations

Locations (8)

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