Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model

Wounds and Injuries Clinical Trial

— SHOCKMATRIX  

Official title:

External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06270615
• Other study ID #: CRCBDD1712
• Status: Recruiting
• Start date: July 1, 2022
• Est. completion date: June 2024

Study information

• Verified date: February 2024
• Source: Assistance Publique - Hôpitaux de Paris
• Contact: Tobias Gauss, MD
• Phone: +33476769288
• Email: tgauss[@]chu-grenoble.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500
• Est. completion date: June 2024
• Est. primary completion date: June 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Hemorrhage
• Hemorrhagic Shock
• Shock
• Shock, Hemorrhagic
• Shock, Traumatic
• Traumatic Shock
• Wounds and Injuries

Intervention

Other:
• 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

Locations

Country	Name	City	State
France	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	Georges-Pompidou European Hospital AP-HP, Anesthesia-Intensive Care Department	Paris
France	Pitié-Salpêtrière 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

Sponsors (6)

Lead Sponsor	Collaborator
Assistance Publique - Hôpitaux de Paris	Capgemini Invent, CNRS (Centre national de la recherche scientifique), Ecole polytechnique, EHESS (Ecole des hautes études en sciences sociales), Traumabase Group

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Fß-score, with ß = 4	A configurable single-score metric for evaluating a binary classification model. The parameter ß allows placing more emphasis on false-negative prediction error.; The formula for Fß-score is given below (TP true positives, FN false negatives, FP false positives): Fß= ((1+ß^2 ).TP)/((1+ß^2 ).TP+ ß^2.FN+FP)	18 months
Secondary	Common binary classification metrics	Sensitivity Se, Specificity Sp, Accuracy Acc, Positive Predictive Value PPV, Negative Predictive Value NPV	18 months