- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06270615
Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model (SHOCKMATRIX)
External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Tobias Gauss, MD
- Phone Number: +33476769288
- Email: tgauss@chu-grenoble.fr
Study Contact Backup
- Name: Samia Salah
- Phone Number: +33476769288
- Email: SSalah1@chu-grenoble.fr
Study Locations
-
-
-
Clichy, France, 92110
- Recruiting
- Beaujon Hospital AP-HP, Anesthesia-Intensive Care Department
-
Contact:
- Mathilde Holleville, MD
- Email: mathilde.holleville@aphpr.fr
-
La Tronche, France, 38700
- Recruiting
- Grenoble Alpes University Hospital
-
Contact:
- Tobias Gauss, MD
- Phone Number: +33476769288
- Email: tgauss@chu-grenoble.fr
-
Contact:
- Samia Salah
- Phone Number: +33476769288
- Email: SSalah1@chu-grenoble.fr
-
Le Kremlin-Bicêtre, France, 94270
- Recruiting
- Bicêtre Hospital AP-HP, Anesthesia-Intensive Care Department
-
Contact:
- Marie Werner, MD
- Email: marie.werner@aphp.fr
-
Lille, France, 59037
- Recruiting
- Lille University Hospital, Anaesthesia and Intensive Care Unit
-
Contact:
- Benjamin Bijok, MD
- Email: Benjamin.BIJOK@CHRU-LILLE.FR
-
Paris, France, 75013
- Recruiting
- Pitié-Salpêtrière Hospital AP-HP, Anesthesia-Intensive Care Department
-
Contact:
- Arthur James, MD
- Email: arthur.james@aphp.fr
-
Paris, France, 75015
- Recruiting
- Georges-Pompidou European Hospital AP-HP, Anesthesia-Intensive Care Department
-
Contact:
- Nathalie Delhaye, MD
- Email: nathalie.delhaye@aphp.fr
-
Strasbourg, France, 67200
- Recruiting
- University Hospitals Strasbourg, Anaesthesia, Intensive Care and Peri-Operative Medicine Department
-
Contact:
- Alain Meyer, MD
- Email: alain.meyer@chru-strasbourg.fr
-
Toulouse, France, 31059
- Recruiting
- University Hospital of Toulouse, Polyvalent Intensive Care
-
Contact:
- Véronique Ramonda, MD
- Email: ramonda.v@chu-toulouse.fr
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
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
|
Retrospective and prospective validation of a machine learning model to predict major haemorrhage in trauma patients compared to clinician prediction
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Fβ-score, with β = 4
Time Frame: 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) |
18 months
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Common binary classification metrics
Time Frame: 18 months
|
Sensitivity Se, Specificity Sp, Accuracy Acc, Positive Predictive Value PPV, Negative Predictive Value NPV
|
18 months
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimated)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- CRCBDD1712
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Wounds and Injuries
-
3MWithdrawn
-
Centre Hospitalier Universitaire de NīmesCompletedWounds and Injuries, Hands | Wounds and Injuries, Wrists | Wounds and Injuries, Feet | Wounds and Injuries, AnklesFrance
-
Samsung Medical CenterCompletedNeedle Stick InjuriesKorea, Republic of
-
Assaf-Harofeh Medical CenterUnknownInjuries and Wounds
-
Karolinska InstitutetCompletedWounds and Injuries | Blast Injuries | War-Related Injuries | Gunshot WoundSweden
-
Medical University of South CarolinaCompletedNeedlestick InjuriesUnited States
-
Hospital Universiti Sains MalaysiaActive, not recruiting
-
Temple UniversityWithdrawnLacerations | Injuries | WoundsUnited States
-
Acibadem UniversityCompletedNeedlestick InjuriesTurkey
Clinical Trials on Ambispective validation of machine learning-based predictive model
-
Peking University Third HospitalCompletedInfertility, Male | Azoospermia, NonobstructiveChina
-
Assiut UniversityNot yet recruiting
-
West China HospitalAffiliated Hospital of North Sichuan Medical College; Nanchong Central Hospital and other collaboratorsRecruitingChronic Obstructive Pulmonary Disease | Deep Vein Thrombosis | Machine Learning | Pulmonary Thromboembolisms | Chronic Obstructive Pulmonary Disease With Acute Exacerbation, UnspecifiedChina
-
First Affiliated Hospital of Xinjiang Medical UniversityShihezi UniversityUnknownUnstable Angina | NSTEMI - Non-ST Segment Elevation MIChina
-
IRCCS Eugenio MedeaMassachusetts Institute of Technology; Politecnico di MilanoRecruiting
-
University Health Network, TorontoUniversity of TorontoRecruiting
-
Lizora LLCSheng'ai Traditional Chinese Medicine HospitalCompletedCOVID-19 | Influenza | Post-COVID-19 SyndromeChina
-
University of AlicanteCompletedCardiovascular Diseases | Hypertension | Exercise | Body Composition | Overweight and Obesity | Machine Learning | Diet, Mediterranean | Triglyceride-Storage; DiseaseSpain
-
National Taipei University of Nursing and Health...Recruiting
-
Liverpool University Hospitals NHS Foundation TrustKidney Research United KingdomNot yet recruitingFrailty | Kidney Transplant Rejection | Renal Transplant | Diagnosis | Kidney Transplant; Complications | Kidney Transplant | Renal Transplant Failure | Transplant DysfunctionUnited Kingdom