Wounds and InjuriesSHOCKMATRIXNCT06270615
Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model
An observational study of Wounds and Injuries and Traumatic Shock, run by Assistance Publique - Hôpitaux de Paris at 8 sites in France.
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Wounds and Injuries, Traumatic Shock, Hemorrhagic Shock
- What is being tested?
- Ambispective validation of machine learning-based predictive model
- How many people
- 1,584 participants
- Where
- 8 sitesFrance
- Study dates
- 1 Jul 2022to 24 Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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
Who may be able to join
- every severe trauma adult patient to be admitted to a participating center
Who cannot join
- 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
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
France8 sites
Beaujon Hospital AP-HP, Anesthesia-Intensive Care Department
Clichy, 92110
Grenoble Alpes University Hospital
La Tronche, 38700
Bicêtre Hospital AP-HP, Anesthesia-Intensive Care Department
Le Kremlin-Bicêtre, 94270
Lille University Hospital, Anaesthesia and Intensive Care Unit
Lille, 59037
Pitié-Salpêtrière Hospital AP-HP, Anesthesia-Intensive Care Department
Paris, 75013
Georges-Pompidou European Hospital AP-HP, Anesthesia-Intensive Care Department
Paris, 75015
University Hospitals Strasbourg, Anaesthesia, Intensive Care and Peri-Operative Medicine Department
Strasbourg, 67200
University Hospital of Toulouse, Polyvalent Intensive Care
Toulouse, 31059
About this study
Official title: External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients
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.
Read the 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
- Being tested
- Ambispective validation of machine learning-based predictive model
- Keywords
- Predictive model, Ambispective validation, Machine learning, Missing values
- Medical subject headings
- Wounds and Injuries, Shock, Traumatic, Shock, Hemorrhagic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ambispective validation of machine learning-based predictive modelOther
Retrospective and prospective validation of a machine learning model to predict major haemorrhage in trauma patients compared to clinician prediction
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Other: Ambispective validation of machine learning-based predictive model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Fβ-score, with β = 4
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)
Measured: 18 months
Secondary outcomes (1)
Common binary classification metrics
Sensitivity Se, Specificity Sp, Accuracy Acc, Positive Predictive Value PPV, Negative Predictive Value NPV
Measured: 18 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2024
- Study start1 Jul 2022
- Primary completion24 Jun 2024
- Study completion24 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Wounds and InjuriesStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 1,584 participants (actual)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- Traumabase Group, Capgemini Invent, Ecole polytechnique, EHESS (Ecole des hautes études en sciences sociales), CNRS (Centre national de la recherche scientifique)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06270615
- Sponsor's protocol ID
- CRCBDD1712
- Acronym
- SHOCKMATRIX
- Status last verified
- Feb 2025
- First submitted
- 14 Feb 2024
- Last updated
- 18 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients
- ClinicalTrials.gov ID
- NCT06270615
- Acronym / sponsor ID
- SHOCKMATRIX / CRCBDD1712
- Conditions
- Wounds and Injuries, Traumatic Shock, Hemorrhagic Shock
- Keywords
- Predictive model, Ambispective validation, Machine learning, Missing values
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,584 participants (actual)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- Traumabase Group, Capgemini Invent, Ecole polytechnique, EHESS (Ecole des hautes études en sciences sociales), CNRS (Centre national de la recherche scientifique)
- Study start
- 1 Jul 2022 (actual)
- Primary completion
- 24 Jun 2024 (actual)
- Study completion
- 24 Jun 2024 (actual)
- First posted
- 21 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prehospital severe trauma patients | – | Other: Ambispective validation of machine learning-based predictive modelEvery 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (1 listed)
- every severe trauma adult patient to be admitted to a participating center
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ambispective validation of machine learning-based predictive modelRetrospective and prospective validation of a machine learning model to predict major haemorrhage in trauma patients compared to clinician prediction | Other | Prehospital severe trauma patients |
Primary outcome
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)
1 secondary outcome
Common binary classification metrics
Time frame: 18 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Feb 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Wounds and Injuries and Traumatic Shock, run by Assistance Publique - Hôpitaux de Paris at 8 sites in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Fβ-score, with β = 4 (time frame: 18 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Feb 2025.