Wounds and Injuries Clinical Trial
— SHOCKMATRIXOfficial title:
External Validation of a Real-time Machine Learning-based Predictive Model for Early Severe Hemorrhage and Hemorrhage Resource Needs in Trauma Patients
NCT number | NCT06270615 |
Other study ID # | CRCBDD1712 |
Secondary ID | |
Status | Recruiting |
Phase | |
First received | |
Last updated | |
Start date | July 1, 2022 |
Est. completion date | June 2024 |
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.
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 |
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 |
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 |
France,
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 |
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