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Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model

NCT06270615 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1584

Last updated 2025-02-18

No results posted yet for this study

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.

Conditions

  • Wounds and Injuries
  • Traumatic Shock
  • Hemorrhagic Shock

Interventions

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

Sponsors & Collaborators

  • Traumabase Group

    collaborator UNKNOWN

  • Capgemini Invent

    collaborator UNKNOWN

  • Ecole polytechnique

    collaborator UNKNOWN

  • EHESS (Ecole des hautes études en sciences sociales)

    collaborator UNKNOWN

  • CNRS (Centre national de la recherche scientifique)

    collaborator UNKNOWN

  • Assistance Publique - Hôpitaux de Paris

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-07-01
Primary Completion
2024-06-24
Completion
2024-06-24

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06270615 on ClinicalTrials.gov