Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model
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.
- EligibilityCriteria: 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 - HealthyVolunteers: No - Gender: All - MinimumAge: 18 Years - StdAgeList: Adult, Older Adult - StudyPopulation: 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 - SamplingMethod: Non-Probability Sample