Prospective Validation of the SHOCKMATRIX Hemorrhage Predictive Model

Hemorrhagic Shock, Traumatic Shock, Wounds and Injuries

Predictive model, Ambispective validation, Machine learning, Missing values

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.

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.

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