Hemorrhage with trauma‑induced coagulopathy (TIC) is a major cause of preventable death in trauma. Early blood product use is key, but identifying patients needing massive transfusions, or at risk for TIC is challenging, especially in austere settings. AI and ML models show promise in predicting transfusion needs from trauma data but are mostly retrospective, binary, and triage focused. Many of these models are considered pre prospective data validation prototypes, defined as “Phase I, while few progress to Phase II, which involves validation and implementation.
This funding opportunity seeks “Phase II” studies to prospectively validate, which would include Phase I prototype model validation, refinement with prospective data, as well as integrating real-time data streams with Point-of-Care (POC) systems, such as ROTEM (Rotational Thromboelastometry), FloPatch, POC INR (International Normalized Ratio). Proposals should focus on exploring and assessing the limitations of these models via validation, as well as identifying methods to improve, refine, and generalize them, thereby contributing to broader efforts in medical translational research.