Rapid platelet dysfunction detection in whole blood samples using machine learning powered micro-clot imaging.

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Abstract This project will optimize a point-of-care (POC) platelet force monitoring technology for clinical application in trauma care. The leading causes of death and disability after trauma are related to hemorrhage and traumatic brain injury with intracranial hemorrhage (ICH). Platelets are critical to hemostasis by inducing clot formation via adhesion, aggregation, and contraction at wounds. Platelets often become dysfunctional after trauma which worsens internal bleeding and ICH progression and increasing morbidity. POC platelet assays have not been incorporated in practice due to:1) lack of large cohort ED patient testing; 2) poor accuracy in transfusion prediction and 3) extended processing times. We have made an innovative POC technology to test platelet function by directly measuring platelet contractile forces on microfluidic force sensors. Advantages of our POC test vs. existing assays: 1) rapid, direct activation and measures of platelet functions and 2) innovative machine vision with deep potential for machine learning insight. However, this technology needs optimization and validation in a large major ED trauma cohort and remains untested after ICH. Our pilot data suggests platelet contractile forces are sensitive to a range of relevant activation pathways and mechanisms and force is significantly decreased in trauma patients requiring blood transfusion. Further, in prior clinical trials, platelet transfusion has been found to be harmful when used indiscriminately. Building on this unmet scientific need, we will determine if our POC technology is predictive of hemorrhagic complications in trauma patients, informing a personalized transfusion strategy. Our overarching hypothesis is our POC platelet force monitor technology is an efficient indicator of bleeding complications after trauma and ICH. Aim 1: Optimize the platelet force monitor optics to improve platelet force sensor performance. We hypothesize the addition of a second fluorescent imaging channel can improve our current platelet force sensor performance. Aim 2: Use machine learning (ML) image analysis to improve detection of platelet dysfunction and prediction of trauma outcomes. We hypothesize image-based ML models can improve test performance. We will compare the accuracy of direct platelet force measurements (Aim 1) vs. ML-enhancement measurements for detecting platelet dysfunction and predicting outcomes. Aim 3: Validate our platelet function algorithm for predicting blood transfusion needs, mortality, and the progression of traumatic ICH in a prospective cohort of severely-injured ED trauma patients. We hypothesize platelet force will be a powerful predictor of blood transfusion needs, mortality, and progression of ICH. In the ED we will apply our algorithm (both the original and optimized algorithm from Aim 1) to blood from trauma patients and compare the predicted transfusion requirements against actual transfusion (Aim-3a) and measure the association between measured platelet force and ICH progression (Aim-3b).