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

摘要 该项目将优化床旁（POC）血小板力监测技术，用于临床应用， 创伤护理创伤后死亡和残疾的主要原因与出血和创伤有关。 脑损伤伴颅内出血（ICH）。血小板通过诱导凝块形成而对止血至关重要， 伤口处的粘附、聚集和收缩。创伤后，血小板通常会出现功能障碍， 颅内出血和ICH进展以及发病率增加。POC血小板测定尚未 由于以下原因纳入实践：1）缺乏大型队列艾德患者检测; 2）输血预测准确性差 以及3）延长的处理时间。我们开发了一种创新的POC技术，通过以下方式测试血小板功能： 在微流体力传感器上直接测量血小板收缩力。我们的POC测试与 现有的检测方法：1）快速、直接激活和测量血小板功能; 2）创新的机器视觉 在机器学习方面有很大的潜力然而，这项技术需要在一个 大型严重艾德创伤队列，ICH后仍未进行测试。我们的试验数据表明血小板收缩力 对一系列相关激活途径和机制敏感， 需要输血的创伤患者。此外，在先前的临床试验中，已发现血小板输注 不加区别地使用是有害的。基于这一未满足的科学需求，我们将确定我们的POC 技术可预测创伤患者的出血并发症，为个性化输血提供信息 战略我们的总体假设是我们的POC血小板力监测技术是一个有效的指标， 创伤和ICH后出血并发症。目的1：优化血小板力监测器光学器件， 血小板力传感器性能。我们假设增加第二个荧光成像通道可以 改进了我们当前板力传感器性能。目标2：使用机器学习（ML）图像分析， 改善血小板功能障碍检测和创伤结果的预测。我们假设基于图像的 ML模型可以提高测试性能。我们将比较直接血小板力测量的准确性 (Aim 1）与ML增强测量检测血小板功能障碍和预测结果。目标3： 血小板功能算法用于预测输血需求、死亡率和 创伤性脑出血进展的前瞻性队列研究--创伤性艾德患者我们 假设血小板作用力将是输血需求、死亡率和 ICH。在艾德中，我们将我们的算法（目标1中的原始算法和优化算法）应用于血液， 创伤患者，并将预测的输血需求与实际输血进行比较（Aim-3a）， 测量测量的血小板力与ICH进展之间的关联（Aim-3b）。

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).