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

使用机器学习驱动的微凝块成像快速检测全血样本中的血小板功能障碍。

• 批准号: 10621281
• 负责人: Lucas H Ting
• 金额: $ 40.91万
• 依托单位: STASYS MEDICAL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-19 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10621281
• 关键词: ADP Receptors; Address; Adhesions; Algorithmic Analysis; Algorithms; Arachidonic Acids; Biological Assay; Blood; Blood Cells; Blood Platelets; Blood Tests; Blood Transfusion; Blood coagulation; Blood specimen; Caring; Cause of Death; Clinical; Clinical Trials; Coagulation Process; Color; Complex; Contracts; Critical Illness; Data; Detection; Devices; Electrons; Emergency Department patient; Engineering; Enrollment; Freezing; Functional disorder; Hemorrhage; Hemostatic function; Hospitals; Image; Image Analysis; Intracranial Hemorrhages; Machine Learning; Measurement; Measures; Mechanics; Medicine; Microfluidics; Monitor; Morbidity - disease rate; Optics; Outcome; Output; Pathway interactions; Patients; Performance; Phase; Platelet Function Tests; Platelet Transfusion; Platelet aggregation; Point of Care Technology; Positioning Attribute; Preparation; Prospective cohort; Receptor Inhibition; Reporting; Research Design; Risk; Scanning; Structure; Technology; Testing; Time; Training; Transfusion; Trauma; Trauma patient; Traumatic Brain Injury; Traumatic injury; Traumatic intracranial hemorrhage; Update; Validation; Whole Blood; Work; blood product; clinical application; clinical decision-making; clinical practice; cohort; design; diagnostic assay; diagnostic technologies; disability; flexibility; fluorescence imaging; force sensor; improved; innovation; insight; machine learning algorithm; machine learning model; machine vision; microsensor; mortality; nanonewton; novel; outcome prediction; performance tests; platelet function; point of care; point of care testing; prediction algorithm; prospective; seal; sensor; severe injury; shear stress; trauma care; wound

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).

抽象的 该项目将优化用于临床应用的即时护理（POC）血小板力监测技术 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对止血至关重要 伤口处的粘附、聚集和收缩。创伤后血小板经常变得功能障碍 加重内出血和脑出血进展并增加发病率。尚未进行 POC 血小板检测 纳入实践的原因是：1）缺乏大队列急诊患者测试； 2）输血预测准确性差 3) 延长处理时间。我们开发了一种创新的 POC 技术来测试血小板功能 直接测量微流体力传感器上的血小板收缩力。我们的 POC 测试与其他测试相比的优势 现有检测：1) 快速、直接激活和测量血小板功能，2) 创新机器视觉 具有机器学习洞察力的深厚潜力。然而，该技术需要在一定范围内进行优化和验证 大量主要 ED 创伤队列，并且在 ICH 后仍未经过测试。我们的试验数据表明血小板收缩力 对一系列相关的激活途径和机制敏感，并且力在 需要输血的外伤患者。此外，在之前的临床试验中，已发现血小板输注可以 滥用时会产生危害。基于这一未满足的科学需求，我们将确定我们的 POC 技术可以预测创伤患者的出血并发症，为个性化输血提供信息 战略。我们的首要假设是我们的 POC 血小板力监测技术是一种有效的指标 创伤和脑出血后的出血并发症。目标 1：优化血小板力监测光学器件以提高 血小板力传感器性能。我们假设添加第二个荧光成像通道可以 提高我们当前的血小板力传感器性能。目标 2：使用机器学习 (ML) 图像分析 改善血小板功能障碍的检测和创伤结果的预测。我们假设基于图像 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 （目标 1）与用于检测血小板功能障碍和预测结果的 ML 增强测量相比。目标 3： 验证我们的血小板功能算法，以预测输血需求、死亡率和 严重受伤的 ED 创伤患者的前瞻性队列中创伤性 ICH 的进展。我们 假设血小板力将成为输血需求、死亡率和病情进展的有力预测因子 ICH。在 ED 中，我们将应用我们的算法（目标 1 中的原始算法和优化算法）到来自 创伤患者并将预测的输血需求与实际输血进行比较 (Aim-3a) 和 测量测量的血小板力与 ICH 进展之间的关联 (Aim-3b)。