R01 Administrative Supplement for AI Prediction of Trauma Resuscitation Responsiveness

父R01项目摘要 创伤患者的最初复苏通常被描述为混乱，临床医生指导 在做出生死决定时，护理必须创造平静，往往是不充分的 信息。尽管在理解出血的生物学方面取得了一些进展，但损伤仍然 每年造成500多万人死亡，相当于全球每10人中就有1人死亡 仍然是美国45岁以下人群的主要死因。而90%的创伤患者会这样做 那么，对可预防的死亡的最大贡献仍然是那些遭受出血的人 及其相关的并发症。创伤有一个已知的发病时间零点，这使它成为理想的 研究与出血相关的即刻病理生理变化的模型 不同的结果。到目前为止，治疗途径被认为是反映尝试的基本途径。 根据平均治疗效果来优化结果，而不是适应于 独特的患者表型。母公司R01提案专注于探索更深层次的 理解初始患者对损伤的反应的机械模拟(目标1)和 这与改进的实时床边决策支持技术相结合(目标2) 以便及早识别那些面临不良结局风险的人。父建议书的净产品是定义 新的患者表型可能需要精确的复苏方法来最大限度地 出血后的结局。母公司R01的目标是开发数字生物标记物 精确创伤复苏，重点是了解患者之间的串扰 作为对创伤的全身反应而发生的炎症和凝血情况。这个 父R01项目的总体目标保持不变，并解决我们的 通过改进对护理点患者结局轨迹的预测来获得当前知识 那些遭受创伤性损伤的人。父R01项目通过两个项目解决了这些差距 相互关联的目标也保持不变： 目的1.开发一种知识网络(神经网络)方法来表征早期 病人在出血后的轨迹。我们假设(1a)可预测的轨迹 死亡率和并发症可通过深度学习方法确定；(1B)增加 生物学数据(炎症和凝血标志物)将进一步改善对患者状态的预测 或独特的表型患者档案(也称为数字生物标记物)可归因于差异 结果，和(1C)这些表型可以被用来改善对患者的早期识别。 预测轨迹，从而优化分诊和治疗路径。 目的2.开发用于检测隐匿性出血的初步预测模型 集成高保真、集成的护理点数据和床边成像。我们 假设对隐匿性出血的预测敏感性较差可以通过开发 (2a)使用高级机器学习的自动计算机算法，以检测 通过集成多模数据源在护理点超声图像(2B)上显示腹部 有了2A，我们可以预测临床上有意义的隐性出血，以及(2C)结合成像 利用目标1中的知识网络建立预测模型，我们可以开发试点增强的数字 用于识别临床隐匿性出血患者的生物标志物，这些患者最有可能发生 并发症，以及那些最有可能因伤而死亡的人。 到目前为止，我们对亲本R01的研究表明，存在不同的血栓形成风险， 基于初始凝血谱3-4的出血、呼吸和死亡率结果。我们的发现 都表明生物学在区分两种表面上看起来 重伤后结局不同的相同患者3-19。这包括 表明严重受伤的患者最初会因纤溶功能亢进而出血， 血小板功能障碍和凝血功能紊乱。那些幸存下来的人很快就会过渡到一个国家 高凝状态。这种情况的机制仍然难以捉摸，但却引发了混乱的炎症 细胞因子上调已被牵连，我们继续在亲本R01中探索这一点。

PARENT R01 PROJECT ABSTRACT The initial resuscitation of a trauma patient is often described as chaos and the clinician directing the care must create calm while making life and death decisions often with inadequate information. Despite some advances in understanding the biology of hemorrhage, injury still accounts for over 5 million deaths per year, represents 1 out of every 10 deaths worldwide, and remains the leading U.S. cause of death for those under 45. While > 90% of trauma patients do well, the largest contribution to preventable death remains for those suffering from hemorrhage and its related complications. Trauma has a known time zero of onset which makes it an ideal model to study the immediate pathophysiologic changes associate with hemorrhage that lead to differential outcome. To date, treatment pathways are considered rudimentary reflecting attempts to optimize outcome based upon the average treatment effect, rather than being adaptable for unique patient phenotypes. The parent R01 proposal is focused on exploring a deeper understanding of the mechanistic modeling of initial patient response to injury (Aim 1) and coupling this with improved real-time point of care bedside decision support technology (Aim 2) to identify early those at risk of poor outcome. The net product of the parent proposal is to define novel patient phenotypes that may require precision resuscitation approaches to maximize outcome following hemorrhage. The goal of the parent R01 is to develop digital biomarkers for precision trauma resuscitation with a focus on understanding the cross talk between the inflammatory and coagulation profiles that occur as a systemic response to traumatic injury. The overall goal of the parent R01 project remains unchanged and are to address limitations of our current knowledge by improving forecasts of patient outcome trajectory at the point of care for those suffering from traumatic injury. The parent R01 project addresses these gaps through two interrelated aims which also remain unchanged: AIM 1. To develop a knowledge network (neural net) approach for characterizing early patient trajectory following hemorrhage. We hypothesize that (1A) predictive trajectories for mortality and complications can be ascertained through deep learning approaches; (1B) the addition of biologic data (inflammatory and coagulation markers) will further improve the prediction of patient states or unique phenotypic patient profiles (also known as digital biomarkers) attributable to differential outcome, and (1C) these phenotypes could be utilized to improve earlier recognition of patients off their predicted trajectory and thus, optimize triage and treatment pathways. AIM 2. To develop pilot prediction models for the detection of occult hemorrhage through the integration of high fidelity, integrated point-of-care data and bedside imaging. We hypothesize that the poor sensitivity for prediction of occult hemorrhage can be improved by developing (2A) an automated computer algorithm using advanced machine learning to detect any free fluid in the abdominal cavity on point- of-care sonographic images (2B) by integrating multimodality data sources with 2A, we can predict clinically significant occult hemorrhage, and (2C) combining the imaging prediction models with the knowledge network in Aim 1, we can develop pilot enhanced digital biomarkers to identify patients suffering from clinically occult hemorrhage, those most likely to develop complications, and those most likely to succumb to their injuries. Our work to date on the Parent R01 has shown that there is differential risk of thrombotic, bleeding, respiratory, and mortality outcomes based on initial coagulation profiles3-4. Our findings have suggested that biology plays a major factor in distinguishing between two seemingly identical patients who have disparate outcomes following critically injury3-19. This includes demonstrating that severely injured patients initially suffer from bleeding due to hyperfibrinolysis, platelet dysfunction, and disordered coagulation. Those who survive rapidly transition to a state of hypercoagulability. The mechanisms for this remains elusive but disordered inflammatory cytokine upregulation has been implicated and we continue to explore this in the parent R01.