Leveraging Artificial Intelligence Solutions to Develop Digital Biomarkers for Precision Trauma Resuscitation

利用人工智能解决方案开发用于精准创伤复苏的数字生物标记物

• 批准号: 10063555
• 负责人: Rachael A Callcut
• 金额: $ 77.25万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063555
• 关键词: Abdomen; Abdominal Cavity; Address; Area; Artificial Intelligence; Bedside Technology; Biological; Biological Markers; Biology; Blood; Caring; Cause of Death; Cessation of life; Characteristics; Clinical; Coagulation Process; Complex; Computational Science; Computational algorithm; Computerized Medical Record; Computers; Coupled; Coupling; Data; Data Sources; Detection; Devices; Diagnosis; Diagnostic; Early Diagnosis; Early Intervention; Event; Foundations; Goals; Health Care Costs; Hemorrhage; Image; Individual; Inflammatory; Injury; Intervention; Knowledge; Lead; Life; Liquid substance; Machine Learning; Mentored Research Scientist Development Award; Modeling; Outcome; Outcome Measure; Pathway interactions; Patients; Pattern; Phenotype; Physiologic Monitoring; Physiology; Play; Provider; Resuscitation; Risk; Stream; System; Techniques; Technology; Therapeutic; Time; Trauma; Trauma patient; Traumatic injury; Triage; Ultrasonography; United States National Institutes of Health; Work; advanced analytics; base; care providers; clinical decision-making; clinical predictors; clinically significant; deep learning; detection platform; digital; diverse data; experience; improved; individual patient; individualized medicine; insight; mortality; multimodal data; novel; point of care; prediction algorithm; predictive modeling; pressure; preventable death; profiles in patients; relating to nervous system; sensor; severe injury; treatment effect

PROJECT SUMMARY / ABSTRACT In the U.S., trauma is the leading cause of death for those 1-45 years old and hemorrhage remains the largest contributing factor to preventable death. Providers must rapidly identify those suffering from hemorrhage to optimize outcome, but internal bleeding remains difficult to diagnose even for experienced clinicians. Little is known on presentation about those suffering from occult hemorrhage and providers must quickly make treatment decisions in these time-pressured, time-sensitive clinical scenarios. This proposal seeks to develop through artificial intelligence, a type of advanced machine learning, prediction algorithms that could be deployed at the bedside of patients to assist clinicians with more timely recognition of hemorrhage. By doing so, we hypothesize that this approach (integrating diverse data sources that have not previously been combined to one another) could identify patterns in our patients that far surpass current capabilities to quickly detect and act on the critical components contributing to outcome. The ability to rapidly pinpoint these patterns and display them to the bedside clinician could allow more timely intervention and precise therapeutic approaches for hemorrhage control. Beyond the challenges in rapidly identifying bleeding, current treatment of hemorrhage is rudimentary with a standard resuscitation approach for all patients. This reflects attempts to optimize outcome based upon the average treatment effect, rather than being adaptable for unique patient phenotypes. Hemorrhage is believed to initiate a complex chain of events involving crosstalk between the coagulation and inflammatory systems that are hypothesized to play a key role in outcome. Trauma has a known time zero of onset, making it an ideal model to study the immediate pathophysiologic changes associated with hemorrhage. This complex, individual patient biology is believed to explain why those suffering similar injury have differing outcomes. However, to date, these individual characteristics are poorly understood and not factored into initial treatment approaches. Through this proposal, I also seek to define novel digital biomarkers representing patient phenotypes that require precision resuscitation approaches to maximize outcome. Fundamental to reducing hemorrhagic deaths is the need to elucidate a deeper understanding of these mechanistic models of patient states. Strategies that help to identify novel patient phenotypes that could benefit from more tailored treatment pathways may provide important advances in decreasing preventable death. The net result of this proposal will be a deeper insight into the mechanistic models contributing to evolving patient states following hemorrhage, and identify the key phenotypes or digital biomarkers associated with mortality, complications, and occult hemorrhage. Finding solutions to advance our resuscitation approaches following hemorrhage has potential to decrease complications, save lives, and reduce health care costs.

项目总结/摘要 在美国，创伤是1-45岁人群的主要死因，出血仍是最大的死因 导致可预防死亡的因素。提供者必须迅速识别出血患者， 但是即使对于有经验的临床医生来说，内出血仍然很难诊断。之甚少 在介绍中了解那些患有隐性出血的人，提供者必须迅速进行治疗 在这些时间紧迫、时间敏感的临床情况下做出决定。该提案旨在通过 人工智能，一种先进的机器学习，可以部署的预测算法， 帮助临床医生更及时地识别出血。通过这样做， 我们假设这种方法（整合以前没有组合的各种数据源， 彼此）可以识别我们患者的模式，远远超过目前快速检测和采取行动的能力 对结果有贡献的关键要素。能够快速地确定这些模式并显示 他们的床边临床医生可以允许更及时的干预和精确的治疗方法， 出血控制。 除了快速识别出血的挑战之外，目前的出血治疗是初级的， 所有病人的标准复苏方法。这反映了基于以下方面优化结果的尝试： 平均治疗效果，而不是适应于独特的患者表型。出血被认为是 引发一系列复杂的事件，包括凝血系统和炎症系统之间的串扰， 被假设为在结果中起关键作用。创伤有一个已知的时间零点，使其成为理想的模型 研究与出血相关的即刻病理生理学变化。这个复杂的个体病人 生物学被认为可以解释为什么那些遭受类似伤害的人会有不同的结果。然而，迄今为止，这些 人们对个体特征知之甚少，也没有将其纳入初始治疗方法。通过这个 此外，我还试图定义代表患者表型的新型数字生物标志物， 精确的复苏方法来最大化结果。减少出血性死亡的根本是 需要阐明对患者状态的这些机械模型的更深入理解。帮助的策略 识别新的患者表型，可以从更定制的治疗途径中受益， 在减少可预防的死亡方面取得了重大进展。 这一建议的最终结果将是对有助于实现以下目标的机制模型的更深入的了解： 出血后患者状态的演变，并确定关键表型或数字生物标志物 与死亡率、并发症和隐性出血相关。寻找解决方案， 出血后的复苏方法有可能减少并发症，挽救生命， 医疗保健费用。