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

项目摘要/摘要 在美国，创伤是1-45岁儿童的主要死因，出血仍然是最大的死因 导致可预防死亡的因素。医疗服务提供者必须迅速识别那些遭受出血的人 优化结果，但内出血即使是有经验的临床医生也很难诊断。小才是 已知的隐匿性出血患者，必须迅速进行治疗 在这些时间紧迫、时间敏感的临床场景中的决策。这项提议寻求通过 人工智能，一种先进的机器学习，预测算法，可以部署 在患者床边协助临床医生更及时地认识出血情况。通过这样做， 我们假设，这种方法(集成以前未合并到 彼此)可以识别我们患者的模式，远远超过当前快速检测和采取行动的能力 关于对结果作出贡献的关键组成部分。能够快速定位这些图案并显示 将他们送到床边的临床医生那里，可以更及时地进行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外，目前对出血的治疗还处于初级水平 适用于所有患者的标准复苏方法。这反映了基于 平均治疗效果，而不是适应独特的患者表型。出血被认为是 启动一系列复杂的事件，涉及凝血和炎症系统之间的串扰， 被认为对结果起着关键作用。创伤有一个已知的发病时间零点，使其成为理想的模型 研究出血后即刻的病理生理变化。这位复杂的个体患者 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，到目前为止，这些 对个体特征了解甚少，在最初的治疗方法中没有考虑到这一点。通过这件事 提案中，我还试图定义代表患者表型的新型数字生物标记物，这些表型需要 精准复苏方法，以最大限度地提高结果。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性模型的更深层次的理解。有帮助的战略 识别可能受益于更定制的治疗途径的新的患者表型可能提供 在减少可预防死亡方面的重要进展。 这一提议的最终结果将是更深入地了解有助于 出血后患者状态的演变，并确定关键的表型或数字生物标记物 与死亡率、并发症和隐匿性出血有关。寻找解决方案来推进我们的 出血后的复苏方法有可能减少并发症，挽救生命，减少 医疗保健费用。

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.