PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

项目摘要 心脏骤停-ICECAP(PRECICECAP)精确护理的目标是发现新的生物标志物 心跳骤停后脑和脑外器官衰竭的征象预测治疗反应 以及长期的复苏。我们将通过与ICECAP试验合作实现这一目标，ICECAP试验是一种反应适应剂量 寻找临床研究试验，寻求确定停搏后体温过低的最佳持续时间。心脏 逮捕是一个发病率和死亡率都很高的重大公共卫生问题。五分之四的患者住院治疗 心脏骤停后有明显的脑损伤，神经损伤死亡屡见不鲜。改进 生存和功能恢复是一项关键的公共卫生目标，需要创新的方法。 目前的临床情况是前所未有的。目前CA后脑损伤是一种急性、突发性危重 在如何最好地描述损伤的严重程度和确定哪些损伤的严重程度方面存在重大知识差距的疾病 个别患者可能从特定的神经保护策略中受益。因此，高技术的发展。 进行生物标记物签名是一项迫切的需求，并将转化为医疗保健的立即变化。我们 假设并非所有患者都是相同的(即治疗效果将存在异质性)，并且 通过我们创新的、多参数数据驱动的方法，我们将能够识别 定义患者的亚组。先进的数据科学和分析方法将使识别 这些小组以前是不可能的。这些亚群将具有重要的临床意义，因为它们 将显示对治疗的不同反应和不同的功能恢复轨迹。这个项目 将在疾病进程的早期获取高分辨率多模式数据，从而使我们能够解决这些问题 目前的知识差距和提高我们对疾病的理解在早期的急性背景下 干预可以改善结果。在这里学到的知识将被应用于开发个性化 心脏骤停幸存者的治疗，满足美国国立卫生研究院和我们延长生命和减少 残疾。

Project Summary The objective of PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) is to discover novel biomarker signatures of post cardiac arrest brain and extracerebral organ failure that predict treatment responsiveness and long-term recovery. We will achieve this by partnering with the ICECAP trial, a response-adaptive dose finding clinical research trial that seeks to determine the optimal duration of post-arrest hypothermia. Cardiac arrest is a major public health problem with high morbidity and mortality. Four in five patients hospitalized after cardiac arrest have significant brain injury, and death from neurologic damage is common. Improving survival and functional recovery is a critical public health objective and will require innovative approaches. The current clinical situation is unprecedented. Currently post-CA brain injury is an acute, sudden critical illness with major knowledge gaps about how best to characterize severity of injury and to identify which individual patients are likely to benefit from specific neuroprotective strategies. Thus, development of high- performing biomarker signatures is a critical need and would translate in to immediate changes in care. We hypothesize that not all patients are identical (i.e. there will be a heterogeneity of treatment effect) and that through our innovative, multi-parametric data driven approach, we will be able to identify novel signatures that define subgroups of patients. Advanced data science and analytical approaches will allow the identification of these subgroups that was previously not possible. These subgroups will be clinically important insofar as they will indicate differential responses to treatment and different trajectories of functional recovery. This project will acquire high resolution multi-modal data early in the disease course that will allow us to address these current knowledge gaps and improve our understanding of the disease in the early acute setting when interventions can improve outcome. The knowledge learned here will be applied to develop personalized treatments for cardiac arrest survivors, addressing the NIH's and our goals of lengthening life and reducing disability.