基于EEG和HRV信息融合的复苏后亚低温治疗监测与优化关键技术研究

以神经功能恢复为主要目标的脑复苏是心搏骤停抢救与治疗的重要环节。现行的指南推荐对所有复苏后患者实施至少24小时、目标温度为32℃至36℃的亚低温治疗。但由于亚低温治疗的效果与心搏骤停的原因、停搏时长等因素密切相关，因此对不同患者实施目标温度管理的具体方案也应有所不同。如何准确地评估疗效并实施个性化的目标温度管理，是危重症医学面临的一项难题。本项目在研究发现EEG和HRV的分析指标可以预测脑复苏的预后并且对亚低温治疗非常敏感的基础上，提出一种基于EEG和HRV信息融合的复苏后亚低温治疗监测与优化研究方案：首先通过动物实验阐明复苏后EEG和HRV特征的变化规律，提取并筛选与脑损伤程度及预后相关的特征参数；然后研究亚低温治疗过程中这些参数的动态变化与疗效之间的关系；最后运用D-S决策理论构建可用于预后评估与疗效监测的数学模型及优化目标温度管理的决策算法，为实施精准化的脑复苏提供可行的技术方案。

The neurological function recovery targeted cerebral resuscitation is an important link for the treatment of cardiac arrest. Current Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care recommend at least 24 hours of therapeutic mild hypothermia with targeted temperature management of 32℃ to 36℃ for all patients who are resuscitated from cardiac arrest. However, the targeted temperature management and duration of therapeutic mild hypothermia need to be optimized because of the severity of brain injury is correlated with causes of cardiac arrest, duration of untreated arrest and individual tolerance to ischemia/reperfusion. Therefore, how to accurately assess the prognosis of cerebral resuscitation and to perform individualized therapeutic mild hypothermia is still a big challenge in the field of critical care medicine. Based on the observations that characteristics of EEG and HRV can predict neurological outcome and these characteristics are very sensitive to targeted temperature management, we hypothesize that combination of EEG and HRV analysis could be used to monitor the effects of post resuscitation therapeutic mild hypothermia and to optimize targeted temperature managements. The proposed project aims to explore characteristics of earlier post-resuscitation EEG and HRV using different animal models of cardiac arrest; to extract quantitative features relating to severity of brain injury and prognosis of neurological outcome; to investigate the relationship between changes of these quantitative features and curative effects during therapeutic mild hypothermia with different targeted temperature managements and different durations; to develop a mathematical model and decision algorithm for prognosis evaluation, curative effects monitoring and optimization of targeted temperature managements with the use of D-S decision theory; and to provide a feasible technical solution for the implementation of precision brain resuscitation.