Dynamic markers of intraoperative instability

描述(由申请人提供)：这项研究的广泛、长期目标是开发实时动态心血管风险指数，以帮助预测结果并指导正在进行复杂程序的高危患者的管理。随着心血管高危患者接受复杂的心血管手术，中风、心力衰竭和心肌梗死等围术期主要不良事件(MAE)的发生率增加，并与更长的住院时间、更高的死亡率和更高的医疗费用相关。传统的风险分层的“静态”指标，如年龄或合并症，不能预测哪些患者有患MAE的风险，也不能为个体化治疗策略提供见解。这些方法未能考虑到个别专利中生理控制的波动，并使用简化的线性模型，无法捕捉复杂的、时变的特征，而这些特征是“真实世界”生理信号的标志。该项目将把最先进的非线性方法应用于术中实时血压信号，并创建一套新的动态指标，有助于及早检测微妙的术中血流动力学障碍。通过将这些复杂的术中信号与MAE相关联，从经过验证的胸外科医生协会(STS)国家结果数据库确定MAE，将确定具有MAE预测值的血流动力学“特征”。为了实现这些目标，拟议计划的三个具体目标是：1)确定BPV在手术的不同阶段是否对特定个人是固定的，b)BPV对心脏手术后MAE的预测能力2)测试BPV从基线到CPB后期间的变化是否比基线或CPB后BPV更能预测MAE，并验证BPV对结果的预测能力3)创建一个独特的开放访问数据库(术前、术中血流动力学信号记录加上术后结果数据)，可通过NIH赞助的复杂生理信号的PhysioNet研究资源(www.Physionet.org)公开获得。术中逐次血流动力学数据将直接从手术室监护仪收集，并将与自动麻醉信息系统和STS结果数据库集成。利用多尺度熵对数据进行识别和分析。将测试熵数据的MAE预测能力，并将其与传统的STS风险指数单独或作为其一部分进行比较。术后结果最佳的熵范围将被确定并用作未来介入研究的指导。所提出的动态方法为区分患者级别提供了一个有希望的解决方案；改善了患者咨询、术中血流动力学管理和术后结果。

DESCRIPTION (provided by applicant): The broad, long-term objective of this research is to develop real-time dynamic cardiovascular risk indices to help predict outcomes and guide management of high-risk patients undergoing complex procedures. As high-cardiovascular risk patients present for complex cardiovascular surgery, the frequency of major adverse perioperative events (MAE) such as stroke, heart failure, and myocardial infarction has increased and is associated with longer hospitalization, increased mortality, and increased health care costs. Traditional "static" metrics of risk stratification, such as age or co morbid conditions, are not able to predict which patients are at risk for MAE or offer insights into individualized treatment strategies. These approaches fail to take into account fluctuations in physiologic control in individual patents and use simplified linear models that are unable to capture the complex, time varying features that are hallmarks of 'real-world' physiological signals. This project will apply state-of-the-art nonlinear methods to real-time intraoperative blood pressure signals and create a novel dynamical set of indices that facilitate early detection of subtle intraoperative hemodynamic disturbances. By relating these complex intraoperative signals to MAE, determined from the validated Society of Thoracic Surgeons (STS) National outcomes database, hemodynamic "signatures" with predictive value for MAE will be determined. To achieve these goals, the three specific aims of the proposed program are: 1) To determine a) if BPV is fixed for a given individual at various stages of surgery, b) BPV's predictive ability for postoperative MAE following cardiac surgery 2) To test the change in BPV from baseline to post-CPB periods as more predictive of MAE than either baseline or post-CPB BPV and to validate BPV's predictive ability of outcome 3) To create a unique open access database (preoperative, intraoperative hemodynamic signal recordings plus postoperative outcome data) publicly available via the NIH-sponsored PhysioNet Research Resource for Complex Physiologic Signals (www.physionet.org). The intraoperative beat-by-beat hemodynamic data will be collected directly from the operating room monitors and will be integrated with the automated anesthesia information systems and STS outcome database. The data will be deidentified and analyzed with multi scale entropy. The entropy data will be tested for its MAE predictive ability and compared to the traditional STS risk indices by itself or as a part of it. The entropy range at which the postoperative outcome is optimal will be determined and used as guidance for future interventional studies. The proposed dynamic approach offers a promising solution for patient level discrimination; improve patient counseling, intraoperative hemodynamic management and postoperative outcome.