Dynamic markers of intraoperative instability

描述（由申请人提供）：本研究的广泛、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接受复杂手术的高风险患者的管理。由于高心血管风险患者需要进行复杂的心血管手术，因此主要不良围手术期事件（MAE）（如卒中、心力衰竭和心肌梗死）的频率增加，并与住院时间延长、死亡率增加和医疗保健费用增加相关。传统的“静态”风险分层指标，如年龄或合并症，不能预测哪些患者有MAE风险，也不能提供个性化治疗策略的见解。这些方法没有考虑到个体专利中生理控制的波动，并且使用简化的线性模型，该模型不能捕获作为“真实世界”生理信号的标志的复杂的时变特征。该项目将应用最先进的非线性方法来实时术中血压信号，并创建一个新的动态指标集，便于早期检测微妙的术中血流动力学紊乱。通过将这些复杂的术中信号与MAE相关联（根据经确认的胸外科医师协会（STS）国家结局数据库确定），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，拟议方案的三个具体目标是：1）为了确定a）在手术的各个阶段对于给定个体BPV是否固定，B）BPV对心脏手术后MAE的预测能力2）检验BPV从基线到CP后B阶段的变化是否比基线或CP后B BPV更能预测MAE，并验证BPV对结局的预测能力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和术后结局数据），可通过NIH赞助的复杂生理信号PhysioNet研究资源（www.example.com）公开访问www.physionet.org。术中逐搏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结局数据库集成。将数据去识别和多尺度熵分析。熵数据将被测试其MAE的预测能力，并与传统的STS风险指数本身或作为其一部分进行比较。熵的范围，术后结果是最佳的将被确定，并作为指导未来的介入研究。所提出的动态方法为患者水平区分提供了一种有前途的解决方案;改善了患者咨询、术中血流动力学管理和术后结局。

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.