Exploring the Feasibility of Computational Markers to Predict Atrial Fibrillation

描述(申请人提供)：术后房颤(PAF)影响心脏手术患者的很大一部分。它与术后死亡率和发病率的增加有关，也会导致住院时间更长、费用更高。虽然预防性应用β-肾上腺素能阻滞剂和胺碘酮等治疗可以减少PAF的发生率，但现有工具需要改进，以便将患者与适合其风险的治疗相匹配。许多高危患者可能会受益于比他们目前接受的更积极的治疗。相反，许多低风险患者目前接受的治疗超过了他们发生PAF的实际风险。我们研究的目的是开发新的计算标记物，可用于对因PAF而接受心脏手术的患者进行风险分层。我们的研究使用复杂的信号处理和机器学习技术来发现心电信号中与自主神经和脉冲传导异常相关的信息。我们描述了如何将这些信息与现有指标相结合来识别高危患者。此外，我们建议创建一个包含详细的患者元数据和结果的大型公共心电信号数据集，以供更广泛的科学界研究预测PAF。这项建议的具体目标是：(1)开发一个公共数据库，其中包括心电信号和详细的患者元数据，用于预测PAF的研究。我们将收集这个项目前12个月在密歇根大学医学中心接受心脏手术的900多名患者的心电数据，预计PAF的发生率约为30%。我们将通过PhysioNet以非识别的方式与更广泛的研究社区共享这些数据和其他临床信息(即人口统计数据、合并症、实验室报告、与手术相关的信息以及PAF的结果)；以及(2)开发和验证PAF基于心电图的新风险标记物。我们将探索基于心脏交感迷走神经调制的指标，例如心率震荡(HRT)和减速能力(DC)，它们最近在预测室性心律失常方面显示出很好的前景。我们还将开发基于心电形状的新指标来评估心房心肌不稳定。这些方法将在AIM 1收集的数据上得到验证，并将用于开发基于非参数机器学习技术的模型，以准确评估PAF风险。

DESCRIPTION (provided by applicant): Postoperative atrial fibrillation (PAF) affects a large fraction of patients undergoing cardiac surgery. It is associated with increased postoperative mortality and morbidity, and also results in longer and more expensive hospital stays. While treatments such as prophylactic administration of beta-adrenergic blockers and amiodarone can reduce the incidence of PAF, existing tools to match patients to treatments that are appropriate for their risk need to be improved. Many high-risk patients may benefit from more aggressive treatment than they receive presently. Conversely, many low-risk patients are currently treated in excess of their actual risk of developing PAF. The aim of our research is to develop novel computational markers that can be used to risk stratify patients undergoing cardiac surgery for PAF. Our research uses sophisticated signal processing and machine learning techniques to discover information in the electrocardiogram (ECG) signal related to autonomic and impulse conduction abnormalities. We describe how this information can be combined with existing metrics to identify high risk patients. In addition, we propose the creation of a large public dataset of ECG signals with detailed patient meta-data and outcomes for research by the broader scientific community on predicting PAF. The specific aims of this proposal are: (1) To develop a public database with ECG signals and detailed patient metadata for research on predictive PAF. We will collect ECG data from over 900 patients undergoing cardiac surgery at the University of Michigan Medical Center during the first 12 months of this project with an expected PAF incidence of around 30%. We will share this data and other clinical information (i.e., demographics, comorbidities, laboratory reports, information related to the procedure, and the outcome of PAF) in a de- identified manner with the broader research community through PhysioNet; and (2) To develop and validate novel ECG-based risk markers for PAF. We will explore metrics based on the sympathovagal modulation of the heart, e.g., heart rate turbulence (HRT) and deceleration capacity (DC), which have shown recent promise in predicting ventricular arrhythmias. We will also develop new metrics based on the shape of the ECG to assess atrial myocardial instability. These approaches will be validated on the data collected in Aim 1, and will be used to develop models based on non-parametric machine learning techniques to accurately assess PAF risk.