Exploring the Feasibility of Computational Markers to Predict Atrial Fibrillation

探索计算标记物预测心房颤动的可行性

• 批准号: 8242479
• 负责人: Satinder Singh Baveja
• 金额: $ 21.36万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-06 至 2014-06-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8242479
• 关键词: Address; Adrenergic beta-Antagonists; Adverse effects; Affect; Amiodarone; Arrhythmia; Atrial Fibrillation; Blinded; Bradycardia; Cardiac Surgery procedures; Clinical; Coagulation Process; Communities; Comorbidity; Coronary Artery Bypass; Data; Data Set; Databases; Deceleration; Electrocardiogram; Equilibrium; Event; Heart; Heart Atrium; Heart Rate; Hospitalization; Incidence; Institutional Review Boards; Laboratories; Length of Stay; Machine Learning; Medical center; Metadata; Methods; Metric; Michigan; Modeling; Morbidity - disease rate; Myocardial; Operative Surgical Procedures; Outcome; Outcomes Research; Patients; Performance; Physicians; Population; Postoperative Period; Prevention; Procedures; Prophylactic treatment; Reporting; Research; Research Activity; Research Personnel; Risk; Risk Estimate; Risk Marker; Shapes; Signal Transduction; Techniques; Universities; Ventricular Arrhythmia; Work; base; clinical practice; computerized data processing; cost; data sharing; demographics; high risk; improved; interest; millisecond; mortality; novel; prevent; prophylactic; repaired; tool; valve replacement

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.

描述（由申请人提供）：术后房颤（PAF）影响大部分接受心脏手术的患者。它与术后死亡率和发病率增加有关，还导致住院时间更长，费用更高。虽然预防性给予β-肾上腺素能受体阻滞剂和胺碘酮等治疗可以降低PAF的发生率，但需要改进现有的工具，使患者与适合其风险的治疗相匹配。许多高风险患者可能受益于比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图（ECG）信号中与自主神经和冲动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，以识别高风险患者。此外，我们建议创建一个大型的ECG信号公共数据集，其中包含详细的患者元数据和结果，供更广泛的科学界预测PAF的研究。该提案的具体目标是：（1）开发一个包含ECG信号和详细患者元数据的公共数据库，用于预测PAF的研究。我们将在本项目的前12个月内收集密歇根大学医学中心900多名接受心脏手术的患者的ECG数据，预计PAF发生率约为30%。我们将分享这些数据和其他临床信息（即，人口统计学、合并症、实验室报告、与手术相关的信息以及PAF的结局），通过PhysioNet与更广泛的研究群体进行去识别化;以及（2）开发并验证PAF的新型基于ECG的风险标志物。我们将探索基于心脏的交感迷走神经调制的指标，例如，心率震荡（HRT）和减速能力（DC），最近显示出预测室性心律失常的前景。我们还将开发基于ECG形状的新指标，以评估心房心肌不稳定性。这些方法将在目标1中收集的数据上进行验证，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。