Automated detection and prediction of atrial fibrillation during sepsis

脓毒症期间心房颤动的自动检测和预测

• 批准号: 9283910
• 负责人: Allan J. Walkey
• 金额: $ 54.45万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9283910
• 关键词: Affect; Algorithmic Analysis; Algorithms; American; Antibiotics; Arrhythmia; Atrial Fibrillation; Big Data; Cardiac; Cardiac Surgery procedures; Cardiovascular system; Cessation of life; Characteristics; Clinical; Clinical Research; Clinical Treatment; Comorbidity; Complication; Computer Assisted; Critical Illness; Data; Databases; Detection; Development; Early Diagnosis; Electrolytes; Electromagnetics; Electronic Health Record; Evidence based treatment; Functional disorder; Future; Gold; Grant; Heart Abnormalities; Heart Atrium; Heart Rate; Heart failure; Hospitalization; Hospitals; Hour; Infection; Intensive Care; Intervention; Investigation; Knowledge; Laboratories; Life; Link; Liquid substance; Machine Learning; Manuals; Methods; Modeling; Monitor; Morbidity - disease rate; Morphologic artifacts; Motion; Myocardial dysfunction; Noise; Organ; Outcome; Patient risk; Patient-Focused Outcomes; Patients; Physiologic pulse; Prevention; Preventive therapy; Quality of life; Research; Resources; Resuscitation; Risk; Risk Factors; Sepsis; Shock; Stroke; Stroke Volume; Subgroup; Technology; Telemetry; Time; United States; United States National Institutes of Health; Variant; base; clinical predictors; electronic data; heart rhythm; hemodynamics; high risk; improved; improved outcome; innovation; learning strategy; mortality; novel; portability; predictive signature; prevent; response; septic; therapeutic target; time use; tool; treatment strategy

7. ABSTRACT / PROJECT SUMMARY We propose the “Automated detection and prediction of atrial fibrillation during sepsis” study to develop automated technologies capable of accurate atrial fibrillation (AF) detection and prediction during sepsis. Sepsis is a life-threatening, dysregulated response to infection and the most common illness leading to hospitalization in the United States, affecting ~1 million Americans yearly, and is associated with 50% of all hospital deaths. With the exception early antibiotic and fluid use, few therapies improve outcomes among septic patients; new treatment strategies are greatly needed to improve survival. New-onset AF is a common dysrhythmia among critically ill patients with sepsis, affecting up to 1 in 3 septic patients and conferring increased short- and long-term risks stroke, heart failure, and death. Prevention of AF or its complications may improve sepsis outcomes by reducing AF-related morbidity and mortality. Although several evidence-based treatments have shown efficacy in treating and preventing AF in certain high-risk subgroups (e.g., AF prevention following cardiac surgery), studying application of these therapies among critically ill patients with sepsis has been hampered by two major factors: 1) we lack validated automated mechanisms to detect AF and facilitate real-world AF research in large clinical databases, and 2) we cannot presently predict which patients with sepsis will develop AF. Our project will leverage the unique resources of the recently released Multiparameter Intelligent Monitoring in Intensive Care (MIMIC III) database. MIMIC III links continuous ECG and pulse plethysmographic waveforms to a wealth of time-varying clinical and hemodynamic data. Our project will develop and validate state-of-the art automated AF detection algorithms using waveform data from critically ill patients. Automated AF detection would enable expedited clinical treatment of AF, identification of subclinical AF, and will catalyze the study of AF in emerging electronic health record waveform databases. We will develop innovative automated AF prediction capabilities using state-of-the-art waveform analysis algorithms and machine learning methods in critically ill patients. Automated algorithms that identify patients at high risk for developing AF in the near-term would enable targeting of preventative therapies and potentially usher in a new era of AF prevention for critically ill patients. AF prevention and treatment facilitated through our project will allow targeting of novel, AF-based mechanisms of poor outcomes during and following sepsis.

7。摘要 /项目摘要 我们提出了“败血症期间心房颤动的自动检测和预测”研究 能够在败血症期间能够准确的心房颤动（AF）检测和预测的自动化技术。 败血症是一种威胁生命的，对感染的反应失调，是最常见的疾病，导致 在美国的住院，每年影响约100万美国人，与所有人的50％有关 医院死亡。除早期抗生素和液体使用外，很少有疗法改善预后 化粪池患者；非常需要新的治疗策略来改善生存。新的AF是常见的 患有败血症的重症患者中的心律失常，影响三分之一的化粪池患者和会议 短期和长期风险中风，心力衰竭和死亡增加。预防AF或其并发症可能 通过降低与AF相关的发病率和死亡率来改善败血症的结果。虽然有几个循证 治疗表明在某些高风险亚组中治疗和预防AF的效率（例如AF 心脏手术后的预防），研究这些疗法在重症患者中的应用 败血症受到两个主要因素的阻碍：1）我们缺乏检测AF和的自动化机制 在大型临床数据库中促进现实世界中的AF研究，2）我们目前无法预测哪些患者 随着败血症的发展，会发展自动对抗。我们的项目将利用最近发布的独特资源 重症监护（MIMIC III）数据库中的多参数智能监测。模拟III链接连续ECG 脉搏图波形具有大量时变临床和血液动力学数据。我们的项目 将使用批判性的波形数据开发和验证最先进的自动化AF检测算法 IL患者。自动化AF检测将使AF的快速临床治疗，鉴定亚临床 AF，并将催化在新兴的电子健康记录波形数据库中对AF的研究。我们将 使用最先进的波形分析算法开发创新的自动化预测功能 和重病患者的机器学习方法。自动化算法识别高风险的患者 对于在短期内开发AF将有助于预防疗法，并可能引入 预防AF的新时代针对重症患者。通过我们的项目准备的预防和治疗 将允许针对败血症期间和之后的新型，基于AF的新型机制。