Automated detection and prediction of atrial fibrillation during sepsis

7.摘要/项目总结 我们提出了“脓毒症期间房颤的自动检测和预测”研究， 自动化技术能够在脓毒症期间准确检测和预测房颤（AF）。 脓毒症是一种危及生命的，对感染的失调反应，是导致感染的最常见疾病。 在美国，每年影响约100万美国人，并且与50%的 医院死亡除了早期使用抗生素和液体外，很少有治疗能改善患者的预后。 脓毒症患者;非常需要新的治疗策略来提高生存率。新发房颤是常见的 败血症重症患者中的心律失常，影响多达1/3的败血症患者， 增加中风、心力衰竭和死亡的短期和长期风险。预防房颤或其并发症可能 通过降低AF相关发病率和死亡率改善脓毒症结局。尽管一些基于证据的 治疗在某些高风险亚组（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 脓毒症受到两个主要因素的阻碍：1）我们缺乏有效的自动机制来检测AF， 促进大型临床数据库中的真实世界AF研究，2）我们目前无法预测哪些患者 脓毒症将发展AF。我们的项目将利用最近发布的 重症监护多参数智能监测（MIMIC III）数据库。MIMIC III链接连续ECG 和脉搏体积描记波形与大量随时间变化的临床和血液动力学数据相关联。我们的项目 将开发和验证最先进的自动AF检测算法使用波形数据从关键 生病的病人自动化AF检测将加快AF的临床治疗，识别亚临床 AF，并将在新兴的电子健康记录波形数据库中促进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AF预测功能 和机器学习方法在重症患者中的应用。自动算法识别高风险患者 在短期内发展房颤将使预防性治疗的目标，并可能迎来一个新的 重症患者房颤预防的新时代。通过我们的项目促进AF预防和治疗 将允许靶向脓毒症期间和之后的不良结局的新的基于AF的机制。

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.