Characterizing patients at risk for sepsis through Big Data

通过大数据描述有败血症风险的患者

基本信息

  • 批准号:
    10454830
  • 负责人:
  • 金额:
    $ 17.82万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-08-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

Characterizing Patients at Risk for Sepsis Through Big Data SUMMARY The goal of this KL2 research proposal is create an extension of an existing data-driven sepsis algorithm, the artificial intelligence sepsis expert (AISE), by forecasting the type and sequence of sepsis-specific organ failure using clinical data from the electronic medical record, then identifying the incremental benefit received by adding high-resolution data derived from cardiovascular waveforms (arterial waveform and electrocardiographic waves), and small molecule metabolite data over time to provide some mechanistic context. The most important data (features) used in real-time from AISE will be used as inputs for a fuzzy k- means clustering algorithm (“Saving Organs from Sepsis”, or SOS) using retrospectively-collected data, designed to better characterize patients at risk for sepsis by their organ failure. I have selected three organs in particular: shock, acute respiratory failure, and acute kidney injury (AKI). Principal component analyses (PCA) data from 2,375 ICU patients with sepsis will be projected onto a novel visual representation for patient phenotyping based on risk of (trajectory toward) different types of organ failure. I will identify if this SOS algorithm can accurately forecast new organ failure within 12 hours based on SOS. To better understand the impact of specific features on organ failure, I will test the ability of each high- resolution features, and metabolomics data to forecast septic shock. Among those who develop septic shock, I will measure all nine high-resolution features from the beginning of the ICU stay up to shock onset and compare those changes to those who develop sepsis but not septic shock, and those who do not develop sepsis. I will then see if the collective addition of high-resolution data improves performance of septic shock forecasting. Finally, I will conduct a prospective observational study to collect metabolomic information in a 60- patient study to identify the incremental improvement of adding metabolomics data to SOS for predicting septic shock, over SOS with just EMR and waveform data. The results of this work will provide preliminary data for further career development and NIH-funding. The long-term goal would be to build a model that optimizes the timing of appropriate therapy, thus decreasing the incidence of sepsis and associated organ failure. As a K23 candidate, I will use this award to acquire formal didactic training and more hands-on experience in machine learning, signal processing, metabolomics analysis. I will seek focused training that will complement my experience as a clinical trialist so that I can design high-quality studies to contribute to Big Data analytics in critical care research and practice. My overarching career goal is to become a leader in the application of Big Data analysis of critically ill patients to predict progression of disease, specifically sepsis. The Emory environment is an ideal place to develop these capabilities. Emory Healthcare houses over 200 medical, surgical, and subspecialty ICU beds, many of which are “wired” to store streaming data. In addition to the physical resources, my development will be enhanced by my superb mentorship team, led by Dr. Greg Martin.
通过大数据表征脓毒症风险患者 总结 该KL 2研究提案的目标是创建现有数据驱动脓毒症算法的扩展, 人工智能脓毒症专家(AISE),通过预测脓毒症特异性器官衰竭的类型和顺序 使用来自电子医疗记录的临床数据,然后识别通过以下方式接收的增量益处: 添加从心血管波形(动脉波形和 心电图波)和小分子代谢物数据,以提供一些机制 上下文来自AISE的实时使用的最重要的数据(特征)将用作模糊k- 均值聚类算法(“从败血症中拯救器官”,或SOS)使用回顾性收集的数据, 旨在通过器官衰竭更好地表征脓毒症风险患者。我选择了三个器官, 特别是:休克、急性呼吸衰竭和急性肾损伤(阿基)。主成分分析(PCA) 来自2,375名ICU脓毒症患者的数据将被投影到一种新的患者视觉表示上, 基于不同类型器官衰竭的风险(走向)的表型。我会确认这个求救信号 该算法可以准确预测12小时内基于SOS的新发器官衰竭。 为了更好地了解器官衰竭的具体特征的影响,我将测试每个高- 分辨率特征和代谢组学数据来预测败血性休克。在那些发生败血性休克的人中,我 将测量从ICU开始到休克发作的所有九个高分辨率特征, 将这些变化与发生脓毒症但未发生脓毒性休克的患者以及未发生脓毒性休克的患者进行比较, 败血症然后,我将观察高分辨率数据的集体添加是否改善了感染性休克的表现 预测。最后,我将进行一项前瞻性观察性研究,收集60例患者的代谢组学信息, 一项患者研究,旨在确定将代谢组学数据添加到SOS中以预测脓毒症 只有电磁波和波形数据的紧急呼救这项工作的结果将提供初步数据, 进一步的职业发展和NIH资助。长期目标是建立一个模型,优化 适当治疗的时机,从而降低脓毒症和相关器官衰竭的发生率。 作为K23候选人,我将利用这个奖项获得正式的教学培训和更多的实践经验, 机器学习信号处理代谢组学分析我将寻求重点培训, 我作为临床试验专家的经验,使我能够设计高质量的研究,为大数据分析做出贡献, 重症监护研究与实践我的总体职业目标是成为大数据应用领域的领导者, 对重症患者进行数据分析以预测疾病进展,特别是败血症。埃默里 环境是发展这些能力的理想场所。埃默里医疗中心拥有200多名医疗, 外科和专科ICU病床,其中许多是“有线”存储流数据。除了有 我的发展将得到格雷格·马丁博士领导的一流导师团队的帮助。

项目成果

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Andre L Holder其他文献

Andre L Holder的其他文献

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{{ truncateString('Andre L Holder', 18)}}的其他基金

Characterizing patients at risk for sepsis through Big Data
通过大数据描述有败血症风险的患者
  • 批准号:
    10668998
  • 财政年份:
    2020
  • 资助金额:
    $ 17.82万
  • 项目类别:
Characterizing patients at risk for sepsis through Big Data (Supplement)
通过大数据描述有败血症风险的患者(补充)
  • 批准号:
    10599662
  • 财政年份:
    2020
  • 资助金额:
    $ 17.82万
  • 项目类别:
Characterizing patients at risk for sepsis through Big Data
通过大数据描述有败血症风险的患者
  • 批准号:
    10213098
  • 财政年份:
    2020
  • 资助金额:
    $ 17.82万
  • 项目类别:
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