Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to Rescue

个性化风险预测,预防和早期发现术后抢救失败

基本信息

  • 批准号:
    10753822
  • 负责人:
  • 金额:
    $ 53.56万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-01 至 2027-08-31
  • 项目状态:
    未结题

项目摘要

Abstract In the Hospital of the Future hospitalization will be reserved almost exclusively for patients with severe acute illness, staff numbers will be reduced, and hospitals will be built around smart environments that facilitate consistent delivery of effective, equitable, and error-free care focused on patient-centered rather than provider- centered outcomes. This is particularly relevant to the surgical population. While ambulatory surgical centers are the fastest growing providers, more than 51 million inpatients procedures are performed annually in hospitals in the US and inpatient surgery centers are taking care of sicker and older patients. While intraoperative mortality is rare due to improvements in surgical techniques, anesthesia management, and intraoperative monitoring, global postoperative mortality remains the third leading cause of death among American People. Recent studies have shown that while the incidence of postoperative major complications after major surgery is similar between hospitals (~25%), the postoperative mortality following postoperative major complications from one hospital to the other can be up to 2.5-fold higher. This suggests that reducing variations in mortality following major surgery will require strategies to improve the ability of high-mortality hospitals to manage postoperative major complications and decrease failure-to-rescue. One of the solutions identified is to leverage Health Information Technologies. The goal of this proposal is to use machine learning approaches to develop, validate, and test real-time postoperative risk prediction tools based on multi-modal data sources using electronic health record data, high-fidelity physiological waveform features, and genomic data to identify patients who are at risk of developing postoperative major complications after surgery. Using extensive electronic health record derived annotation augmented with high-fidelity physiological waveform features and genomic data and applying state-of-the-art machine learning approaches, common patterns in subjects destined to develop postoperative major complications and those at very low risk of developing postoperative major complications after surgery will be characterized and quantified. These inputs will then be used in simulated real-time bedside management to iteratively design a prototype clinical decision support tool. This clinical decision support tool will be used at discharge from the post anesthesia care unit to identify surgical patients who will benefit from continuous remote monitoring and early warning system on the ward to prevent postoperative failure to rescue. The feasibility and acceptability of this approach will then be assessed in a small-scale prospective, longitudinal pilot evaluation in sequential 10-weeks, 13-weeks, 10-weeks phases at UCLA to help design a future, large-scale clinical trial.
摘要 在未来的医院里,住院治疗将几乎完全留给严重急性呼吸道疾病的患者。 疾病,工作人员数量将减少,医院将围绕智能环境建立, 持续提供有效、公平和无差错的护理,重点是以患者为中心,而不是以提供者为中心, 以结果为中心。这与手术人群尤其相关。虽然门诊手术中心 是增长最快的供应商,每年有超过5100万例住院手术, 美国的医院和住院手术中心正在照顾病情较重和年龄较大的患者。而 由于手术技术、麻醉管理和 术中监测,全球术后死亡率仍然是第三大死亡原因, 美国人民最近的研究表明,虽然术后主要并发症的发生率 大手术后医院之间相似(~25%),术后死亡率 从一家医院到另一家医院的严重并发症可能高达2.5倍。这表明,减少 大手术后死亡率的变化将需要策略来提高高死亡率的能力, 医院管理术后主要并发症,减少抢救失败。的解决方案之一 确定的是利用卫生信息技术。这项提案的目标是利用机器学习 开发、验证和测试基于多模态的实时术后风险预测工具的方法 使用电子健康记录数据、高保真生理波形特征和基因组特征的数据源 数据来识别手术后有发生术后重大并发症风险的患者。使用 用高保真生理波形增强的广泛的电子健康记录导出的注释 特征和基因组数据,并应用最先进的机器学习方法, 注定发生术后重大并发症的受试者和发生风险极低的受试者 将对手术后的术后主要并发症进行表征和量化。这些输入将被 用于模拟实时床边管理,以迭代地设计原型临床决策支持工具。 该临床决策支持工具将在麻醉后监护室出院时使用,以识别 手术患者将受益于病房的持续远程监控和预警系统, 防止术后抢救失败。然后将评估这种方法的可行性和可接受性 在连续10周、13周、10周阶段的小规模前瞻性、纵向试点评价中, 帮助设计未来的大规模临床试验。

项目成果

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Maxime Cannesson其他文献

Maxime Cannesson的其他文献

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{{ truncateString('Maxime Cannesson', 18)}}的其他基金

Multidisciplinary Anesthesiology and Perioperative Medicine Research Training Program
多学科麻醉学和围手术期医学研究培训计划
  • 批准号:
    10556264
  • 财政年份:
    2023
  • 资助金额:
    $ 53.56万
  • 项目类别:
Biomedical Informatics Tools for Applied Perioperative Physiology
应用围手术期生理学的生物医学信息学工具
  • 批准号:
    10376293
  • 财政年份:
    2020
  • 资助金额:
    $ 53.56万
  • 项目类别:
Biomedical Informatics Tools for Applied Perioperative Physiology
应用围手术期生理学的生物医学信息学工具
  • 批准号:
    10612383
  • 财政年份:
    2020
  • 资助金额:
    $ 53.56万
  • 项目类别:
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients
生理波形和电子健康记录数据的机器学习可预测、诊断和治疗手术患者的血流动力学不稳定
  • 批准号:
    10330420
  • 财政年份:
    2019
  • 资助金额:
    $ 53.56万
  • 项目类别:
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients
生理波形和电子健康记录数据的机器学习可预测、诊断和治疗手术患者的血流动力学不稳定
  • 批准号:
    10589931
  • 财政年份:
    2019
  • 资助金额:
    $ 53.56万
  • 项目类别:

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  • 批准号:
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  • 财政年份:
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  • 财政年份:
    1990
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  • 项目类别:
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    1989
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