The Use of Novel Linked Databasesto Reduce Postoperative Opioid Use Among Patients Undergoing Inpatient Surgery

使用新型链接数据库减少住院手术患者术后阿片类药物的使用

基本信息

  • 批准号:
    10745607
  • 负责人:
  • 金额:
    $ 110.55万
  • 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 2025-08-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY AND ABSTRACT Surgery places patients at increased for opioid use disorder and persistent postoperative opioid use (PPOU). In addition to its direct impact on patient health, PPOU, which affects 5-6% of surgical patients, is associated with an increased risk of opioid use disorder, opioid overdose, and surgical mortality/morbidity. This issue has particular salience for older adults. Over half of all surgical procedures in the United States occur among older adults, over half of older adults will require a surgery once in their lifetime, and the incidence of PPOU among older adults can be as high as 10%. In this light, the long-term goal of this project is to characterize the effectiveness of perioperative interventions in reducing the risk of long-term outcomes such as PPOU and opioid use disorder among older adults undergoing inpatient surgery. While a wide variety of interventions has been hypothesized to reduce the incidence of PPOU and other post-operative opioid outcomes, there remains a lack of consensus about their effectiveness, in part due to data limitations. In particular, it is often challenging to obtain detailed data on perioperative care (i.e., amount of opioid administered intraoperatively) and data on long-term opioid outcomes. This study builds on a novel dataset that links two datasets: the Multicenter Perioperative Outcomes Group (MPOG), a large, multicenter registry of surgical cases using data extracted from electronic medical records and a healthcare claims data for Medicare fee-for-service patients. This novel dataset unites the best aspects of both datasets: the ability to measure perioperative care and the ability to follow patients in order to assess long-term opioid outcomes. We will accomplish the goals of the project through four specific aims. First, we will augment the existing dataset by developing scalable and generalizable tools to incorporate relevant data from the inpatient stay (i.e. opioid administration and the use of non-opioid adjuncts). Second, we will demonstrate the feasibility of these methods at a single institution and expand their use to five institutions. Third, we will use the augmented dataset to evaluate the association between intraoperative interventions (i.e., opioid administration, use of nerve blocks) and long-term opioid outcomes (i.e., PPOU and opioid use disorder). Finally, we will use the augmented dataset to evaluate the association between inpatient stay interventions (i.e., reduced opioid utilization, reduced prescribing at discharge) and long-term opioid outcomes. The findings of this project will be significant as they will help guide crucial aspects of perioperative decision-making such as intraoperative and postoperative opioid administration. The expected outcomes of this project are relevant to the goals of the HEAL Initiative as they will enhance efforts to reduce the incidence of PPOU and other long-term opioid outcomes such as opioid use disorder. Crucially, throughout the project, we will work with stakeholders to maximize its impact, such as consulting with stakeholders to decide the interventions to study and working with stakeholders to incorporate the project findings into clinical guidelines and policy.
项目摘要和摘要 手术将患者置于阿片类药物使用障碍和术后阿片类药物持续使用(PPOU)的增加。 除了对患者健康的直接影响外,影响5%-6%的外科患者的PPOU还与 阿片类药物使用障碍、阿片类药物过量和手术死亡率/发病率增加的风险。这个问题有 对于老年人来说尤为突出。在美国,超过一半的外科手术发生在老年人中 成年人中,超过一半的老年人一生中需要做一次手术,PPOU的发病率 老年人的比例可能高达10%。从这个角度来看,这个项目的长期目标是描述 围手术期干预在降低PPOU和PPOU等长期结局风险方面的有效性 住院手术的老年人中的阿片类药物使用障碍。虽然各种各样的干预措施已经 假设可以减少PPOU的发生率和手术后的其他阿片类药物结果,仍有 对其有效性缺乏共识,部分原因是数据限制。特别是,这往往是具有挑战性的。 获取有关围手术期护理的详细数据(即术中使用阿片类药物的数量)和 长期的阿片类药物结果。这项研究建立在一个新的数据集的基础上,该数据集连接了两个数据集:多中心 围术期结果组(MPOG),使用提取的数据对手术病例进行大型、多中心登记 来自电子医疗记录和医疗保险收费患者的医疗索赔数据。这部小说 数据集结合了两个数据集的最佳方面:测量围手术期护理的能力和 跟踪患者,以评估阿片类药物的长期疗效。我们将完成这个项目的目标 通过四个具体目标。首先,我们将通过开发可伸缩的和 纳入住院期间相关数据的通用工具(即阿片类药物的使用和使用 非阿片类药物)。第二,我们将在一个机构中演示这些方法的可行性 将其使用扩大到五个机构。第三,我们将使用增强的数据集来评估关联 术中干预措施(即阿片类药物的使用、神经阻滞的使用)和长期阿片类药物的使用 结果(即PPOU和阿片类药物使用障碍)。最后,我们将使用增强的数据集来评估 住院干预措施之间的关联(即减少阿片类药物的使用,减少在 和长期的阿片类药物结果。该项目的发现将具有重要意义,因为它们将有助于指导 围手术期决策的关键方面,如术中和术后阿片类药物 行政管理。该项目的预期成果与治愈倡议的目标相关,因为它们 将加强努力,以减少PPOU的发生率和其他长期阿片类药物结果,如阿片类药物的使用 无序。至关重要的是,在整个项目中,我们将与利益相关者合作,以最大限度地发挥其影响,例如 咨询利益相关者,以决定要研究的干预措施,并与利益相关者合作,纳入 该项目的研究结果纳入了临床指南和政策。

项目成果

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Douglas Alastair Colquhoun其他文献

Douglas Alastair Colquhoun的其他文献

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{{ truncateString('Douglas Alastair Colquhoun', 18)}}的其他基金

Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery
数据驱动的方法改善重大住院手术后肺部并发症的风险预测
  • 批准号:
    10665631
  • 财政年份:
    2021
  • 资助金额:
    $ 110.55万
  • 项目类别:
Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery
数据驱动的方法改善重大住院手术后肺部并发症的风险预测
  • 批准号:
    10469672
  • 财政年份:
    2021
  • 资助金额:
    $ 110.55万
  • 项目类别:
Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery
数据驱动的方法改善重大住院手术后肺部并发症的风险预测
  • 批准号:
    10283012
  • 财政年份:
    2021
  • 资助金额:
    $ 110.55万
  • 项目类别:

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