Finding Good TEMporal PostOperative pain Signatures (TEMPOS)

寻找良好的颞叶术后疼痛特征 (TEMPOS)

基本信息

  • 批准号:
    8863868
  • 负责人:
  • 金额:
    $ 49.19万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-07-01 至 2020-06-30
  • 项目状态:
    已结题

项目摘要

 DESCRIPTION (provided by applicant): Over 100 million patients undergo surgery each year in the US, and more than 60% of these patients will suffer from severe acute postoperative pain. Recent data suggest that the time course of pain resolution following surgery is highly variable with over one-third of patients experiencing stable or increasing, rather than decreasing, pain on each day after surgery for at least the first 7 postoperative days. While prior work has focused on linear trajectories of average daily postoperative pain, temporal profiles of pain that measure hourly variations in pain intensity provide a more accurate depiction of the postoperative pain experience than simple linear functions derived from daily pain assessments. The purpose of the proposed research is to elucidate the nature, mechanistic underpinnings, and clinical implications of TEMporal PostOperative pain Signatures (TEMPOS) by applying advanced algorithms to characterize postoperative pain profiles in a prospective cohort. The research will address three Specific Aims: Specific Aim 1: To characterize TEMPOS within the surgical population via state of the art time-series analysis techniques; Specific Aim 2: To identify clinical, biological, psychological, and social (CBPS) mechanisms that contribute to TEMPOS; Specific Aim 3: To determine which TEMPOS optimally predict the development of persistent postsurgical pain. To address these aims, we propose a single-center, prospective observational cohort study of 500 surgical patients. Prior to surgery, sociodemographic variables will be obtained via the electronic medical record (EMR), and patients will complete multiple online inventories for depression, anxiety and catastrophizing. A blood sample will be obtained for genetic studies exploring a variety of pain-related genes, and perioperative surgery and anesthetic details will be extracted from the EMR. Pain outcomes will be assessed at three resolutions: every 6 minutes via a patient-controlled analgesia device interrogation; every four hours via clinical assessments; and every day using the McGill Pain Questionnaire and Brief Pain Inventory. Clinical data on analgesic consumption and patient activity will be used for contextual assessment of pain intensity. Patients will be followed for up to 7 days after surgery, and will again be queried at 6 months after surgery to determine the presence and extent of persistent postsurgical pain. Analyses will first compare existing models, which classify patients as positive, neutral, or negative in pain trajectory slope, with higher-order models offering greater resolution in predicting postoperative pain at discrete time points. We will then perform clustering analyses with respect to the temporal patterns of postoperative pain in order to better define TEMPOS phenotypes. These analyses will be repeated with the clinical, biological, psychological, and social factors listed above to determine how these characteristics drive the mechanisms underlying the observed TEMPOS. Finally, we will use advanced machine learning models to forecast both acute and persistent postoperative pain outcomes with respect to the derived TEMPOS definitions.
 描述(由适用提供):在美国,每年有超过1亿例手术患者,其中超过60%的患者将患有严重的急性阳性疼痛。最近的数据表明,手术后的疼痛解决方案是高度可变的,三分之一以上的患者在手术后每天至少在前7个积极因素中每天疼痛而不是减轻疼痛。虽然先前的工作集中在平均每日术后疼痛的线性轨迹上,但测量疼痛强度时疼痛的临时曲线比从日常疼痛评估中得出的简单线性功能更准确地描述了术后疼痛体验。拟议研究的目的是通过应用高级算法来表征前瞻性队列中潜在的疼痛特征来阐明时间术后疼痛特征(TEMPOS)的性质,机械基础和临床意义。这项研究将解决三个特定目标:具体目标1:通过最先进的时间序列分析技术来表征手术人群中的节奏;具体目的2:确定有助于节奏的临床,生物学,心理和社会(CBP)机制;特定目的3:确定哪种节奏可以最佳地预测持续性术后疼痛的发展。为了解决这些目标,我们建议对500名手术患者进行一项单一中心的前瞻性观察队列研究。在手术之前,将通过电子病历(EMR)获得社会人口统计学变量,并且患者将完成多个在线抑郁,焦虑和灾难性的库存。将获得一个血液样本,用于探索各种疼痛相关基因和周期性手术的基因研究。并从EMR中提取麻醉细节。疼痛结果将以三种分辨率进行评估:每6分钟通过患者控制的镇痛装置询问;每四个小时通过临床评估;每天都使用McGill疼痛问卷和简短的疼痛清单。镇痛消耗和患者活动的临床数据将用于疼痛强度的上下文评估。手术后长达7天,将跟踪患者,并在手术后6个月再次查询,以确定持续性术后疼痛的存在和程度。分析将首先比较现有模型,该模型将患者分类为疼痛轨迹斜率的正,中性或阴性,高阶模型在预测离散时间点的正痛方面提供了更大的分辨率。然后,我们将对术后疼痛的临时模式进行聚类分析,以更好地定义节奏表型。这些分析将通过上面列出的临床,生物学,心理和社会因素重复,以确定这些特征如何驱动观察到的节奏的机制。最后,我们将使用先进的机器学习模型来预测有关派生的节奏定义的急性和持续性术后疼痛结果。

项目成果

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Patrick J Tighe其他文献

Patrick J Tighe的其他文献

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{{ truncateString('Patrick J Tighe', 18)}}的其他基金

Perioperative Cognitive Anesthesia Network Extension for Socially Vulnerable Older Adults
针对社会弱势老年人的围手术期认知麻醉网络扩展
  • 批准号:
    10633174
  • 财政年份:
    2021
  • 资助金额:
    $ 49.19万
  • 项目类别:
Perioperative Cognitive Anesthesia Network Extension for Socially Vulnerable Older Adults
针对社会弱势老年人的围手术期认知麻醉网络扩展
  • 批准号:
    10281822
  • 财政年份:
    2021
  • 资助金额:
    $ 49.19万
  • 项目类别:
Perioperative Cognitive Anesthesia Network Extension for Socially Vulnerable Older Adults
针对社会弱势老年人的围手术期认知麻醉网络扩展
  • 批准号:
    10475724
  • 财政年份:
    2021
  • 资助金额:
    $ 49.19万
  • 项目类别:
Finding Good TEMporal PostOperative pain Signatures (TEMPOS)
寻找良好的颞叶术后疼痛特征 (TEMPOS)
  • 批准号:
    9291477
  • 财政年份:
    2015
  • 资助金额:
    $ 49.19万
  • 项目类别:
Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F
使用机器学习分类器预测严重急性术后疼痛 F
  • 批准号:
    8901203
  • 财政年份:
    2012
  • 资助金额:
    $ 49.19万
  • 项目类别:
Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F
使用机器学习分类器预测严重急性术后疼痛 F
  • 批准号:
    8505014
  • 财政年份:
    2012
  • 资助金额:
    $ 49.19万
  • 项目类别:
Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F
使用机器学习分类器预测严重急性术后疼痛 F
  • 批准号:
    8353726
  • 财政年份:
    2012
  • 资助金额:
    $ 49.19万
  • 项目类别:
Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F
使用机器学习分类器预测严重急性术后疼痛 F
  • 批准号:
    8677604
  • 财政年份:
    2012
  • 资助金额:
    $ 49.19万
  • 项目类别:

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  • 批准号:
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