Finding Good TEMporal PostOperative pain Signatures (TEMPOS)

寻找良好的颞叶术后疼痛特征 (TEMPOS)

• 批准号: 8863868
• 负责人: Patrick J Tighe
• 金额: $ 49.19万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/8863868
• 关键词: Abdomen; Absence of pain sensation; Accounting; Acute; Address; Age; Algorithms; Analgesics; Anesthetics; Anxiety; Biological; Blood specimen; Brief Pain Inventory; Candidate Disease Gene; Characteristics; Chronic; Clinical; Clinical Data; Clinical assessments; Cluster Analysis; Cohort Studies; Complex; Computerized Medical Record; Consumption; Data; Demographic Factors; Development; Devices; Drug Kinetics; Educational Background; Equipment and supply inventories; Female; Genes; Genetic study; Hour; Lead; Linear Models; Literature; Machine Learning; Markov Chains; McGill Pain Scale; Measures; Mental Depression; Methods; Minority; Modeling; Nature; Nerve Block; Operative Surgical Procedures; Opioid; Orthopedics; Outcome; Pain; Pain Measurement; Pain intensity; Pain management; Patient-Controlled Analgesia; Patients; Pattern; Perioperative; Phenotype; Population; Postoperative Pain; Postoperative Period; Predisposing Factor; Probability; Procedures; Research; Resolution; Risk; Series; Severities; Signal Transduction; Techniques; Testing; Time; Time Series Analysis; Translating; Variant; Work; base; cohort; experience; genetic variant; improved; indexing; low socioeconomic status; novel; prospective; psychologic; public health relevance; research study; sex; signal processing; social; temporal measurement; trend

 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(申请人提供)：在美国，每年有超过1亿名患者接受手术，其中超过60%的患者会遭受严重的急性术后疼痛。最近的数据表明，术后疼痛缓解的时间进程是高度可变的，超过三分之一的患者在术后至少7天内每天都经历稳定或增加的疼痛，而不是减少疼痛。虽然以前的工作主要集中在术后平均每天疼痛的线性轨迹上，但与从日常疼痛评估得出的简单线性函数相比，测量疼痛强度每小时变化的时间曲线提供了更准确的术后疼痛体验描述。拟议研究的目的是通过应用先进的算法来描述前瞻性队列中的术后疼痛特征，以阐明术后临时性疼痛信号(TEMPO)的性质、机制基础和临床意义。这项研究将针对三个具体目标：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律；具体目标2：确定导致节拍的临床、生物、心理和社会(CBPS)机制；具体目标3：确定哪些节律最能预测术后持续性疼痛的发展。为了达到这些目标，我们提出了一项对500名外科患者进行的单中心前瞻性观察队列研究。在手术前，社会人口统计变量将通过电子病历(EMR)获得，患者将完成多个关于抑郁、焦虑和灾难的在线清单。将获得血液样本，用于探索各种疼痛相关基因的遗传学研究，并将从EMR中提取围手术期手术和麻醉细节。疼痛结果将在三种分辨率下进行评估：通过患者控制的止痛器询问每6分钟一次；通过临床评估每四小时一次；以及每天使用麦吉尔疼痛问卷和简短的疼痛清单。有关止痛药消费和患者活动的临床数据将用于疼痛强度的背景评估。患者将在术后最多7天接受随访，并在术后6个月再次接受询问，以确定术后持续性疼痛的存在和程度。分析将首先比较现有的模型，这些模型将患者的疼痛轨迹斜率分为阳性、中性或阴性，高阶模型在预测离散时间点的术后疼痛方面提供了更高的分辨率。然后，我们将对术后疼痛的时间模式进行聚类分析，以便更好地定义节律表型。这些分析将与上面列出的临床、生物、心理和社会因素一起重复，以确定这些特征如何驱动所观察到的节律的机制。最后，我们将使用先进的机器学习模型根据TEMPOS定义来预测急性和持续性术后疼痛的结果。