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
• 负责人: Patrick J Tighe
• 金额: $ 50.72万
• 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/9291477
• 关键词: Abdomen; Absence of pain sensation; 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; Dimensions; 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; Prospective cohort; Research; Resolution; Risk; Series; Severities; Signal Transduction; Techniques; Testing; Time; Time Series Analysis; Translating; Variant; Work; base; discrete time; experience; experimental study; genetic variant; improved; indexing; learning strategy; low socioeconomic status; novel; opioid use; prospective; psychologic; public health relevance; 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（由申请人提供）：美国每年有超过 1 亿患者接受手术，其中超过 60% 的患者会遭受严重的术后急性疼痛。最近的数据表明，手术后疼痛缓解的时间过程变化很大。超过三分之一的患者在手术后至少前 7 天每天都会经历稳定或增加而不是减少的疼痛。每小时测量一次与日常疼痛评估得出的简单线性函数相比，疼痛强度的变化可以更准确地描述术后疼痛体验。本研究的目的是阐明术后临时疼痛特征 (TEMPOS) 的性质、机制基础和临床意义。通过先进的算法来表征前瞻性队列中的术后疼痛特征 该研究将解决三个具体目标： 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 TEMPOS；具体目标 2：确定有助于 TEMPOS 的临床、生物、心理和社会 (CBPS) 机制；具体目标 3：确定哪种 TEMPOS 能够最佳地预测持续性术后疼痛的发展。该中心对 500 名手术患者进行了前瞻性观察队列研究，在手术前，将通过电子病历 (EMR) 获得社会人口统计学变量，患者将完成多项在线调查，了解抑郁、焦虑和灾难性情绪。将获取样本用于探索各种疼痛相关基因的基因研究，并从 EMR 中提取围手术期手术和麻醉详细信息。疼痛结果将以三种分辨率进行评估：每 6 分钟通过患者自控镇痛装置询问一次；通过临床评估每四个小时进行一次；每天使用麦吉尔疼痛问卷和简要疼痛清单进行一次镇痛药用量和患者活动的临床数据，对患者进行长达 7 天的疼痛强度评估。手术后，将在手术后 6 个月再次询问，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，该模型将患者的疼痛轨迹斜率分类为阳性、中性或阴性。然后，我们将针对术后疼痛的时间模式进行聚类分析，以便更好地定义 TEMPOS 表型。上述社会因素确定这些特征如何驱动观察到的 TEMPOS 背后的机制，最后，我们将使用先进的机器学习模型根据派生的 TEMPOS 定义来预测急性和持续性术后疼痛结果。