Finding Good TEMporal PostOperative pain Signatures (TEMPOS)

 描述（由申请人提供）：美国每年有超过1亿例患者接受手术，其中超过60%的患者将遭受严重的急性术后疼痛。最近的数据表明，手术后疼痛缓解的时间过程是高度可变的，超过三分之一的患者在手术后至少前7天每天经历稳定或增加的疼痛，而不是减少。虽然以前的工作集中在平均每日术后疼痛的线性轨迹，疼痛的时间曲线，测量每小时的变化，疼痛强度提供了一个更准确的描述术后疼痛的经验比简单的线性函数来自每日疼痛评估。拟议研究的目的是通过应用先进的算法来表征前瞻性队列中的术后疼痛特征，阐明TEMporal术后疼痛特征（TEMPOS）的性质、机制基础和临床意义。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：确定哪些TEMPOS最佳预测持续性术后疼痛的发展。为了实现这些目标，我们提出了一项500例手术患者的单中心前瞻性观察性队列研究。在手术前，将通过电子病历（EMR）获得社会人口统计学变量，患者将完成抑郁，焦虑和灾难化的多个在线清单。将获得血液样本用于探索各种疼痛相关基因的遗传研究，并将从EMR中提取围手术期手术和麻醉细节。将以三种方式评估疼痛结局：每6分钟通过患者自控镇痛装置询问一次;每4小时通过临床评估一次;每天使用麦吉尔疼痛问卷和简明疼痛量表。镇痛药用量和患者活动的临床数据将用于疼痛强度的背景评估。术后将对患者进行长达7天的随访，并在术后6个月时再次询问患者，以确定是否存在持续性术后疼痛及其程度。分析将首先比较现有模型，这些模型将患者在疼痛轨迹斜率方面分类为阳性、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更高的分辨率。然后，我们将进行聚类分析，术后疼痛的时间模式，以更好地定义TEMPOS表型。这些分析将与上述临床、生物学、心理学和社会因素重复进行，以确定这些特征如何驱动观察到的TEMPOS的机制。最后，我们将使用先进的机器学习模型来预测急性和持续性术后疼痛的结果与派生的TEMPOS定义。

 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.