P-QST Project: Pancreatic Quantitative Sensory Testing (P-QST) to Predict Treatment Response for Pain in Chronic Pancreatitis

P-QST 项目：胰腺定量感觉测试 (P-QST) 预测慢性胰腺炎疼痛的治疗反应

• 批准号: 10297329
• 负责人: Anna Evans Phillips
• 金额: $ 58.04万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297329
• 关键词: Abdominal Pain; Adopted; Affect; Algorithms; Analgesics; Automobile Driving; Biochemical; Biological Markers; Clinical; Clinical Trials; Data; Denmark; Development; Disease; Drainage procedure; Duct (organ) structure; Enrollment; Enzyme-Linked Immunosorbent Assay; Etiology; Fibrosis; Foundations; Goals; Health; Health Care Costs; Health Expenditures; Hyperalgesia; Individual; Inflammatory; Investigative Techniques; Lasso; Lead; Linear Regressions; Machine Learning; Measures; Medical; Modeling; Morbidity - disease rate; Multicenter Studies; Nerve; Neuronal Plasticity; Neurons; Neuropathy; Nociception; Nomograms; Numeric Rating Scale; Obstruction; Operative Surgical Procedures; Outcome; Pain; Pain Measurement; Pain management; Pancreas; Pancreatic Diseases; Pancreatic duct; Patient Outcomes Assessments; Patients; Peripheral Nervous System Diseases; Persistent pain; Persons; Phenotype; Prediction of Response to Therapy; Prevalence; Probability; Procedures; Proteins; Protocols documentation; Quality of life; Reporting; Risk; Role; Sampling; Sensory; Serum; Severities; Spinal; Standardization; Stimulus; Suggestion; Techniques; Testing; Treatment outcome; Universities; Up-Regulation; Urine; Visceral; Wages; central pain; central sensitization; chemokine; chronic pancreatitis; comorbidity; cost; demographics; dermatome; disability; experience; follow-up; health care service utilization; high risk; impression; improved; individual patient; inflammatory marker; neuroinflammation; neurosensory; neurotransmission; nociceptive response; novel; opioid therapy; opioid use; pain processing; pain reduction; pain relief; pain score; patient response; patient subsets; personalized approach; personalized predictions; post intervention; predicting response; predictive modeling; predictive test; primary outcome; productivity loss; response; secondary outcome; side effect; success; symptom management; tool; treatment strategy

ABSTRACT: Abdominal pain is the primary driver of morbidity in chronic pancreatitis (CP) and affects approximately 90% of patients over the course of their disease with devastating effects on quality of life. Etiology of pain in CP is multi-factorial. Patients with evidence of pancreatic duct obstruction due to stones and/or strictures are offered invasive treatments such as endotherapy or surgical drainage to relieve pain. However, response to invasive treatments is unpredictable, and currently no clinical tool is available to identify patients who will respond to technically successful treatment. The lack of pain response is at least partially due to supraspinal central sensitization (SCS), a phenomenon of neuropathic and neuroplastic remodeling resulting from persistent pain stimuli. Quantitative Sensory Testing (QST), an investigative technique of standardized stimulations to test nociception (the neural signaling that encodes noxious stimuli and the downstream experience of pain), is used in other pain conditions to differentiate between patient subgroups to guide treatment. QST has the potential to change the management algorithm of patients with painful CP. Our preliminary data show that pancreatic QST (P-QST) can phenotype patients with CP into three groups: normal pain processing, segmental (T10 dermatome at the pancreas) sensitization, and widespread hyperalgesia (consistent with SCS). In this proposal, we will evaluate the ability of P-QST to predict response to invasive treatment for painful CP, and to develop a predictive model for individualized prediction of treatment response. Our specific aims are: Aim 1. Test the predictive capability of pre-treatment P-QST phenotype for pain improvement following invasive treatment for painful CP. Using pre-procedure P-QST, we will phenotype 150 patients undergoing clinically-indicated invasive treatment for painful CP at UPMC and Johns Hopkins University. Our primary outcome will be average pain score measured by Numeric Rating Scale at 6 months post-intervention. Aim 2. Incorporate P-QST with known and suspected patient, disease, and treatment- related factors to create a model for individualized prediction of response to invasive treatment. Using machine learning tools, we will develop a model that optimizes the prediction of probability of response to invasive treatment in individual patients. This will also determine the relative strength of P-QST as an overall predictor of treatment response. Aim 3. Augment the predictive model (Aim 2) with biochemical inflammatory markers to assess the potential to increase predictive capability for pain improvement following invasive treatment for painful CP. The predictive model developed in aim 2 will be further strengthened by incorporating serum neuroinflammatory markers at baseline. Our findings will be a major step toward development of individualized prediction of treatment response following invasive treatment for painful CP. They will lay the foundation for multicenter studies to fully define the role of P-QST in locally invasive and other treatments for pain management for painful CP.

摘要：腹痛是慢性胰腺炎（CP）发病的主要驱动力，并影响 在他们的疾病过程中，大约90％的患者对生活质量有毁灭性的影响。 CP疼痛的病因是多因素的。有石头引起的胰管阻塞证据的患者 和/或狭窄被提供侵入性疗法，例如内疗或外科流失，以缓解疼痛。 但是，对侵入性治疗的反应是不可预测的，目前尚无临床工具可以识别 将对技术成功治疗做出反应的患者。缺乏疼痛反应至少部分到期 脊柱上央敏化（SCS），一种神经性和神经塑性重塑的现象 从持续的疼痛刺激。定量感觉测试（QST），一种标准化的研究技术 测试伤害感受的刺激（编码有害刺激和下游的神经信号传导 疼痛的经验），在其他疼痛状况中使用以区分患者亚组以指导 治疗。 QST有可能改变CP疼痛患者的管理算法。我们的 初步数据表明，胰腺QST（P-QST）可以表型患者分为三组：正常 疼痛加工，分段（胰腺上的T10皮肤病）敏化和广泛的痛觉过敏 （与SCS一致）。在此提案中，我们将评估P-QST预测对侵入性反应的能力 治疗疼痛的CP，并开发一个预测模型以实现治疗的预测 回复。我们的具体目的是：目标1。测试预处理P-QST表型的预测能力 侵入性治疗疼痛的CP后，疼痛改善。使用预处理P-QST，我们将表型 150例在UPMC和Johns Hopkins接受临床指示的侵入性侵入性治疗的患者 大学。我们的主要结果将是通过数字评分量表在6个月时测量的平均疼痛评分 干预后。 AIM 2。将P-QST与已知和可疑的患者，疾病和治疗 - 相关因素，以创建一个模型，用于个性化对侵入性治疗的反应的模型。使用机器 学习工具，我们将开发一个模型，以优化对侵入性响应概率的预测 单个患者的治疗。这还将确定P-QST作为总体预测指标的相对强度 治疗反应。目标3。用生化炎症标记增强预测模型（目标2） 评估在侵入性治疗后提高预测能力改善疼痛能力的潜力 痛苦的CP。 AIM 2中开发的预测模型将通过合并血清进一步增强 基线时神经炎症标记。我们的发现将是发展的主要一步 侵入性治疗后，治疗反应的个性化预测疼痛CP。他们将放置 多中心研究的基础，以充分定义P-QST在本地侵入性和其他治疗中的作用 CP疼痛的疼痛管理。