Suppr超能文献

一种用于预测疼痛敏感性的新型皮质生物标志物特征:PREDICT纵向分析验证研究方案

A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study.

作者信息

Seminowicz David A, Bilska Katarzyna, Chowdhury Nahian S, Skippen Patrick, Millard Samantha K, Chiang Alan K I, Chen Shuo, Furman Andrew J, Schabrun Siobhan M

机构信息

Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, United States.

Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, MD, United States.

出版信息

Pain Rep. 2020 Jul 27;5(4):e833. doi: 10.1097/PR9.0000000000000833. eCollection 2020 Jul-Aug.

Abstract

INTRODUCTION

Temporomandibular disorder is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic temporomandibular disorder in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This article describes, a priori, the methods and analysis plan.

METHODS

This study uses a multisite longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. One hundred fifty healthy participants will be recruited. Participants will complete twice daily electronic pain diaries from day 0 to day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on days 0, 2, and 5. Intramuscular injection of NGF will be given into the right masseter muscle on days 0 and 2. The primary outcome is pain sensitivity.

PERSPECTIVE

PREDICT is the first study to undertake analytical validation of a PAF and CME biomarker signature. The study will determine the sensitivity, specificity, and accuracy of the biomarker signature to predict an individual's sensitivity to pain.

REGISTRATION DETAILS

ClinicalTrials.gov: NCT04241562 (prospective).

摘要

引言

颞下颌关节紊乱病是一种常见的肌肉骨骼疼痛疾病,49%的患者会出现慢性症状。尽管在横断面研究和病例对照研究中,许多生物学因素已显示与慢性颞下颌关节紊乱病有关联,但目前尚无能够预测慢性症状发展的生物标志物。“预测”研究旨在使用向持续性肌筋膜颞下颌疼痛转变的人体模型(在咬肌内注射神经生长因子[NGF]),对一种新的峰值阿尔法频率(PAF)和皮质运动兴奋性(CME)生物标志物特征进行分析验证。本文预先描述了方法和分析计划。

方法

本研究采用多中心纵向实验研究,对个体进行为期30天的跟踪,观察他们逐步出现并完全缓解NGF诱导的肌肉疼痛的过程。将招募150名健康参与者。参与者将在第0天至第30天每天完成两次电子疼痛日记,并接受压力疼痛阈值评估,以及在第0天、第2天和第5天记录PAF和CME。在第0天和第2天,将NGF注射到右侧咬肌中。主要结局是疼痛敏感性。

观点

“预测”研究是首次对PAF和CME生物标志物特征进行分析验证的研究。该研究将确定生物标志物特征预测个体疼痛敏感性的敏感性、特异性和准确性。

注册详情

ClinicalTrials.gov:NCT04241562(前瞻性)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e326/7390594/d34bcbce94a3/painreports-5-e833-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验