Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States.
Ferguson Laboratory for Orthopaedic and Spine Research, Department of Orthopaedic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, United States.
Pain Med. 2023 Aug 4;24(Suppl 1):S36-S47. doi: 10.1093/pm/pnad009.
As a member of the Back Pain Consortium (BACPAC), the University of Pittsburgh Mechanistic Research Center's research goal is to phenotype chronic low back pain using biological, biomechanical, and behavioral domains using a prospective, observational cohort study. Data will be collected from 1,000 participants with chronic low back pain according to BACPAC-wide harmonized and study-specific protocols. Participation lasts 12 months with one required in person baseline visit, an optional second in person visit for advanced biomechanical assessment, and electronic follow ups at months 1, 2, 3, 4, 5, 6, 9, and 12 to assess low back pain status and response to prescribed treatments. Behavioral data analysis includes a battery of patient-reported outcomes, social determinants of health, quantitative sensory testing, and physical activity. Biological data analysis includes omics generated from blood, saliva, and spine tissue. Biomechanical data analysis includes a physical examination, lumbopelvic kinematics, and intervertebral kinematics. The statistical analysis includes traditional unsupervised machine learning approaches to categorize participants into groups and determine the variables that differentiate patients. Additional analysis includes the creation of a series of decision rules based on baseline measures and treatment pathways as inputs to predict clinical outcomes. The characteristics identified will contribute to future studies to assist clinicians in designing a personalized, optimal treatment approach for each patient.
作为背部疼痛联合会(BACPAC)的成员,匹兹堡大学机械研究中心的研究目标是使用前瞻性观察队列研究,通过生物、生物力学和行为领域对慢性下背痛进行表型分析。根据 BACPAC 广泛协调和特定研究的方案,将从 1000 名慢性下背痛患者中收集数据。参与者的参与时间为 12 个月,需要进行一次现场基线访问,可选的第二次现场访问进行高级生物力学评估,以及在第 1、2、3、4、5、6、9 和 12 个月进行电子随访,以评估下背痛状况和对规定治疗的反应。行为数据分析包括一系列患者报告的结果、健康的社会决定因素、定量感觉测试和身体活动。生物数据分析包括来自血液、唾液和脊柱组织的组学分析。生物力学数据分析包括体格检查、腰骨盆运动学和椎间运动学。统计分析包括传统的无监督机器学习方法,将参与者分为不同组别,并确定区分患者的变量。额外的分析包括基于基线测量和治疗途径创建一系列决策规则,以预测临床结果。确定的特征将有助于未来的研究,以帮助临床医生为每位患者设计个性化的最佳治疗方法。