Optimization and Validation of tools and algorithms that enable personalized care for patients with Chronic Low Back Pain

虽然有充分的证据表明生物、心理和社会因素在病因学和 背痛的预后，研究和临床实践中这 3 种因素的综合效果并不理想。这排除了 cLPB 治疗的个性化方法将支持改善临床结果。初级 该研究项目的目标是满足对新诊断和预后的迫切需求 cLBP 治疗的标志物和相关患者分类方案。为了实现我们的目标，我们 提出三个目标，优先考虑和/或验证评估关键领域的新工具 生物心理社会模型，验证以患者为中心的结果测量，并使用以下方法研究其临床效用 UCSF REACH cLBP 临床和数字队列。 在目标 1 中，我们建议验证表征重要表型性状的通用数据元素 (CDE) cLBP 患者。这些数据元素将与生物心理社会模型的领域保持一致（目标 1a，生物- 行为；目标 1b，病理生理学；和目标 1c，功能/生物力学）。在引入 CDE 之前 REACH 临床队列中，将根据再现性、诊断性等指标将其优先分为三类 准确性和临床有效性：基本的、补充的和新兴的。通过这项工作，我们将验证成像 研究人员可以用它来研究临床队列中的脊柱病理学，临床医生可以用它来改善 他们对 cLBP 患者的护理。 在目标 2 中，我们将定义构成具有临床意义的治疗效果的个性化结果测量 对于个别患者。这些衡量标准将源自患者报告的结果衡量标准 信息系统（PROMIS），并将客观地确定患者“可以接受什么”。 在目标 3 中，我们将结合传统数据分析和深度学习来分析表型特征 方法，定义临床上有用的 cLBP 表型。 在目标 2 和 3 中，我们将利用传统的统计方法和复杂的机器学习 技术。如果我们证明我们的机器学习模型优于临床医生（他们目前正被淹没） 与数据），这些工具可以证明在以患者为中心的环境中是有益的临床决策支持系统 治疗计划。 自始至终，我们都计划与 BACPAC 联盟进行动态互动。 BACPAC/REACH 合作将 增强我们成功实现开发个性化 cLBP 算法的最终目标的能力 导致改善临床结果的治疗。

While there is good evidence for the role of biological, psychological, and social factors in the etiology and prognosis of back pain, the synthesis of the 3 in research and clinical practice is suboptimal. This precludes a personalized approach to cLPB treatment that would support improved clinical outcomes. The primary objective of this research project is to address the critical need for new diagnostic and prognostic markers and associated patient classification protocols for cLBP treatment. To achieve our objectives, we propose three aims to prioritize and/or validate novel instruments that assess critical domains of the biopsychosocial model, validate patient-centered outcome measures, and investigate their clinical utility using the UCSF REACH cLBP Clinical and Digital Cohorts. In Aim 1, we propose to validate common data elements (CDEs) that characterize important phenotypic traits in cLBP patients. These data elements will be aligned with domains of the biopsychosocial model (Aim 1a, bio- behavioral; Aim 1b, pathophysiological; and Aim 1c, functional/biomechanical). Before CDE's are introduced into the REACH clinical cohort, they will be prioritized into three categories by measures of reproducibility, diagnostic accuracy, and clinical validity: basic, supplemental, and emerging. Through this work, we will validate an imaging suite that researchers can use to study the spine pathologies in clinical cohorts, and clinicians can use to improve their care of cLBP patients. In Aim 2, we will define personalized outcome measures that constitute a clinically meaningful treatment effect for individual patients. These measures will be derived from the Patient-Reported Outcomes Measurement Information System (PROMIS), and will objectively determine 'what is acceptable' to the patient. In Aim 3 we will analyze phenotypic traits, using a combination of traditional data analyses and deep learning methods, to define clinically useful cLBP phenotypes. In both Aims 2 and 3, we will utilize both traditional statistical approaches and complex machine learning techniques. If we show that our machine learning models outperform the clinicians (who are currently inundated with data), these tools can prove to be beneficial clinical decision support systems in the setting of patient-centric treatment planning. Throughout, we plan dynamic interactions with the BACPAC consortium. BACPAC/REACH collaborations will enhance our abilities to successfully attain our ultimate goal of developing algorithms for personalized cLBP treatments that lead to improved clinical outcomes.