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

虽然有很好的证据表明生物、心理和社会因素在病因和疾病中的作用 对腰背痛的预后，在研究和临床实践中综合三者是次优的。这就排除了 个性化的ClpB治疗方法将支持改善临床结果。初级阶段 本研究项目的目标是解决对新的诊断和预后的迫切需求 CLBP治疗的标记物和相关的患者分类方案。为了达到我们的目标，我们 提出三个目标，以确定优先次序和/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 加州大学旧金山分校达到了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公共数据元素(CDE 慢性下腰痛患者。这些数据要素将与生物心理社会模式的领域相一致(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)。在将CDE引入 REACH临床队列，它们将根据重复性、诊断性 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 研究人员可以用来研究临床队列中的脊柱病理的套件，临床医生可以用来改进 他们对慢性下腰痛患者的照顾。 在目标2中，我们将定义构成临床上有意义的治疗效果的个性化结果衡量标准。 对个别病人来说。这些测量将从患者报告的结果测量中得出 信息系统(PROIS)，并将客观地确定‘什么是患者可以接受的’。 在目标3中，我们将结合传统的数据分析和深度学习来分析表型性状。 方法定义临床上有用的cLBP表型。 在目标2和目标3中，我们将使用传统的统计方法和复杂的机器学习 技巧。如果我们表明我们的机器学习模型比临床医生(他们目前被淹没了)表现更好 有了数据)，这些工具可以证明是有益的临床决策支持系统在以患者为中心的背景下 治疗计划。 在整个过程中，我们计划与bacpac联盟进行动态互动。BACPAC/REACH合作将 增强我们的能力，成功实现为个性化cLBP开发算法的最终目标 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.