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学中的作用， 背痛的预后，在研究和临床实践中的3个综合是次优的。这就排除了 个性化的cLPB治疗方法，将支持改善临床结果。主 本研究项目的目的是解决新的诊断和预后的迫切需要， 用于cLBP治疗的标记物和相关的患者分类方案。为了实现我们的目标，我们 提出三个目标，优先考虑和（或）验证新的工具， 生物心理社会模型，验证以患者为中心的结局指标，并使用 UCSF REACH cLBP临床和数字队列。 在目标1中，我们建议验证共同的数据元素（CDE），表征重要的表型性状， cLBP患者这些数据元素将与生物心理社会模型的领域（目标1a，生物 行为;目标1b，病理生理学;目标1c，功能/生物力学）。在CDE被引入之前， 在REACH临床队列中，他们将通过再现性、诊断性和治疗性指标被优先分为三类。 准确性和临床有效性：基本、补充和新兴。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脊柱病理，临床医生可以用来改善 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局指标 对于个别患者。这些指标将来自患者报告的结局指标 信息系统（PROMIS），并将客观地确定'什么是可以接受的'的病人。 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性状。 方法，以定义临床上有用的cLBP表型。 在目标2和目标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（他们目前被淹没了 与数据），这些工具可以证明是有益的临床决策支持系统，在设置以患者为中心 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 提高我们的能力，成功实现我们的最终目标，开发个性化的cLBP算法 这些治疗可以改善临床结果。

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.