Advancing personalized medicine in PD using harmonized multi-site clinical data

使用统一的多中心临床数据推进 PD 个性化医疗

基本信息

批准号：
10618762
负责人：
Ashkan Ertefaie
金额：
$ 55.66万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10618762
关键词：
Adoption Alzheimer&apos s Disease Behavior Benefits and Risks Caring Characteristics Clinic Visits Clinical Clinical Data Clinical Research Clinical Trials Code Complex Computer software Coupled Data Data Analyses Data Collection Data Set Decision Making Decision Trees Disease England Future Goals Guidelines Health Heterogeneity Individual Journals Language Lead Learning Longterm Follow-up Measurement Measures Medical Methodology Methods Modeling Movement Disorder Society Unified Parkinson&apos s Disease Rating Scale Nature Observational Study Outcome Output Parkinson Disease Participant Patients Pattern Pharmaceutical Preparations Pharmacotherapy Phase Phenotype Population Procedures Process Protocols documentation Publications Quality of life Randomized Recommendation Recording of previous events Research Research Personnel Sample Size Sampling Software Tools Source Syndrome Target Populations Techniques Therapeutic Time analytical method base care providers clinical care clinical decision-making clinical phenotype clinical research site comparative comparative effectiveness study data harmonization data management demographics dosage flexibility follow-up improved individualized medicine longitudinal dataset machine learning method nervous system disorder open source overtreatment patient response personalized approach personalized medicine precision medicine progression marker repository response software development statistical and machine learning tool treatment planning treatment response treatment strategy

项目摘要

Project Summary Among neurological disorders, the fastest growing is now Parkinson's disease (PD), surpassing Alzheimer's dis- ease. PD manifests as a heterogeneous clinical syndrome and this variability in the clinical phenotype highlights the need to tailor the type and/or the dosage of treatment to the speciﬁc and changing needs of individuals living with PD. The main goal of individualized, or precision, medicine is to use patient characteristics to determine an individualized treatment strategy (ITS) to promote wellness. Due to the complex nature of PD coupled with phenotypic heterogeneity, formulating successful individualized approaches to medical care is a complex prob- lem that may beneﬁt from a more data-driven approach. One of the challenges in developing reliable ITSs is that the analyses require studies with fairly large sample sizes and longitudinal assessment of subjects over a relatively long period of time. The data set must also include various prescribing patterns to allow the analytic method to learn the effects of different treatment sequences (strategies). These important requirements preclude investigators from using data from a single clinical study to construct data-driven ITSs. Existing guidelines for symptomatic drug therapy for PD can best be described as "permissive". The relative lack of comparative evidence for different classes of drugs has created challenges in devising recommendations to follow any speciﬁc therapeutic strategy. We ﬁll this important gap by proposing a two phase study. The ﬁrst phase (R61) focuses on creating a harmonized and curated dataset by integrating data from six clinical trials and the PPMI observational study that, in aggregate, involved 4,705 patients followed from 23.5 to 96 months. To the best of our knowledge, such comprehensive data harmonization has not been done before in PD and it can provide an excellent source of information for future studies as well. In the second phase (R33), we will leverage the harmonized data set to develop high quality ITSs for PD with respect to several clinical outcomes including UPDRS score, quality of life, and Schwab and England (SE) ADL measured at 24 and 48 months of follow-up. Speciﬁcally, the goals of the R33 phase are to (Aim 1) compare commonly used sequences of drug classes for PD; (Aim 2) identify the best individualized treatment strategies to inform optimal sequences of drug classes for PD. In pursuit of these aims, we will propose robust, rigorous and computationally efﬁcient statistical machine learning methods for constructing data-driven optimal ITSs for PD. The proposal expands the scope of existing methods in developing ITSs by relaxing certain unrealistic assumptions and through the use of ﬂexible modeling techniques (e.g., machine learning methods) while maintaining valid statistical inference. These new methods will be integrated into easy-to-use, publicly available software in the R language (Aim 3). This will maximize the adoption of the proposed methodology by other investigators and allow researchers to analyze other PD datasets with a goal of constructing an ITS for PD. Furthermore, because the methods are not disease-speciﬁc, our methods and software will enable similar exploration for other diseases.

项目概要在神经系统疾病中，目前增长最快的是帕金森病（PD），超过了阿尔茨海默病。 PD 表现为一种异质性临床综合征，这种临床表型的变异性凸显。需要根据个人生活的具体和不断变化的需求来调整治疗的类型和/或剂量个体化或精准医学的主要目标是利用患者特征来确定。由于 PD 的复杂性以及促进健康的个体化治疗策略 (ITS)。表型异质性，制定成功的个体化医疗护理方法是一个复杂的问题开发可靠的 ITS 的挑战之一是可能受益于更加数据驱动的方法。分析需要进行相当大的样本量的研究并对受试者进行纵向评估数据集还必须包括各种处方模式以进行分析。了解不同治疗顺序（策略）效果的方法排除了这些重要的要求。研究人员使用来自单个临床研究的数据来构建数据驱动的 ITS。现有的 PD 对症药物治疗指南可以用“宽松”来形容。缺乏不同类别药物的比较证据给制定建议带来了挑战我们通过提出两阶段研究来填补这一重要空白。阶段（R61）侧重于通过整合来自六项临床试验的数据来创建统一和精心策划的数据集 PPMI 观察性研究总共涉及 4,705 名患者，随访时间为 23.5 至 96 个月。据我们所知，这种全面的数据协调在 PD 中以前从未做过，它可以也为未来的研究提供了极好的信息来源。在第二阶段（R33），我们将利用它。统一的数据集，用于针对多种临床结果开发高质量的 PD ITS，包括在 24 个月和 48 个月的随访中测量 UPDRS 评分、生活质量以及 Schwab 和 England (SE) ADL。具体来说，R33 阶段的目标是（目标 1）比较常用的药物类别序列： PD；（目标 2）确定最佳个体化治疗策略，以告知最佳药物类别序列为了实现这些目标，我们将提出稳健、严格且计算高效的统计机。用于构建数据驱动的最优 ITS 的 PD 的学习方法扩展了现有的范围。通过放宽某些不切实际的假设并使用灵活的建模来开发智能交通系统的方法技术（例如机器学习方法），同时保持有效的统计推断。将集成到易于使用的、公开可用的 R 语言软件中（目标 3）。其他研究人员采用所提出的方法并允许研究人员分析其他 PD 数据集的目标是构建 PD 的 ITS 此外，由于这些方法不是特定于疾病的，我们的方法和软件将使对其他疾病的类似探索成为可能。