Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis

项目摘要和摘要 已批准的多发性硬化症(MS)疾病修饰疗法(DMT)的快速扩展 治疗反应的不同个体差异导致了关键的未得到满足的需求 为近300万多发性硬化症患者提供个性化的治疗策略 (PWMS)全球。向指导治疗的精准医学方法转变 基于个人资料的选择将通过确保及时启动来改善患者结果 有效的DMT，同时避免无效的DMT。以填补由于 缺乏随机临床试验证据和推进PWMS的精确医学，它是 对于利用可用的临床数据和开发可在以下位置部署的方法至关重要 关心。电子健康记录(EHR)数据包含丰富的纵向真实临床数据 信息，并为临床发现提供一个补充平台。建立在我们之前的基础上 研究努力，拟议的研究的总体目标是优化DMT的选择和患者 使用EHR数据在PWMS中的结果。我们将使用来自两个学术医疗保健机构的电子病历数据 系统，这两个系统都处于理想的位置，因为它们包含数千个 PWMS和与提供基本事实的MS研究注册机构的关键联系。为 其他验证，我们将使用来自大量人口的集成索赔和EHR数据 商业保险的PWMS。目标1：比较不同DMT患者的复发结果。我们将测试 假设使用完整的EHR功能进行混杂校正会产生更稳健和 与专家选择的协变量相比，在DMT有效性比较分析中得到一致的结果。 我们将使用迁移学习方法来测试泛化能力。目标2：确定患者身份 基于随时间推移的DMT处方序列的群集。我们将检验这一假设 DMT处方序列告知不同的患者群和多发性硬化症的结果。我们会申请 一个协变量调整的混合马尔可夫模型。目标3：确定最佳的DMT序列 预测有利的治疗反应。我们将检验优化DMT的假设 通过强化学习的处方序列(S)可以改善MS结果(即， 复发率、患者报告的结果)。这项研究将缩小由于以下原因造成的知识差距 缺乏随机临床试验证据和有限的现实证据来指导最佳MS 治疗选择。它将通过临床的发展使精确医学更接近PWMS 可部署的战略，以优化治疗选择。这个项目与使命是一致的。 NINDS用于减轻MS等神经系统疾病的负担。

PROJECT SUMMARY AND ABSTRACT The rapid expansion of approved multiple sclerosis (MS) disease-modifying therapies (DMTs) and the diverse individual variation in treatment response contribute to the critical unmet need for individually tailored treatment strategy for the nearly 3 million persons with multiple sclerosis (pwMS) worldwide. The shift towards a precision medicine approach to guide treatment selection based on individual profiles will improve patient outcome by ensuring prompt initiation of effective DMTs while avoiding ineffective DMTs. To fill the knowledge gaps due to the absence of randomized clinical trial evidence and to advance precision medicine for pwMS, it is crucial to harness available clinical data and develop approaches deployable at the point of care. Electronic health records (EHR) data contain a wealth of longitudinal real-world clinical information and provide a complementary platform for clinical discovery. Building on our prior research efforts, the proposed study has the overall goal to optimize DMT selection and patient outcomes in pwMS using EHR data. We will use EHR data from two academic healthcare systems, both ideally positioned as they contain longitudinal clinical information of thousands of pwMS and hold crucial linkage to MS research registries that provide the ground truth. For additional validation, we will use integrated claims and EHR data from a large population of commercially insured pwMS. Aim 1: Compare relapse outcomes across DMTs. We will test the hypothesis that confounder correction using full EHR features yields more robust and consistent results in DMT effectiveness comparison analysis than expert-selected covariates. We will test the generalizability by using a transfer learning approach. Aim 2: Identify patient clusters based on DMT prescription sequences over time. We will test the hypothesis that DMT prescription sequences inform differential patient clusters and MS outcomes. We will apply a covariate-adjusted mixture Markov Model. Aim 3: Identify optimal DMT sequences that predict favorable treatment response. We will test the hypothesis that optimized DMT prescription sequence(s) through reinforcement learning could improve MS outcomes (i.e., relapse rate, patient-reported outcomes). This research will close knowledge gaps due to absent randomized clinical trial evidence and limited real-world evidence to guide optimal MS treatment selection. It will bring precision medicine closer to pwMS by developing clinically deployable strategies to optimize treatment selection. This project is consistent with the mission of the NINDS to reduce the burden of neurological diseases such as MS.