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

利用电子健康记录优化多发性硬化症的治疗选择和反应

• 批准号: 10583784
• 负责人: Zongqi Xia
• 金额: $ 67.82万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583784
• 关键词: Address; Benchmarking; Chronic; Classification; Clinical; Clinical Data; Clinical Management; Collaborations; Computing Methodologies; Data; Data Analyses; Disease; Effectiveness; Electronic Health Record; Ensure; Event; Favorable Clinical Outcome; Frequencies; Future; Goals; Healthcare Systems; Individual; Knowledge; Learning; Machine Learning; Maps; Medical center; Mission; Modeling; Multiple Sclerosis; National Institute of Neurological Disorders and Stroke; Nervous System Physiology; Observational Study; Outcome; Patient Outcomes Assessments; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Population; Positioning Attribute; Probability; Productivity; Psychological reinforcement; Randomized, Controlled Trials; Recording of previous events; Registries; Relapse; Research; Selection for Treatments; Severities; Testing; Time; Time trend; Treatment Efficacy; Treatment Failure; Treatment outcome; Universities; Validation; cohort; comorbidity; comparative effectiveness analysis; compare effectiveness; cost; data registry; data warehouse; demographics; efficacy clinical trial; improved; individual variation; individualized medicine; longitudinal, prospective study; markov model; meetings; multiple sclerosis patient; multiple sclerosis treatment; nervous system disorder; non-compliance; novel; optimal treatments; patient registry; phenome; point of care; precision medicine; randomized, clinical trials; transfer learning; treatment optimization; treatment response; treatment strategy; trial comparing

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.

项目总结和摘要 获批的多发性硬化症（MS）疾病修饰疗法（DMT）的快速扩张 治疗反应的个体差异导致了关键的未满足需求 为近300万多发性硬化症患者提供个性化治疗策略 （世界各地）。转向精确医学方法来指导治疗 基于个体特征的选择将通过确保及时启动来改善患者结局 有效的DMT，同时避免无效的DMT。为了填补知识空白， 由于缺乏随机临床试验证据，为了推进精准医学治疗多发性硬化症， 关键是利用现有的临床数据，并开发可在 在乎电子健康记录（EHR）数据包含大量纵向真实世界临床数据， 信息，并为临床发现提供补充平台。根据我们之前 研究工作，拟议的研究的总体目标是优化DMT的选择和患者 使用EHR数据的CIMMS结果。我们将使用来自两个学术医疗保健的EHR数据 系统，两者都是理想的定位，因为它们包含数千个 它与MS研究登记处有着至关重要的联系，这些登记处提供了基本事实。为 额外的验证，我们将使用来自大量人口的综合索赔和EHR数据， 有商业保险的保险公司。目的1：比较不同DMT的复发结果。我们将测试 假设使用完整的EHR特征进行混淆校正， 在DMT有效性比较分析中，结果与专家选择的协变量一致。 我们将通过使用迁移学习方法来测试可推广性。目标2：识别患者 基于DMT处方序列随时间的聚类。我们将检验这个假设， DMT处方序列告知不同的患者群和MS结果。我们将应用 协变量调整混合马尔可夫模型。目的3：确定最佳DMT序列， 预测良好的治疗反应。我们将测试优化DMT的假设， 通过强化学习的处方序列可以改善MS结果（即， 复发率，患者报告的结果）。这项研究将缩小知识差距， 缺乏随机临床试验证据和有限的现实世界证据来指导最佳MS 治疗选择它将通过临床开发使精准医学更接近于CIMMS 可部署的策略，以优化治疗选择。该项目与使命一致 的NINDS，以减少神经系统疾病的负担，如MS。