PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System Diseases

项目摘要 该提案寻求支持使用电子健康记录(EHR)开发新的数据集成方法 从多个CTSA枢纽创建多系统疾病的预测模型。建议的项目直接 阐述了PAR-19-099中强调的领域，即在预先存在的协作中吸引新的合作者，以 解决任何一个枢纽都无法单独解决的翻译科学问题。 研究差距：该计划的总体目标是通过网络发展预测分析 分布式算法(Panda)框架，可实现准确的风险预测，帮助医疗保健 提供者更早地得出准确的诊断。我们提出的方法直接解决了两个主要障碍：1)缺乏 用于多系统条件的预测模型；2)缺乏有效地结合来自 以保护隐私和高效沟通的方式提供多个站点。 在这个提案中，我们将使用两个原型多系统来开发和评估Panda框架 疾病，有不同的流行程度：肉芽肿合并多血管炎(GPA，一种血管炎， 患病率为74‰)和牛皮癣关节炎(1500‰)，预计 这种方法将很容易适用于其他疾病。这两个条件特别适合于 考虑到诊断中常见的延迟，我们的预测方法的发展可能会 从几个月到几年。这些延误可能与高发病率和早期死亡率有关。我们有 三个具体目标： 目的1.建立肉芽肿病合并多血管炎和银屑病关节炎的预测模型，并提供数据 集成算法，以实现多个机构之间安全高效的数据共享。 目标2.使用来自单独集合的聚合数据(非IPD)测试来自目标1的预测模型 CTSA站点，以验证数据集成方法。 目标3.开发一个“工具箱”的资源，熊猫通过它来处理算法生成 而且数据聚合可以很容易地与所有CTSA和其他机构共享和采用。 该项目的成功将带来新的分析工具，以促进高效和隐私保护数据 跨CTSA站点共享和协作风险预测。熊猫过程中的新分析工具 辅助临床诊断和干预应通过实用试验进行研究，以评估其 有可能减少诊断延迟和改变患者的健康轨迹。该项目具有很高的可行性和 对数据科学和临床医学都有潜在的变革。

Project Summary This proposal seeks support to develop novel data integration methods using electronic health records (EHR) from multiple CTSA hubs to create predictive models of multi-system diseases. The proposed project directly addresses the areas of emphasis in PAR-19-099 to “engage new collaborators in pre-existing collaborations to solve a translational science problem no one hub can solve alone”. Research gap: The overarching goal of this proposal is to develop the Predictive Analytics via Networked Distributed Algorithms (PANDA) framework, which will enable accurate risk prediction to help healthcare providers reach accurate diagnoses earlier. Our proposed methods directly address two major barriers: 1) lack of predictive models for multi-system conditions; 2) lack of algorithms that effectively combine data from multiple sites in a privacy-preserving and communication-efficient fashion. In this proposal, we will develop and evaluate the PANDA framework using two prototypic multi-system conditions, with different levels of prevalence: granulomatosis with polyangiitis (GPA, a type of vasculitis, prevalence of 74 per million) and psoriatic arthritis (PsA) (1500 per million), with the expectation that the approach will be readily applicable to other diseases. These two conditions are particularly well-suited to the development of our predictive methods given the commonly encountered delays in diagnosis that can range from months to years. These delays may be associated with high morbidity and early mortality. We have three Specific Aims: Aim 1. Develop predictive models for granulomatosis with polyangiitis and psoriatic arthritis, and data integration algorithms to enable secure and efficient data sharing among multiple institutions. Aim 2. Test the predictive models from Aim 1 using aggregated data (not IPD) from a separate set of CTSA sites to validate the data integration methodology. Aim 3. Develop a “toolbox” of resources through which the PANDA processes of algorithm generation and data aggregation can be easily shared with and adopted for use by all CTSAs and others. The success of this project will lead to novel analytic tools for facilitating efficient and privacy-preserving data sharing and collaborative risk predictions across CTSA sites. The PANDA process of novel analytic tools to assist clinical diagnoses and interventions should then be studied through pragmatic trials to evaluate its potential to decrease diagnostic delays and alter patients’ health trajectories. This project is highly feasible and is potentially transformative for both data science and clinical medicine.