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

项目摘要 该提案寻求支持，以开发使用电子健康记录（EHR）的新型数据集成方法 从多个CTSA中心创建多系统疾病的预测模型。该项目直接 解决了PAR-19-099中的重点领域，即“让新的合作者参与到先前存在的合作中， 解决一个没有一个中心可以单独解决的转化科学问题”。 研究差距：本提案的总体目标是通过网络开发预测分析 分布式算法（PANDA）框架，这将使准确的风险预测，以帮助医疗保健 提供者可以更早地做出准确的诊断。我们提出的方法直接解决了两个主要障碍：1）缺乏 多系统条件的预测模型; 2）缺乏有效地将联合收割机的数据 以隐私保护和通信高效的方式访问多个站点。 在本提案中，我们将使用两个原型多系统开发和评估PANDA框架 条件，具有不同的患病率水平：肉芽肿性多血管炎（GPA，一种血管炎， 患病率为74/百万）和银屑病关节炎（PsA）（1500/百万），预计 这种方法也适用于其他疾病。这两个条件特别适合 我们的预测方法的发展，考虑到在诊断中经常遇到的延迟， 从数月到数年。这些延迟可能与高发病率和早期死亡率有关。我们有 三个具体目标： 目标1.开发肉芽肿病伴多血管炎和银屑病关节炎的预测模型和数据 集成算法，以实现多个机构之间的安全和高效的数据共享。 目标2.使用来自一组单独的聚合数据（非IPD）测试Aim 1的预测模型 CTSA站点验证数据集成方法。 目标3。开发一个资源“工具箱”，通过该工具箱， 并且数据聚合可以容易地与所有CTSA和其他机构共享并被其采用。 该项目的成功将导致新的分析工具，以促进有效和隐私保护的数据 跨CTSA站点共享和协作风险预测。新分析工具的PANDA过程， 辅助临床诊断和干预措施，然后应通过务实的试验，以评估其 减少诊断延迟和改变患者健康轨迹的潜力。该项目可行性强， 对数据科学和临床医学都具有潜在的变革性。

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.