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

PANDA-MSD：通过网络分布式算法对多系统疾病进行预测分析

• 批准号: 10677539
• 负责人: Jiang Bian
• 金额: $ 119.57万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677539
• 关键词: Address; Adopted; Algorithms; Area; Awareness; Clinical; Clinical Medicine; Collaborations; Communication; Consumption; Data; Data Aggregation; Data Science; Development; Diagnosis; Diagnostic; Disease; Disease Outcome; Early Diagnosis; Electronic Health Record; Florida; Generations; Goals; Health; Health Personnel; Health system; Incidence; Institution; Intervention; Knowledge; Learning; Manuals; Medical; Methodology; Methods; Morbidity - disease rate; Patients; Pattern; Predictive Analytics; Prevalence; Process; Provider; Psoriatic Arthritis; Rare Diseases; Reproducibility; Research; Resources; Secure; Site; Software Engineering; Syndrome; System; Techniques; Technology; Testing; Therapeutic; Therapeutic Intervention; Time; Translational Research; Vasculitis; Wegener' s Granulomatosis; accurate diagnosis; analytical tool; clinical diagnosis; clinical research site; cost; data hub; data integration; data sharing; expectation; implementation science; individual patient; mortality; next generation; novel; operation; pragmatic trial; predict clinical outcome; prediction algorithm; predictive modeling; privacy preservation; prototype; research clinical testing; risk prediction; success; tool

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.

项目概要 该提案寻求支持使用电子健康记录 (EHR) 开发新颖的数据集成方法 直接从多个 CTSA 中心创建多系统疾病的预测模型。 解决了 PAR-19-099 中的重点领域，“让新的合作者参与现有的合作，以 解决一个没有一个中心能够单独解决的转化科学问题。” 研究差距：该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，将实现准确的风险预测，以帮助医疗保健 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）缺乏有效组合数据的算法； 以保护隐私和高效通信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来开发和评估 PANDA 框架 不同程度的患病率：肉芽肿性多血管炎（GPA，一种血管炎， 患病率为每百万人 74 人）和银屑病关节炎 (PsA)（每百万人 1500 人），预计 该方法很容易适用于其他疾病，这两种情况特别适合。 考虑到诊断中常见的延误情况，我们的预测方法的发展可能会有所不同 这些延迟可能与高发病率和早期死亡率有关。 三个具体目标： 目标 1. 开发肉芽肿性多血管炎和银屑病关节炎的预测模型和数据 集成算法可实现多个机构之间安全高效的数据共享。 目标 2. 使用来自一组单独数据的聚合数据（不是 IPD）测试目标 1 的预测模型 CTSA 站点用于验证数据集成方法。 目标 3. 开发一个资源“工具箱”，PANDA 通过该工具箱处理算法生成 数据聚合可以轻松地与所有 CTSA 和其他人共享和采用。 该项目的成功将带来新颖的分析工具，以促进高效且保护隐私的数据 跨 CTSA 站点共享和协作风险预测的新型分析工具的 PANDA 流程。 然后应通过实用试验来研究辅助临床诊断和干预措施，以评估其效果 该项目具有减少诊断延误和改变患者健康轨迹的潜力。 对数据科学和临床医学都有潜在的变革作用。