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.

项目总结