Early detection, containment, and management of COVID-19 in dialysis facilities using multi-modal data sources

使用多模式数据源在透析设施中早期检测、遏制和管理 COVID-19

基本信息

批准号：
10320487
负责人：
WENSHENG GUO
金额：
$ 67.28万
依托单位：
UNIVERSITY OF CALIFORNIA SANTA BARBARA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-21 至 2023-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10320487
关键词：
Address Age Black Populations COVID-19 COVID-19 patient COVID-19 surveillance COVID-19 test Caring Cessation of life Clinic Clinical Clinical Treatment Containment Coronavirus Data Data Collection Data Sources Detection Dialysis patients Dialysis procedure Early Diagnosis Ensure Goals Graph Healthcare Hemodialysis Hispanic Populations Individual Investigation Kidney Laboratories Life Location Mathematics Measures Medical Minority Modeling Nature Network-based Pathway Analysis Pathway interactions Patients Pattern Procedures Provider Recommendation Research Institute Risk SARS-CoV-2 exposure SARS-CoV-2 infection SARS-CoV-2 transmission Safety Sampling Serology Serology test Serum Shelter facility Source Space Models Statistical Models Symptoms Time Travel Validation asymptomatic COVID-19 comorbidity data resource detection platform feature extraction high dimensionality high risk high risk population improved innovation machine learning method mathematical model metabolomics multimodal data multiple data sources novel patient safety personalized care predictive modeling prevent procedure safety prospective recurrent neural network sensor socioeconomics statistical and machine learning surveillance data tool transmission process wearable device

Address Age Black Populations COVID-19 COVID-19 patient COVID-19 surveillance COVID-19 test Caring Cessation of life Clinic Clinical Clinical Treatment Containment Coronavirus Data Data Collection Data Sources Detection Dialysis patients Dialysis procedure Early Diagnosis Ensure Goals Graph Healthcare Hemodialysis Hispanic Populations Individual Investigation Kidney Laboratories Life Location Mathematics Measures Medical Minority Modeling Nature Network-based Pathway Analysis Pathway interactions Patients Pattern Procedures Provider Recommendation Research Institute Risk SARS-CoV-2 exposure SARS-CoV-2 infection SARS-CoV-2 transmission Safety Sampling Serology Serology test Serum Shelter facility Source Space Models Statistical Models Symptoms Time Travel Validation asymptomatic COVID-19 comorbidity data resource detection platform feature extraction high dimensionality high risk high risk population improved innovation machine learning method mathematical model metabolomics multimodal data multiple data sources novel patient safety personalized care predictive modeling prevent procedure safety prospective recurrent neural network sensor socioeconomics statistical and machine learning surveillance data tool transmission process wearable device

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项目摘要

Abstract With older age and multiple comorbidities, dialysis patients are at high risk for serious complications, even death, from COVID-19. There is a large disproportionate representation of minorities, especially Blacks and Hispanics. Over 85% of hemodialysis patients travel three times a week to dialysis facilities to receive life-sustaining treatments and cannot shelter in place. There is a critical need to characterize COVID-19 transmission pathways in dialysis patients and clinics, identify potential coronavirus carriers, and develop procedures to curb the spread. With regular medical encounters, a large amount of data has been collected for each patient over time. These data have not been fully utilized for COVID-19 prediction and control in dialysis clinics. In this proposal, we seek to leverage demographic, clinical, treatment, laboratory, socioeconomic, serological, metabolomic, wearable and machine-integrated sensors, and COVID-19 surveillance data to develop mathematical and statistical models and implement them in a large number of dialysis clinics. The mathematical and statistical modeling using multiple data resources will help us understand how COVID-19 spread in dialysis facilities, identify potential COVID-19 patients before symptoms appear, and identify potential asymptomatic COVID-19 patients. We will develop novel mathematical and statistical models that fully utilize the high dimensional multimodal data available to us and other dialysis providers. We capitalize on the intrinsic advantages of hemodialysis clinics to implement and validate the proposed prediction models. We firmly believe that this cross-disciplinary effort will improve patients’ and staff’s safety while delivering high-quality, individualized care to a high-risk population.

抽象的透析患者年龄较大和多种合并症，严重并发症，甚至死亡，甚至从Covid-19。少数群体，尤其是黑人和西班牙裔的代表不成比例。超过85％的血液透析患者每周三次前往透析设施，以获得维持生命治疗，无法庇护到位。迫切需要表征COVID-19传输途径在透析患者和诊所中，鉴定潜在的冠状病毒载体，并制定遏制扩散的程序。随着时间的推移，随着时间的推移，已经为每个患者收集了大量数据。这些在透析诊所中，数据尚未完全用于COVID-19的预测和控制。在此提案中，我们寻求利用人口统计，临床，治疗，实验室，社会经济，血清学，代谢组，可穿戴和机器集成传感器和COVID-19监视数据以开发数学和统计模型并在大量透析诊所中实施它们。使用的数学和统计建模多个数据资源将有助于我们了解透析设施中的Covid-19如何传播，确定潜力 Covid-19症状之前的19例患者出现，并确定潜在的无症状COVID-19患者。我们将开发充分利用高维多模式数据的新型数学和统计模型可用于我们和其他透析提供者。我们利用血液透析诊所的内在优势实施并验证提出的预测模型。我们首先相信这种跨学科的努力将在为高危人群提供高质量的个性化护理的同时，提高患者和员工的安全性。