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：
10554348
负责人：
WENSHENG GUO
金额：
$ 66.63万
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 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10554348
关键词：
Address Black Populations COVID-19 COVID-19 patient COVID-19 surveillance COVID-19 test Caring Cessation of life Clinic Clinical Contact Tracing Containment Coronavirus Data Data Collection Data Sources Detection Dialysis patients Dialysis procedure Early Diagnosis Ensure Goals 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 Risk Reduction 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 human old age (65+)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

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.

抽象的由于年龄较大和多种合并症，透析患者面临严重并发症甚至死亡的高风险，少数族裔的比例过高，尤其是黑人和西班牙裔。超过 85% 的血液透析患者每周 3 次前往透析机构接受维持生命的服务无法就地避难，因此迫切需要确定 COVID-19 传播途径的特征。在透析患者和诊所中，识别潜在的冠状病毒携带者，并制定遏制传播的程序。通过定期的医疗接触，随着时间的推移，每个患者都会收集大量数据。数据尚未充分用于透析诊所的 COVID-19 预测和控制。在本提案中，我们寻求。利用人口统计学、临床、治疗、实验室、社会经济、血清学、代谢组学、可穿戴和机器集成传感器和 COVID-19 监测数据来开发数学和统计模型并使用数学和统计建模在大量透析诊所中实施。多个数据资源将帮助我们了解 COVID-19 如何在透析设施中传播，识别潜在的我们将在症状出现之前识别出潜在的无症状 COVID-19 患者。开发充分利用高维多模态数据的新颖的数学和统计模型我们和其他透析提供商可以利用血液透析诊所的固有优势来实现这一目标。我们坚信，这种跨学科的努力将会实现并验证所提出的预测模型。提高患者和工作人员的安全，同时为高危人群提供高质量、个性化的护理。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Testing of worn face mask and saliva for SARS-CoV-2.

DOI：
10.3389/fpubh.2023.1237512
发表时间：
2023
期刊：
FRONTIERS IN PUBLIC HEALTH
影响因子：
5.2
作者：
Wang, Xiaoling;Thwin, Ohnmar;Haq, Zahin;Dong, Zijun;Tisdale, Lela;Fuentes, Lemuel Rivera;Grobe, Nadja;Kotanko, Peter
通讯作者：
Kotanko, Peter

SARS-CoV-2 Seropositivity Rates in Patients and Clinical Staff in New York City Dialysis Facilities: Association With the General Population.

DOI：
10.1016/j.xkme.2021.02.010
发表时间：
2021-07
期刊：
Kidney medicine
影响因子：
3.9
作者：
Thwin O;Grobe N;Tapia Silva LM;Ye X;Zhang H;Wang Y;Kotanko P
通讯作者：
Kotanko P

Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration.

通过纵向生物标记注册进行未知时间起源的事件时间分析。

DOI：
10.1080/01621459.2021.2023552
发表时间：
2023
期刊：
Journal of the American Statistical Association
影响因子：
3.7
作者：
Wang,Tianhao;Ratcliffe,SarahJ;Guo,Wensheng
通讯作者：
Guo,Wensheng

Arterial oxygen saturation and hypoxemia in hemodialysis patients with COVID-19.

DOI：
10.1093/ckj/sfab019
发表时间：
2021-04
期刊：
Clinical kidney journal
影响因子：
4.6
作者：
Preciado P;Tapia Silva LM;Ye X;Zhang H;Wang Y;Waguespack P;Kooman JP;Kotanko P
通讯作者：
Kotanko P

SARS-CoV-2 neutralizing antibody response after three doses of mRNA1273 vaccine and COVID-19 in hemodialysis patients.