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

• 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
• 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

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.

摘要 随着年龄的增长和多种合并症，透析患者发生严重并发症甚至死亡的风险很高， 从COVID-19少数民族，特别是黑人和西班牙裔人的代表比例过高。 超过85%的血液透析患者每周三次前往透析机构接受维持生命的治疗。 治疗，不能就地避难。迫切需要描述COVID-19传播途径的特征 在透析患者和诊所，识别潜在的冠状病毒携带者，并制定程序来遏制传播。 通过定期的医疗接触，随着时间的推移，已经为每个患者收集了大量的数据。这些 数据尚未充分用于透析诊所的COVID-19预测和控制。在这一建议中，我们寻求 利用人口统计学、临床、治疗、实验室、社会经济学、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，以开发数学和统计模型 并在大量的透析诊所中实施。数学和统计建模使用 多种数据资源将帮助我们了解COVID-19如何在透析设施中传播， COVID-19患者出现症状之前，并识别潜在的无症状COVID-19患者。我们将 开发新的数学和统计模型，充分利用高维多模态数据 提供给我们和其他透析提供者。我们利用血液透析诊所的固有优势， 实施并验证所提出的预测模型。我们坚信，这种跨学科的努力将 改善患者和工作人员的安全，同时为高危人群提供高质量的个性化护理。

项目成果

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

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

• DOI: 10.3389/fneph.2022.926635
• 发表时间: 2022-01-01
• 期刊: Frontiers in nephrology
• 作者: Wang, Xiaoling;Han, Maggie;Kotanko, Peter
• 通讯作者: Kotanko, Peter

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