dbGaP Study Accession: phs002657

NIH Institute/Center: NIDDK

RADx Data Program: RADx-rad

Release Date: 11/07/2022

DOI: 10.60773/073r-9g87

Study Description: 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. This study sought 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 helped to understand how COVID-19 spreads in dialysis facilities, identify potential COVID-19 patients before symptoms appear, and identify potential asymptomatic COVID-19 patients. Novel mathematical and statistical models were developed that fully utilize the high dimensional multimodal data available. This study capitalized on the intrinsic advantages of hemodialysis clinics to implement and validate the proposed prediction models. There was a firm belief that this cross-disciplinary effort would improve patient and staff safety while delivering high-quality, individualized care to a high-risk population.

Updated Date: 01/18/2024

Principal Investigator: Wang, Yuedong

Has Data Files: Yes

Study Domain: Artificial Intelligence and Machine Learning; Multimodal Surveillance

Data Collection Method: Wearable; Real-World Data

Keywords: Infectious Diseases; Kidney Disease; Predictive Modeling; Patient Safety; Prevention; Transmission in Dialysis Facilities; COVID Transmission; Bioengineering; Coronaviruses Diagnostics and Prognostics; Data Science

Study Design: Longitudinal Cohort

Multi-Center Study: TRUE

Study Sites: University of California-Santa Barbara; University of Pennsylvania; Renal Research Institute; Fresenius Medical Care North America

Data Types: Physical Activity; Clinical; Electronic Medical Records; Metabolomic; Other

Data Types, Other: Only a subset of total cohort for wearable data expected (N=30); Wearables Data

Study Start Date: 12/21/2021

Study End Date: 11/30/2023

Species: Human Data

Estimated Cohort Size: 120

Study Population Focus: Dialysis Patients; Adults; African American; Hispanic and Latino; Underserved/Vulnerable Population

Publication URL: https://pubmed.ncbi.nlm.nih.gov/37588769/; https://pubmed.ncbi.nlm.nih.gov/33878507/; https://pubmed.ncbi.nlm.nih.gov/33898967/; https://pubmed.ncbi.nlm.nih.gov/34230102/; https://pubmed.ncbi.nlm.nih.gov/34378318/; https://pubmed.ncbi.nlm.nih.gov/34530746/; https://pubmed.ncbi.nlm.nih.gov/36031854/; https://pubmed.ncbi.nlm.nih.gov/36106337/; https://pubmed.ncbi.nlm.nih.gov/36594047/; https://pubmed.ncbi.nlm.nih.gov/37771511/; https://pubmed.ncbi.nlm.nih.gov/33655270/; https://pubmed.ncbi.nlm.nih.gov/33685976/; https://pubmed.ncbi.nlm.nih.gov/33878507/; https://pubmed.ncbi.nlm.nih.gov/33898967/; https://pubmed.ncbi.nlm.nih.gov/34094520/; https://pubmed.ncbi.nlm.nih.gov/34230102/; https://pubmed.ncbi.nlm.nih.gov/34378318/; https://pubmed.ncbi.nlm.nih.gov/34530746/; https://pubmed.ncbi.nlm.nih.gov/36031854/; https://pubmed.ncbi.nlm.nih.gov/36106337/; https://pubmed.ncbi.nlm.nih.gov/36594047/; https://pubmed.ncbi.nlm.nih.gov/37771511/

Acknowledgement Statement: This study was supported through funding, 5R01DK130067-03, for the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) as part of the RADx-rad program. The study PI, Dr. Yuedong Wang acknowledges support for this study from the National Institutes of Health (NIH). A special acknowledgment and gratitude to our collaborators, the research teams, and to the research participants that made this study possible. Approved users should acknowledge the provision of data access by dbGaP for accession phs002657.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: RFA-OD-20-016

NIH Grant or Contract Number(s): 5R01DK130067-03

Consent/Data Use Limitations: General Research Use