dbGaP Study Accession: phs002561

NIH Institute/Center: NHLBI

RADx Data Program: RADx-rad

Release Date: 10/18/2023

DOI: 10.60773/8jx9-5s54

Updated Date: 10/18/2023

Study Description: A rapid, sensitive, low-cost diagnostic device for early detection of novel Coronavirus Disease (COVID-19) can significantly change the paradigm in real-time and efficient management of patients and available healthcare resources specifically during this time of increased demand for hospital services. The quantitative real-time polymerase chain reaction (qPCR) represents the most important method for the diagnosis of COVID-19 at its early stage of infection and before the appearance of clinical symptoms. However, qPCR-based molecular diagnostics are bulky, expensive, or lab-based, or require trained personnel for system operation. The majority of the FDA EUA-approved COVID diagnostics are PCR-based which require nucleic acid extraction and amplification and thermal cycling. Such systems also require relatively expensive/bulky equipment for sample processing. Recent studies have shown large variations up to 10,000 fold variations in limit of detection (LoD) of some of the nucleic acid-based and antigen-based COVID-19 diagnostics currently approved under emergency use authorization (EUA). Therefore, there is still a need in developing POC diagnostics and particularly antigen-based SARS-CoV-2 detection devices with LoD comparable to PCR-based assays. The main goal of this interdisciplinary project was to develop a portable diagnostic system for rapid and sensitive detection of intact SARS-CoV-2 in small volume of COVID-19 patient samples such as anterior/nasopharyngeal/oropharyngeal swab samples using an inexpensive, disposable, and mass-producible microfluidic cartridge.

Principal Investigator: Shafiee, Hadi

Has Data Files: Yes

Study Domain: Medical Device/Tool Development; Rapid Diagnostic Test (RDT)

Data Collection Method: Antigen Testing Device

Keywords: Bioengineering; Hematology; Heart Disease

Study Design: Device Validation Study

Multi-Center Study: FALSE

Data Types: Other; Immunological

Data Types, Other: De-identified COVID-19 patient swab samples to test antigen-based diagnostic assay; The optical signal generated on a disposable microfluidic chip is imaged using a smartphone camera; The smartphone-taken microchip images are analyzed on-phone using deep learning

Study Start Date: 12/21/2020

Study End Date: 07/31/2022

Estimated Cohort Size: 200

Study Population Focus: N/A

Acknowledgement Statement: This study was supported through funding, 4U54HL119145-08, for the National Heart Lung and Blood Institute (NHLBI) as part of the RADx-rad program. This work was also partially supported by other NIH awards R01AI118502, R01AI138800, and R61AI140489. Approved users should acknowledge the provision of data access by dbGaP for accession phs002561.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-HL-13-008

NIH Grant or Contract Number(s): 4U54HL119145-08

Consent/Data Use Limitations: General Research Use