ABSTRACT
The COVID-19 pandemic has led to the commercialization of many antigen-based rapid diagnostic tests (Ag-RDTs), requiring independent evaluations. This report describes the clinical evaluation of the Novel Coronavirus 2019-nCoV Antigen Test (Colloidal Gold) (Beijing Hotgen Biotech Co., Ltd.), at two sites within Brazil and one in the United Kingdom. The collected samples (446 nasal swabs from Brazil and 246 nasopharyngeal samples from the UK) were analyzed by the Ag-RDT and compared to reverse transcription-quantitative PCR (RT-qPCR). Analytical evaluation of the Ag-RDT was performed using direct culture supernatants of SARS-CoV-2 strains from the wild-type (B.1), Alpha (B.1.1.7), Delta (B.1.617.2), Gamma (P.1), and Omicron (B.1.1.529) lineages. An overall sensitivity and specificity of 88.2% (95% confidence interval [CI], 81.3 to 93.3) and 100.0% (95% CI, 99.1 to 100.0), respectively, were obtained for the Brazilian and UK cohorts. The analytical limit of detection was determined as 1.0 × 103 PFU/mL (Alpha), 2.5 × 102 PFU/mL (Delta), 2.5 × 103 PFU/mL (Gamma), and 1.0 × 103 PFU/mL (Omicron), giving a viral copy equivalent of approximately 2.1 × 104 copies/mL, 9.0 × 105 copies/mL, 1.7 × 106 copies/mL, and 1.8 × 105 copies/mL for the Ag-RDT, respectively. Overall, while a higher sensitivity was claimed by the manufacturers than that found in this study, this evaluation finds that the Ag-RDT meets the WHO minimum performance requirements for sensitivity and specificity of COVID-19 Ag-RDTs. This study illustrates the comparative performance of the Hotgen Ag-RDT across two global settings and considers the different approaches in evaluation methods. IMPORTANCE Since the beginning of the SARS-CoV-2 pandemic, we have witnessed growing numbers of antigen rapid diagnostic tests (Ag-RDTs) being brought to market. In the United Kingdom, this was somewhat controlled indirectly as the government offered free tests from a small number of companies. However, as this has now ceased, individuals are responsible for their own acquisition of test kits. Similarly in Brazil, as of January 2022, pharmacies and other health care retailers are permitted to sell Ag-RDTs directly to the community. Many of these Ag-RDTs have not been externally evaluated, and results are not readily available to the public. Thus, there is now a need for a transparent evaluation of Ag-RDTs with both analytical and clinical evaluation. We present an independent review of the Novel Coronavirus 2019-nCoV Antigen Test (Colloidal Gold) (Beijing Hotgen Biotech Co., Ltd.), at two sites within Brazil and one in the United Kingdom.
ABSTRACT
There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.-what we collectively call "citizen forums". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.
How might the public be authentically involved in decisions about medical data sharing for artificial intelligence (AI) research? In this paper, we highlight three ways in which public views are used to improve such decisions, namely, through lay representation on data access committees, through patient and public involvement groups, and through a variety of public engagement events we call "citizen forums." Though each approach has common strengths and weaknesses, we argue that they are unable to support each other due to a lack of proper integration. We therefore propose combining them so that they work in a more coordinated way. The combined model, we argue, could be useful for improving the trustworthiness of big data and AI related medical research in the future.
ABSTRACT
Point-of-care tests for SARS-CoV-2 could enable rapid rule-in and/or rule-out of COVID-19, allowing rapid and accurate patient cohorting and potentially reducing the risk of nosocomial transmission. As COVID-19 begins to circulate with other more common respiratory viruses, there is a need for rapid diagnostics to help clinicians test for multiple potential causative organisms simultaneously.However, the different technologies available have strengths and weaknesses that must be understood to ensure that they are used to the benefit of the patient and healthcare system. Device performance is related to the deployed context, and the diagnostic characteristics may be affected by user experience.This practice review is written by members of the UK's COVID-19 National Diagnostic Research and Evaluation programme. We discuss relative merits and test characteristics of various commercially available technologies. We do not advocate for any given test, and our coverage of commercially supplied tests is not intended to be exhaustive.