Evaluation of serological lateral flow assays for severe acute respiratory syndrome coronavirus-2

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Abstract

Background

COVID-19 has resulted in significant morbidity and mortality worldwide. Lateral flow assays can detect anti-Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) antibodies to monitor transmission. However, standardized evaluation of their accuracy and tools to aid in interpreting results are needed.

Methods

We evaluated 20 IgG and IgM assays selected from available tests in April 2020. We evaluated the assays’ performance using 56 pre-pandemic negative and 56 SARS-CoV-2-positive plasma samples, collected 10–40 days after symptom onset, confirmed by a molecular test and analyzed by an ultra-sensitive immunoassay. Finally, we developed a user-friendly web app to extrapolate the positive predictive values based on their accuracy and local prevalence.

Results

Combined IgG + IgM sensitivities ranged from 33.9 to 94.6%, while combined specificities ranged from 92.6 to 100%. The highest sensitivities were detected in Lumiquick for IgG (98.2%), BioHit for both IgM (96.4%), and combined IgG + IgM sensitivity (94.6%). Furthermore, 11 LFAs and 8 LFAs showed perfect specificity for IgG and IgM, respectively, with 15 LFAs showing perfect combined IgG + IgM specificity. Lumiquick had the lowest estimated limit-of-detection (LOD) (0.1 μg/mL), followed by a similar LOD of 1.5 μg/mL for CareHealth, Cellex, KHB, and Vivachek.

Conclusion

We provide a public resource of the accuracy of select lateral flow assays with potential for home testing. The cost-effectiveness, scalable manufacturing process, and suitability for self-testing makes LFAs an attractive option for monitoring disease prevalence and assessing vaccine responsiveness. Our web tool provides an easy-to-use interface to demonstrate the impact of prevalence and test accuracy on the positive predictive values.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06257-7.

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Most cited references 37

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Mary Charlson, Peter Pompei, Kathy Ales … (1987)

The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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An mRNA Vaccine against SARS-CoV-2 — Preliminary Report

Lisa A Jackson, Evan J Anderson, Nadine G Rouphael … (2020)

Abstract Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and spread globally, prompting an international effort to accelerate development of a vaccine. The candidate vaccine mRNA-1273 encodes the stabilized prefusion SARS-CoV-2 spike protein. Methods We conducted a phase 1, dose-escalation, open-label trial including 45 healthy adults, 18 to 55 years of age, who received two vaccinations, 28 days apart, with mRNA-1273 in a dose of 25 μg, 100 μg, or 250 μg. There were 15 participants in each dose group. Results After the first vaccination, antibody responses were higher with higher dose (day 29 enzyme-linked immunosorbent assay anti–S-2P antibody geometric mean titer [GMT], 40,227 in the 25-μg group, 109,209 in the 100-μg group, and 213,526 in the 250-μg group). After the second vaccination, the titers increased (day 57 GMT, 299,751, 782,719, and 1,192,154, respectively). After the second vaccination, serum-neutralizing activity was detected by two methods in all participants evaluated, with values generally similar to those in the upper half of the distribution of a panel of control convalescent serum specimens. Solicited adverse events that occurred in more than half the participants included fatigue, chills, headache, myalgia, and pain at the injection site. Systemic adverse events were more common after the second vaccination, particularly with the highest dose, and three participants (21%) in the 250-μg dose group reported one or more severe adverse events. Conclusions The mRNA-1273 vaccine induced anti–SARS-CoV-2 immune responses in all participants, and no trial-limiting safety concerns were identified. These findings support further development of this vaccine. (Funded by the National Institute of Allergy and Infectious Diseases and others; mRNA-1273 ClinicalTrials.gov number, NCT04283461).

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Author and article information

Contributors

Sara Suliman: ssuliman1@bwh.harvard.edu

Journal

Journal ID (nlm-ta): BMC Infect Dis

Journal ID (iso-abbrev): BMC Infect Dis

Title: BMC Infectious Diseases

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2334

Publication date (Electronic): 16 June 2021

Publication date PMC-release: 16 June 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 580

Affiliations

[1 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Neurology, , Massachusetts General Hospital, Harvard Medical School, ; Charlestown, Boston, MA USA

[2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Harvard Medical School, ; Boston, MA USA

[3 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Mass General Brigham Innovation, ; Boston, MA USA

[4 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Cardiology Division, , Massachusetts General Hospital, ; Charlestown, MA USA

[5 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Neurosurgery, , Brigham and Women’s Hospital, Harvard Medical School, ; Boston, MA USA

[6 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Department of Medical Oncology and Center for Cancer-Genome Discovery, , Dana-Farber Cancer Institute, ; Boston, MA USA

[7 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, , Harvard Medical School, ; Boston, MA USA

[8 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Regenerative Medicine, , Massachusetts General Hospital, ; Boston, MA USA

[9 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard Stem Cell Institute, ; Cambridge, MA USA

[10 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Psychiatry, , Massachusetts General Hospital, ; Boston, MA USA

[11 ]GRID grid.38142.3c, ISNI 000000041936754X, Wyss Institute for Biologically Inspired Engineering, Harvard University, ; Boston, MA USA

[12 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, , Brigham and Women’s Hospital, Harvard Medical School, ; Boston, MA USA

[13 ]GRID grid.429997.8, ISNI 0000 0004 1936 7531, Sackler School of Biomedical Sciences, Tufts University School of Medicine, ; Boston, MA USA

[14 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Division of Rheumatology, Inflammation and Immunity, , Brigham and Women’s Hospital, ; Boston, MA USA

[15 ]Medical Diagnostic Technology Evaluation, LLC, Carlisle, MA USA

[16 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Systems Biology, , Massachusetts General Hospital, ; Boston, MA USA

[17 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Pathology, , Massachusetts General Hospital, ; Boston, MA USA

[18 ]Wellman Center for Photomedicine, Massachusetts General Research Institute, Boston, MA USA

[19 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Dermatology, , Massachusetts General Hospital, ; Boston, MA USA

[20 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Evergrande Center for Immunologic Diseases, , Brigham and Women’s Hospital, ; Boston, MA USA

[21 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Cardiovascular Division, Department of Medicine, , Brigham and Women’s Hospital, ; Boston, MA USA

[22 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Functional Genomics Laboratory, Channing Division of Network Medicine, , Brigham and Women’s Hospital, ; Boston, MA USA

[23 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Cancer Research, , Massachusetts General Hospital, Harvard Medical School, ; Charlestown, MA USA

[24 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Division of Nephrology and Endocrine Unit Department of Medicine, , Massachusetts General Hospital, ; Boston, MA USA

[25 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Cancer Center Protocol Office, , Massachusetts General Hospital, ; Boston, MA USA

[26 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Preventative Medicine, , Brigham and Women’s Hospital, Harvard Medical School, ; Boston, MA USA

[27 ]GRID grid.38142.3c, ISNI 000000041936754X, Radiology and pathology, , Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA

[28 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Brigham Research Institute, Brigham and Women’s Hospital, ; Boston, MA USA

[29 ]GRID grid.38142.3c, ISNI 000000041936754X, Immunology Program, , Harvard Medical School, ; Boston, MA USA

[30 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Cellular Immunotherapy Program, Cancer Center, , Massachusetts General Hospital, ; Boston, MA USA

[31 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Radiation Oncology, , Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA

[32 ]Folia Health, Inc., Cambridge, MA USA

[33 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Vincent Center for Reproductive Biology, Department of Obstetrics and Gynecology, , Massachusetts General Hospital, ; Boston, MA USA

[34 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, Department of Biology, , Northeastern University, ; Boston, MA USA

[35 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, College of Science, , Northeastern University, ; Boston, MA USA

[36 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Mass General Brigham COVID Center for Innovation, Diagnostics Accelerator, ; Boston, MA USA

Article

Publisher ID: 6257

DOI: 10.1186/s12879-021-06257-7

PMC ID: 8206878

SO-VID: 680c35b3-14a5-4f31-ac3a-08efef7aaa5c

License:

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