Bianca A Trombetta1, Savannah E Kandigian1, Robert R Kitchen2,3, Korneel Grauwet4, Lauren L Ritterhouse5,6, Sara Suliman7,8, Pia Kivisäkk Webb1,2, Glenn A Miller3, Charles G Jennings4,9, Sejal Jain10,11, Samara Miller2,12,13,14, Yikai Kuo1,4, Thadryan Sweeney4, Tal Gilboa15,16, Maia Norman15,16,17, Daimon P Simmons18, Christopher E Ramirez1, Melissa Bedard18, Catherine Fink19, Jina Ko15,20, Esmarline J De León Peralta5,21,22, Gerald Watts18, Emma Gomez-Rivas18, Vannessa Davis16, Rocky M Barilla23, Jianing Wang24, Pierre Cunin18, Samuel Bates25, Chevaun Morrison-Smith16, Benjamin Nicholson26, Edmond Wong26, Leena El-Mufti1, Michael Kann26, Anna Bolling1, Brooke Fortin1, Hayden Ventresca24, Wen Zhou27, Santiago Pardo1, Megan Kwock28, Aditi Hazra2,29, Leo Cheng30, Q Rushdy Ahmad15, James A Toombs31, Rebecca Larson32,33, Haley Pleskow26,34, Nell Meosky Luo35, Christina Samaha35, Unnati M Pandya2,36, Pushpamali De Silva21, Sally Zhou37,38, Zakary Ganhadeiro37,38, Sara Yohannes31, Rakeisha Gay31,38, Jacqueline Slavik31, Shibani S Mukerji1, Petr Jarolim11,16, David R Walt15,16,6, Becky C Carlyle1,2. 1. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Boston, MA, USA. 2. Department of Medicine, Harvard Medical School, Boston, MA, USA. 3. Mass General Brigham Innovation, Boston, MA, USA. 4. Cardiology Division, Massachusetts General Hospital, Charlestown, MA, USA. 5. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. 6. Mass General Brigham COVID Center for Innovation, Diagnostics Accelerator, Boston, MA, USA. 7. Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA. ssuliman1@bwh.harvard.edu. 8. Mass General Brigham COVID Center for Innovation, Diagnostics Accelerator, Boston, MA, USA. ssuliman1@bwh.harvard.edu. 9. Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 10. Department of Medical Oncology and Center for Cancer-Genome Discovery, Dana-Farber Cancer Institute, Boston, MA, USA. 11. Department of Pathology, Harvard Medical School, Boston, MA, USA. 12. Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA. 13. Harvard Stem Cell Institute, Cambridge, MA, USA. 14. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. 15. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. 16. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 17. Sackler School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA. 18. Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA. 19. Medical Diagnostic Technology Evaluation, LLC, Carlisle, MA, USA. 20. Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA. 21. Wellman Center for Photomedicine, Massachusetts General Research Institute, Boston, MA, USA. 22. Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA. 23. Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital, Boston, MA, USA. 24. Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 25. Functional Genomics Laboratory, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. 26. Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. 27. Division of Nephrology and Endocrine Unit Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. 28. Cancer Center Protocol Office, Massachusetts General Hospital, Boston, MA, USA. 29. Division of Preventative Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 30. Radiology and pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 31. Brigham Research Institute, Brigham and Women's Hospital, Boston, MA, USA. 32. Immunology Program, Harvard Medical School, Boston, MA, USA. 33. Cellular Immunotherapy Program, Cancer Center, Massachusetts General Hospital, Boston, MA, USA. 34. Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 35. Folia Health, Inc., Cambridge, MA, USA. 36. Vincent Center for Reproductive Biology, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA. 37. Department of Biology, Northeastern University, Boston, MA, USA. 38. College of Science, Northeastern University, Boston, MA, USA.
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.
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.
Authors: Lisa A Jackson; Evan J Anderson; Nadine G Rouphael; Paul C Roberts; Mamodikoe Makhene; Rhea N Coler; Michele P McCullough; James D Chappell; Mark R Denison; Laura J Stevens; Andrea J Pruijssers; Adrian McDermott; Britta Flach; Nicole A Doria-Rose; Kizzmekia S Corbett; Kaitlyn M Morabito; Sijy O'Dell; Stephen D Schmidt; Phillip A Swanson; Marcelino Padilla; John R Mascola; Kathleen M Neuzil; Hamilton Bennett; Wellington Sun; Etza Peters; Mat Makowski; Jim Albert; Kaitlyn Cross; Wendy Buchanan; Rhonda Pikaart-Tautges; Julie E Ledgerwood; Barney S Graham; John H Beigel Journal: N Engl J Med Date: 2020-07-14 Impact factor: 91.245
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Authors: Jeffrey D Whitman; Joseph Hiatt; Cody T Mowery; Brian R Shy; Ruby Yu; Tori N Yamamoto; Ujjwal Rathore; Gregory M Goldgof; Caroline Whitty; Jonathan M Woo; Antonia E Gallman; Tyler E Miller; Andrew G Levine; David N Nguyen; Sagar P Bapat; Joanna Balcerek; Sophia A Bylsma; Ana M Lyons; Stacy Li; Allison Wai-Yi Wong; Eva Mae Gillis-Buck; Zachary B Steinhart; Youjin Lee; Ryan Apathy; Mitchell J Lipke; Jennifer Anne Smith; Tina Zheng; Ian C Boothby; Erin Isaza; Jackie Chan; Dante D Acenas; Jinwoo Lee; Trisha A Macrae; Than S Kyaw; David Wu; Dianna L Ng; Wei Gu; Vanessa A York; Haig Alexander Eskandarian; Perri C Callaway; Lakshmi Warrier; Mary E Moreno; Justine Levan; Leonel Torres; Lila A Farrington; Rita P Loudermilk; Kanishka Koshal; Kelsey C Zorn; Wilfredo F Garcia-Beltran; Diane Yang; Michael G Astudillo; Bradley E Bernstein; Jeffrey A Gelfand; Edward T Ryan; Richelle C Charles; A John Iafrate; Jochen K Lennerz; Steve Miller; Charles Y Chiu; Susan L Stramer; Michael R Wilson; Aashish Manglik; Chun Jimmie Ye; Nevan J Krogan; Mark S Anderson; Jason G Cyster; Joel D Ernst; Alan H B Wu; Kara L Lynch; Caryn Bern; Patrick D Hsu; Alexander Marson Journal: Nat Biotechnol Date: 2020-08-27 Impact factor: 54.908
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