دورية أكاديمية
أعرض في EDS

Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

التفاصيل البيبلوغرافية
العنوان: Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.
المؤلفون: Castro Dopico X; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., Muschiol S; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden.; Department of Clinical Microbiology Karolinska University Hospital Stockholm Sweden., Grinberg NF; Cambridge Institute of Therapeutic Immunology & Infectious Disease University of Cambridge Cambridge UK., Aleman S; Department of Infectious Diseases Karolinska University Hospital Huddinge Sweden., Sheward DJ; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., Hanke L; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., Ahl M; Department of Infectious Diseases Karolinska University Hospital Huddinge Sweden., Vikström L; Department of Clinical Microbiology Umeå Universitet Umeå Sweden., Forsell M; Department of Clinical Microbiology Umeå Universitet Umeå Sweden., Coquet JM; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., McInerney G; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., Dillner J; Division of Pathology Department of Laboratory Medicine Karolinska Institutet Huddinge Sweden., Bogdanovic G; Cambridge Institute of Therapeutic Immunology & Infectious Disease University of Cambridge Cambridge UK., Murrell B; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden., Albert J; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden.; Department of Clinical Microbiology Karolinska University Hospital Stockholm Sweden., Wallace C; Cambridge Institute of Therapeutic Immunology & Infectious Disease University of Cambridge Cambridge UK.; Medical Research Council Biostatistics Unit University of Cambridge Cambridge UK., Karlsson Hedestam GB; Department of Microbiology, Tumor and Cell Biology Karolinska Institutet Stockholm Sweden.
المصدر: Clinical & translational immunology [Clin Transl Immunology] 2022 Mar 02; Vol. 11 (3), pp. e1379. Date of Electronic Publication: 2022 Mar 02 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: John Wiley & Sons Australia, Ltd. on behalf of Australasian Society for Immunology Inc Country of Publication: Australia NLM ID: 101638268 Publication Model: eCollection Cited Medium: Print ISSN: 2050-0068 (Print) Linking ISSN: 20500068 NLM ISO Abbreviation: Clin Transl Immunology Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: 2018- : [Milton, Queensland] : John Wiley & Sons Australia, Ltd. on behalf of Australasian Society for Immunology Inc.
Original Publication: [London] : Nature Publishing Group, 2012-
مستخلص: Objectives: Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples.
Methods: Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals ( n  = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus ( n  = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose.
Results: In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% ( n =  367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls ( n  = 595). In contrast, SVM-LDA classified 6.4% ( n  = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% ( n  = 230) to have a 50-99% likelihood, and 4.0% ( n  = 203) to have a 10-49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts.
Conclusion: Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.
Competing Interests: The study authors declare no competing financial interests that could compromise the study. CW also receives funding from GlaxoSmithKline and Merck Sharp & Dohme; these funders had no role in the design, analysis or interpretation of this study. The views expressed are those of the authors.
(© 2022 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.)
References: J Clin Invest. 2020 Oct 1;130(10):5235-5244. (PMID: 32634129)
N Engl J Med. 2020 Oct 29;383(18):1724-1734. (PMID: 32871063)
Vaccines (Basel). 2021 Dec 31;10(1):. (PMID: 35062724)
J Med Virol. 2020 Oct;92(10):2243-2247. (PMID: 32510168)
Nat Immunol. 2021 Jan;22(1):25-31. (PMID: 33154590)
Emerg Microbes Infect. 2021 Dec;10(1):1495-1498. (PMID: 34232116)
Nature. 2020 Oct;586(7830):589-593. (PMID: 32785213)
Nature. 2021 Mar;591(7851):639-644. (PMID: 33461210)
N Engl J Med. 2021 Sep 9;385(11):1049-1051. (PMID: 34260850)
Clin Microbiol Infect. 2022 Jan;28(1):139.e1-139.e4. (PMID: 34508885)
Lancet Glob Health. 2021 May;9(5):e559-e560. (PMID: 33705691)
J Intern Med. 2022 Jan;291(1):32-50. (PMID: 34352148)
Science. 2020 Mar 13;367(6483):1260-1263. (PMID: 32075877)
N Engl J Med. 2020 Jun 11;382(24):2302-2315. (PMID: 32289214)
N Engl J Med. 2020 Nov 12;383(20):1920-1931. (PMID: 32663912)
J Allergy Clin Immunol. 2021 Feb;147(2):545-557.e9. (PMID: 33221383)
Nat Med. 2020 Aug;26(8):1200-1204. (PMID: 32555424)
Nat Med. 2020 Jun;26(6):845-848. (PMID: 32350462)
Nat Commun. 2020 Jul 6;11(1):3436. (PMID: 32632160)
Nat Microbiol. 2020 Dec;5(12):1598-1607. (PMID: 33106674)
Science. 2020 Nov 27;370(6520):. (PMID: 32994364)
Nat Commun. 2020 Nov 11;11(1):5703. (PMID: 33177504)
PLoS One. 2020 Oct 21;15(10):e0241104. (PMID: 33085715)
Nature. 2021 Apr;592(7855):616-622. (PMID: 33567448)
Nat Med. 2021 Jul;27(7):1205-1211. (PMID: 34002089)
J Intern Med. 2021 Sep;290(3):666-676. (PMID: 34008203)
Nat Commun. 2020 Sep 4;11(1):4420. (PMID: 32887876)
Cell Host Microbe. 2021 Jun 9;29(6):917-929.e4. (PMID: 33984285)
Clin Microbiol Infect. 2021 Mar;27(3):331-340. (PMID: 33228974)
Cell Rep Med. 2021 Apr 20;2(4):100252. (PMID: 33842900)
معلومات مُعتمدة: 107881 United Kingdom WT_ Wellcome Trust; 220788 United Kingdom WT_ Wellcome Trust; MC_UP_1302/5 United Kingdom MRC_ Medical Research Council; MC_UU_00002/4 United Kingdom MRC_ Medical Research Council
فهرسة مساهمة: Keywords: COVID‐19; SARS‐CoV‐2; antibody responses; antibody testing; probability; serology
تواريخ الأحداث: Date Created: 20220314 Latest Revision: 20230209
رمز التحديث: 20230209
مُعرف محوري في PubMed: PMC8891432
DOI: 10.1002/cti2.1379
PMID: 35284072
قاعدة البيانات: MEDLINE
الوصف
تدمد:2050-0068
DOI:10.1002/cti2.1379