دورية أكاديمية
Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.
العنوان: | Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates. |
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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.) |
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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 |
تدمد: | 2050-0068 |
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DOI: | 10.1002/cti2.1379 |