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A machine learning model for predicting serum neutralizing activity against Omicron SARS-CoV-2 BA.2 and BA.4/5 sublineages in the general population.
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- Author(s): Camacho J;Camacho J; Albert E; Albert E; Álvarez-Rodríguez B; Álvarez-Rodríguez B; Rusu L; Rusu L; Zulaica J; Zulaica J; Moreno AR; Moreno AR; Peiró S; Peiró S; Geller R; Geller R; Navarro D; Navarro D; Navarro D; Navarro D; Giménez E; Giménez E; Giménez E
- Source:
Journal of medical virology [J Med Virol] 2023 Apr; Vol. 95 (4), pp. e28739.- Publication Type:
Journal Article; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 7705876 Publication Model: Print Cited Medium: Internet ISSN: 1096-9071 (Electronic) Linking ISSN: 01466615 NLM ISO Abbreviation: J Med Virol Subsets: MEDLINE
- Publication Information: Publication: New York Ny : Wiley-Liss
Original Publication: New York, Liss. - Subject Terms:
- Abstract: Supervised machine learning (ML) methods have been used to predict antibody responses elicited by COVID-19 vaccines in a variety of clinical settings. Here, we explored the reliability of a ML approach to predict the presence of detectable neutralizing antibody responses (NtAb) against Omicron BA.2 and BA.4/5 sublineages in the general population. Anti-SARS-CoV-2 receptor-binding domain (RBD) total antibodies were measured by the Elecsys® Anti-SARS-CoV-2 S assay (Roche Diagnostics) in all participants. NtAbs against Omicron BA.2 and BA4/5 were measured using a SARS-CoV-2 S pseudotyped neutralization assay in 100 randomly selected sera. A ML model was built using the variables of age, vaccination (number of doses) and SARS-CoV-2 infection status. The model was trained in a cohort (TC) comprising 931 participants and validated in an external cohort (VC) including 787 individuals. Receiver operating characteristics analysis indicated that an anti-SARS-CoV-2 RBD total antibody threshold of 2300 BAU/mL best discriminated between participants either exhibiting or not detectable Omicron BA.2 and Omicron BA.4/5-Spike targeted NtAb responses (87% and 84% precision, respectively). The ML model correctly classified 88% (793/901) of participants in the TC: 717/749 (95.7%) of those displaying ≥2300 BAU/mL and 76/152 (50%) of those exhibiting antibody levels <2300 BAU/mL. The model performed better in vaccinated participants, either with or without prior SARS-CoV-2 infection. The overall accuracy of the ML model in the VC was comparable. Our ML model, based upon a few easily collected parameters for predicting neutralizing activity against Omicron BA.2 and BA.4/5 (sub)variants circumvents the need to perform not only neutralization assays, but also anti-S serological tests, thus potentially saving costs in the setting of large seroprevalence studies.
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- Accession Number: 0 (COVID-19 Vaccines)
0 (Antibodies, Neutralizing)
0 (Antibodies, Viral) - Publication Date: Date Created: 20230515 Date Completed: 20230517 Latest Revision: 20230623
- Publication Date: 20230624
- Accession Number: 10.1002/jmv.28739
- Accession Number: 37185857
- Source:
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