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However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. This study aimed to develop a machine learning-based model able to predict the humoral response to mRNA vaccines in PLWH and to assess the impact of demographic and clinical variables on antibody production over\u00a0time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Different machine learning\u00a0algorithms have been compared in the setting of a longitudinal observational study involving 497 PLWH, after primary and booster\u00a0SARS-CoV-2 mRNA vaccination. Both Generalized Linear Models and non-linear Models (Tree Regression and Random Forest) were trained and tested.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Non-linear algorithms showed better ability to predict vaccine-elicited humoral responses. The best-performing Random\u00a0Forest model identified a few variables as more influential, within 39 clinical, demographic, and immunological factors. In particular, previous SARS-CoV-2 infection, BMI, CD4 T-cell count and CD4\/CD8 ratio were positively associated with the primary cycle immunogenicity, yet their predictive value diminished with the administration of booster doses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In the present work we have built a non-linear Random\u00a0Forest model capable of accurately predicting humoral responses to SARS-CoV-2 mRNA vaccination, and identifying relevant factors that influence the vaccine response in PLWH. In clinical contexts, the application of this model provides promising opportunities for predicting individual vaccine responses, thus facilitating the development of vaccination strategies tailored for PLWH.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12967-024-05147-1","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T08:02:04Z","timestamp":1715068924000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach"],"prefix":"10.1186","volume":"22","author":[{"given":"Giorgio","family":"Montesi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Augello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacopo","family":"Polvere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Marchetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donata","family":"Medaglini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4585-7783","authenticated-orcid":false,"given":"Annalisa","family":"Ciabattini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"5147_CR1","doi-asserted-by":"publisher","first-page":"e554","DOI":"10.1016\/S2352-3018(20)30164-8","volume":"7","author":"P Vizcarra","year":"2020","unstructured":"Vizcarra P, P\u00e9rez-El\u00edas MJ, Quereda C, Moreno A, Vivancos MJ, Dronda F, et al. 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