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We evaluated a two-dose primary series of the AZD1222 vaccine on COVID-19 related hospitalisation, ICU admission or death.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 4,515,280 individuals with a two-dose primary series of AZD1222 vaccine were analysed, where 7,171 individuals had a record of severe COVID-19. Variables with the greatest predictive weight for COVID-19 mortality in vaccinated individuals were age\u2009\u2265\u200985 years, high Cambridge Multi-Morbidity Score, and chronic heart, respiratory and kidney diseases; variables predicting COVID-19 hospitalisation following completed primary series included high CMMS, obesity, and being offered early COVID-19 vaccination in the national vaccine campaign (e.g., vaccinated during the first quarter of 2021); predictors of COVID-19 ICU admission included obesity, female sex, being offered early COVID-19 vaccination in the national vaccine campaign, chronic kidney disease and diabetes. Across models, age\u2009\u2265\u200985 years was highly predictive of mortality and moderately predictive of hospitalisation. However, for ICU admission it was reported as not predictive.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Obesity, chronic heart, respiratory and kidney diseases were the main predictors across models, which is comparable to the scientific literature, validating the explainable AI approach. XGBoost can accurately predict severe outcomes in fully vaccinated individuals. Predictive models built on real-world primary care data can help to timely identify individuals to be prioritised for vaccination booster.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0336449","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:24:36Z","timestamp":1771611876000},"page":"e0336449","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of COVID-19 hospitalisation, ICU admission or death following ChAdOx1 vaccination using artificial intelligence: A clinical predictive model from the English RAVEN 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