Abstract
SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780-0.858) and 0.838 (95% CI, 0.778-0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection.
Keywords: SARS-CoV-2 breakthrough infection; model; nomogram; prediction; vaccinated individuals.
【저자키워드】 nomogram, prediction, Model, SARS-CoV-2 breakthrough infection, vaccinated individuals, 【초록키워드】 SARS-CoV-2, Efficacy, Vaccine, variant, nomogram, delta variant, China, Probability, Retrospective study, Cohort, outbreak, management, Control, Breakthrough infection, predict, AUC, observation, 95% CI, calibration, decision, multivariate logistic regression, validation cohort, validation cohorts, vaccinated individuals, vaccinated individual, benefit, Randomly, Xiamen, reported, indicated, reduced, 【제목키워드】 SARS-CoV-2,