Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian: A Case–Control Real-World Study - Chen, Tianbin; Zeng, Yongbin; Yang, Di; Ye, Wenjing; Zhang, Jiawei; Lin, Caorui; Huang, Yihao; Ye, Yucheng; Li, Jianwen; Ou, Qishui; Li, Jinming; Liu, Can (Frontiers in Cellular and Infection Microbiology, 2022)
SARS-CoV-2 breakthrough infections have been reported because 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, 6189 vaccinated individuals consisted 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 city, Fujian province of China were included. Those vaccinated individuals were randomly split into a training (70%) and a validation (30%) cohort. In the training cohort, a visualized nomogram built based on the stepwise multivariate logistic regression. The 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 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 would be helpful in the management of SARS-CoV-2 breakthrough infection.