TY - JOUR
T1 - Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian
T2 - A Case–Control Real-World Study
AU - Chen, Tianbin
AU - Zeng, Yongbin
AU - Yang, Di
AU - Ye, Wenjing
AU - Zhang, Jiawei
AU - Lin, Caorui
AU - Huang, Yihao
AU - Ye, Yucheng
AU - Li, Jianwen
AU - Ou, Qishui
AU - Li, Jinming
AU - Liu, Can
N1 - Publisher Copyright:
Copyright © 2022 Chen, Zeng, Yang, Ye, Zhang, Lin, Huang, Ye, Li, Ou, Li and Liu.
PY - 2022/6/23
Y1 - 2022/6/23
N2 - 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.
AB - 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.
KW - SARS-CoV-2 breakthrough infection
KW - model
KW - nomogram
KW - prediction
KW - vaccinated individuals
UR - https://www.scopus.com/pages/publications/85133853560
U2 - 10.3389/fcimb.2022.932204
DO - 10.3389/fcimb.2022.932204
M3 - 文章
C2 - 35811681
AN - SCOPUS:85133853560
SN - 2235-2988
VL - 12
JO - Frontiers in Cellular and Infection Microbiology
JF - Frontiers in Cellular and Infection Microbiology
M1 - 932204
ER -
可持续发展目标 3 良好健康与福祉