A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines

Jin Zhang; Yuan Meng; Mei Yang; Wudi Hao; Jianhua Liu; Lina Wu; Xiaojun Yu; Yue Zhang; Baoxu Lin; Chonghong Xie; Lili Ge; Zhijie Zhang; Weiwei Tong; Qing Chang; Yong Liu; Yixiao Zhang; Xiaosong Qin

doi:10.1016/j.intimp.2024.112141

A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines

Int Immunopharmacol. 2024 May 10:134:112141. doi: 10.1016/j.intimp.2024.112141. Online ahead of print.

Authors

Jin Zhang¹, Yuan Meng¹, Mei Yang¹, Wudi Hao¹, Jianhua Liu¹, Lina Wu¹, Xiaojun Yu¹, Yue Zhang¹, Baoxu Lin¹, Chonghong Xie¹, Lili Ge¹, Zhijie Zhang¹, Weiwei Tong¹, Qing Chang², Yong Liu¹, Yixiao Zhang³, Xiaosong Qin⁴

Affiliations

¹ Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
² Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
³ Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China. Electronic address: zhangyx@sj-hospital.org.
⁴ Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China. Electronic address: qinxs@sj-hospital.org.

PMID: 38733819
DOI: 10.1016/j.intimp.2024.112141

Abstract

Background: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate.

Objectives: The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections. It aimed to elucidate the synergistic effects of epidemiological factors, humoral immunity, and cellular immunity. Additionally, regression curves were used to determine the correlation between the protective efficacy of the vaccine and the stimulated immune response.

Methods: Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regression, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination.

Results: Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster antibody levels. The study further constructed regression curves for neutralising antibodies, non-switched memory B cells, CD4⁺T cells, and CD8⁺T cells using LASSO combined with the random forest algorithm.

Conclusion: The establishment of an artificial intelligence evaluation system emerges as pivotal for predicting breakthrough infection prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external factors, elucidating key insights to enhance vaccine effectiveness. 3D modelling discerned distinctive interactions between humoral and cellular immunity within prognostic groups (Class 0-2). This underscores the critical role of the synergistic effect of humoral immunity, cellular immunity, and epidemiological factors in determining the protective efficacy of COVID-19 vaccines post-booster administration.

Keywords: Artificial intelligence evaluation system; Cellular immunity; Humoral immunity; Prospective cohort.