Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection

Tianyu Lu; Qiuhong Man; Xueying Yu; Shuai Xia; Lu Lu; Shibo Jiang; Lize Xiong

doi:10.3389/fimmu.2023.1157892

Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection

Front Immunol. 2023 Mar 1:14:1157892. doi: 10.3389/fimmu.2023.1157892. eCollection 2023.

Authors

Tianyu Lu¹, Qiuhong Man², Xueying Yu², Shuai Xia¹, Lu Lu¹, Shibo Jiang¹, Lize Xiong^{3

4}

Affiliations

¹ Key Laboratory of Medical Molecular Virology Ministry of Education (MOE)/National Health Commission of China (NHC)/Chinese Academy of Medical Sciences (CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Fudan University, Shanghai, China.
² Department of Laboratory Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
³ Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
⁴ Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.

Abstract

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients.

Methods: This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts.

Results: Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 0~1 and nearly 0~0.75 in the training and validation cohorts, correspondingly.

Conclusions: This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.

Keywords: COVID-19; SARS-CoV-2 Omicron variant; cytokines; immune biomarkers; intensive care; prognostic model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / diagnosis
COVID-19* / immunology
Calibration
Hospitalization
Humans
SARS-CoV-2

Supplementary concepts

SARS-CoV-2 variants

Grants and funding

This work was supported by the National Key R&D Program of China (2021YFC2300703 to LL), the National Natural Science Foundation of China (92169112 to SJ; 82002142 to SX), Shanghai Municipal Science and Technology Major Project (ZD2021CY001 to SJ), and the Program of Shanghai Academic/Technology Research Leader (20XD1420300 to LL).