A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Yao Zhang; Jiajia Han; Feng Sun; Yue Guo; Yifei Guo; Haoxiang Zhu; Feng Long; Zhijie Xia; Shanlin Mao; Hui Zhao; Zi Ge; Jie Yu; Yongmei Zhang; Lunxiu Qin; Ke Ma; Richeng Mao; Jiming Zhang

doi:10.3389/fmicb.2022.1031231

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Front Microbiol. 2022 Dec 19:13:1031231. doi: 10.3389/fmicb.2022.1031231. eCollection 2022.

Authors

Yao Zhang¹, Jiajia Han¹, Feng Sun¹, Yue Guo¹, Yifei Guo¹, Haoxiang Zhu¹, Feng Long², Zhijie Xia³, Shanlin Mao³, Hui Zhao³, Zi Ge³, Jie Yu¹, Yongmei Zhang¹, Lunxiu Qin⁴, Ke Ma³, Richeng Mao¹, Jiming Zhang^{1

5

6}

Affiliations

¹ Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
² Department of Respiratory Medicine, Huashan Hospital North, Fudan University, Shanghai, China.
³ Department of Emergency and Acute Critical Care, Huashan Hospital North, Fudan University, Shanghai, China.
⁴ Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China.
⁵ Shanghai Institute of Infectious Diseases and Biosecurity, Key Laboratory of Medical Molecular Virology (MOE/MOH), Shanghai Medical College, Fudan University, Shanghai, China.
⁶ Department of Infectious Diseases, Jing'An Branch of Huashan Hospital, Fudan University, Shanghai, China.

Abstract

Background: The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness.

Methods: A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed.

Results: The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^⁹/L, white blood cell > 10*10^⁹/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit.

Conclusion: The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Keywords: COVID-19; critical illness; omicron; risk factors; severe acute respiratory syndrome coronavirus 2.

Grants and funding

This study was financially supported by Shanghai Municipal Science and Technology Major Project (ZD2021CY001) and Shanghai Key Clinical Specialty Construction Program (ZK2019B24).