Clinical characteristics and risk factors for prediction of severity in patients with COVID-19: a retrospective multicentre study.

Authors

Maidina Abudouaini, Wenjuan Zeng, Shengtao Zeng, Bing Liu, Liang Dong, Amar Aynazar, Dilare Asimu, Jinquan Ma, Dongmei Lu

Published in

Journal of infection in developing countries. Volume 20. Issue 7. Pages 932-943. Jul 31, 2026. Epub Jul 31, 2026.

Abstract

This study aimed to investigate the clinical characteristics of patients infected with coronavirus disease 2019 (COVID-19) and to identify risk factors associated with severe infection among Chinese patients.
We collected demographic data, clinical characteristics, and laboratory test results at admission for COVID-19 patients hospitalized in one of two designated tertiary hospitals in Yili Prefecture, Xinjiang Province, between July 2022 and October 2022. Patients were categorized into Group A (asymptomatic, mild, and moderate cases) and Group B (severe and critical cases) based on disease severity. Multivariate regression analysis was conducted to identify risk factors for severe disease, and a nomogram prediction model was developed using these factors. Additionally, stratified analyses were performed by comorbidity status.
Multivariate analysis indicated that older age, male sex, elevated urea nitrogen, higher D-dimer levels, and combined laboratory parameters at admission were positively associated with disease severity. The predictive performance of these factors, measured by area under the curve, was 0.872 (95% CI: 0.819-0.925), 0.613 (95% CI: 0.514-0.712), 0.813 (95% CI: 0.724-0.902), 0.790 (95% CI: 0.717-0.862), and 0.931 (95% CI: 0.891-0.971), respectively. Conversely, vaccination appeared to mitigate progression to severe disease to some extent.
Patient age, sex, urea nitrogen, D-dimer, and serum creatinine levels at admission, as well as vaccination status and comorbidities, significantly influence disease progression in COVID-19 patients. Clinical management can be optimized by tailoring early warning systems and intervention strategies based on these patient-specific risk factors, thereby supporting informed decisions for diagnosis, treatment, prevention, and control.

PMID:
42566341
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

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