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

Abstract

Introduction: 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.

Methodology: 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.

Results: 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.

Conclusions: 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.