Volume 23 Issue 2
Feb.  2025
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ZHANG Lingling, WANG Muqun, GUO Feng. Analysis of clinical characteristics and risk factors of severe COVID-19 patients in the post-epidemic stage[J]. Chinese Journal of General Practice, 2025, 23(2): 215-218. doi: 10.16766/j.cnki.issn.1674-4152.003870
Citation: ZHANG Lingling, WANG Muqun, GUO Feng. Analysis of clinical characteristics and risk factors of severe COVID-19 patients in the post-epidemic stage[J]. Chinese Journal of General Practice, 2025, 23(2): 215-218. doi: 10.16766/j.cnki.issn.1674-4152.003870
shu

Analysis of clinical characteristics and risk factors of severe COVID-19 patients in the post-epidemic stage

doi: 10.16766/j.cnki.issn.1674-4152.003870
  • 1.

    Department of Respiratory and Critical Care Medicine, Suzhou Hospital of Anhui Medical University (Suzhou Municipal Hospital of Anhui Province), Suzhou, Anhui 234000, China

Funds:

 202304295107020074

 SZWJ2023a035

  • Received Date: 2024-06-12
    Available Online: 2025-03-27
    Abstract
  •   Objective  To analyze the clinical features of patients infected with Coronavirus disease 2019 (COVID-19), and to explore reliable clinical indicators for predicting high-risk populations that are likely to progress to severe cases.  Methods  A retrospective analysis was conducted on 85 patients with confirmed diagnosis of SARS-CoV-2 infection treated at the Department of Respiratory and Critical Care Medicine at the Suzhou Hospital of Anhui Medical University from December 2022 to February 2024. Patients were categorized according to the severity of their clinical symptoms, resulting in the formation of two distinct groups: the non-severe group (n = 47) and the severe group (n = 38). The differences in clinical characteristics, blood routine, C-reactive protein (CRP) and T lymphocyte subsets between the two groups were compared. Logistic regression analysis was applied to identify the risk factors of the severe group and a predictive model was constructed. The receiver operating characteristic (ROC) curve was used to compare the predictive value of each risk factor and their combined application in predicting severity.  Results  A comparison of the non-severe group with the severe group revealed a higher incidence of comorbidities in the latter, including diabetes. Additionally, the severe group exhibited significantly lower levels of eosinophils, CD4+T lymphocytes and CD8+T lymphocytes (P > 0.05). Furthermore, a notable difference was observed in the mean BMI between the two groups. The results of the stepwise logistic regression analysis indicated that CD4+T lymphocytes, diabetes, and BMI were significant risk factors for the severe group. The area under the receiver operating characteristic curve (AUC) for CD4+T lymphocytes, BMI and diabetes were 0. 877, 0. 748 and 0. 663, respectively. The sensitivity of CD4+T lymphocytes, BMI and diabetes were 81. 6%, 60. 5% and 36. 8%, respectively; and the specificity were 85. 1%, 93. 6% and 95. 7%, respectively. The AUC for the model constructed by combining the three factors was 0. 928, the sensitivity was 97. 4% and the specificity was 76. 6%.  Conclusion  A reduced count of CD4+T lymphocytes can effectively predict the clinical severity of patients with COVID-19 alone or in conjunction with a high BMI and comorbid diabetes.

     

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