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<p>A clinical case‒control study was conducted to identify risk factors for severe COVID-19 and to develop a predictive risk model to provide a reference for the dynamic assessment of the severity of disease in COVID-19 patients. A total of 410 patients with COVID-19 were included in the study, of whom 132 had severe or critical cases. The clinical data of the patients were collected, and the variables were subsequently screened via LASSO regression analysis and 10-fold cross-validation. The screened variables were subjected to multifactorial logistic regression analysis to screen out the independent risk factors for patients with severe or critical illnesses, and the independent risk factors were integrated to construct a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA), showing good predictive accuracy. Five variables, including the respiratory rate (R), systolic blood pressure (SBP), plasma albumin (ALB), lactate dehydrogenase (LDH), and C-reactive protein (CRP), were ultimately included to construct a clinical prediction model, with an area under the curve (AUC) of 0.86 (CI 0.82–0.90%). The clinical prediction model constructed in this study using simple clinical indicators can assist in the clinical prediction and identification of patients with heavy or critical COVID-19.</p>

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Risk factors for severe COVID-19 and development of a predictive model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月2日

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  • Ling Zhang,
  • Xinran Li,
  • Ziyan Wang,
  • Lei Zhao,
  • Huixia Gao,
  • Conghui Liu,
  • Jing Bai,
  • Tiejun Liu,
  • Weibin Chen,
  • Wenqiang Li,
  • Jingshan Bai,
  • Aishuang Fu,
  • Yanlei Ge

摘要

A clinical case‒control study was conducted to identify risk factors for severe COVID-19 and to develop a predictive risk model to provide a reference for the dynamic assessment of the severity of disease in COVID-19 patients. A total of 410 patients with COVID-19 were included in the study, of whom 132 had severe or critical cases. The clinical data of the patients were collected, and the variables were subsequently screened via LASSO regression analysis and 10-fold cross-validation. The screened variables were subjected to multifactorial logistic regression analysis to screen out the independent risk factors for patients with severe or critical illnesses, and the independent risk factors were integrated to construct a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA), showing good predictive accuracy. Five variables, including the respiratory rate (R), systolic blood pressure (SBP), plasma albumin (ALB), lactate dehydrogenase (LDH), and C-reactive protein (CRP), were ultimately included to construct a clinical prediction model, with an area under the curve (AUC) of 0.86 (CI 0.82–0.90%). The clinical prediction model constructed in this study using simple clinical indicators can assist in the clinical prediction and identification of patients with heavy or critical COVID-19.