Jing Yu1, Lei Nie2, Dongde Wu2, Jian Chen3, Zhifeng Yang4, Ling Zhang5, Dongqing Li6, Xia Zhou7. 1. Department of Blood Transfusion, Tongji Medical College, Wuhan No.1 Hospital/Wuhan Hospital of Traditional Chinese and Western Medicine, Huazhong University of Science and Technology, Wuhan, China. 2. Department of Hepatobiliary Pancreatic Surgery, Tongji Medical College, Hubei Cancer Hospital, Huazhong University of Science and Technology, Wuhan, China. 3. Department of Head and Neck Surgery, Tongji Medical College, Hubei Cancer Hospital, Huazhong University of Science and Technology, Wuhan, China. 4. Department of Thoracic Surgery, Jinyintan Hospital, Wuhan, China. 5. Key Laboratory of Occupational Hazard Identification and Control in Hubei Province, School of Public Health, Wuhan University of Science and Technology, Wuhan, China. 6. Department of Microbiology Medicine, Wuhan University of Basic Medical of Science, Wuhan, China. 7. Department of Respiratory and Critical Care Medicine, Jinyintan Hospital, Wuhan, China.
Abstract
Background: This study aimed to explore the predictive value of a clinical biochemistry-based nomogram in COVID-19. Methods: The plasma or serum concentrations/levels of carcinoembryonic antigen (CEA) and other biomarkers, e.g., C-reactive protein (CRP), white blood cell (WBC), interleukin-6 (IL-6), ferritin (Fer), procalcitonin (PCT), lymphocyte percentage (L%), D-dimer (D2), and neutrophils percentage (Neu%), were assessed in 314 hospitalized patients with confirmed COVID-19. The area under the curve was used to estimate the diagnostic and prognostic value for COVID-19. Cox and logistic regression analyses were used to estimate the independent prognostic risk factors for the survival of patients with COVID-19. Results: Receiver operating characteristic (ROC) curves were used to determine the area under the curve (AUC) values for CEA, IL-6, CRP, PCT, Fer, D-dimer levels and L%, Neu%, and WBC to assess disease classification. The critical values for these markers to predict severe disease type were then determined. The hazard ratio of prognosis for risk of COVID-19 identified CEA, WBC, CRP, PCT, Fer, D-dimer, Neu%, and L% as independent prognostic factors. For the nomogram of overall survival (OS), the C-index was 0.84, demonstrating a good discriminative performance. Conclusions: An OS nomogram for the clinical diagnosis and treatment of COVID-19 was constructed using biomarkers. These data will be useful for the diagnosis, management, and therapy of COVID-19.
Background: This study aimed to explore the predictive value of a clinical biochemistry-based nomogram in COVID-19. Methods: The plasma or serum concentrations/levels of carcinoembryonic antigen (CEA) and other biomarkers, e.g., C-reactive protein (CRP), white blood cell (WBC), interleukin-6 (IL-6), ferritin (Fer), procalcitonin (PCT), lymphocyte percentage (L%), D-dimer (D2), and neutrophils percentage (Neu%), were assessed in 314 hospitalized patients with confirmed COVID-19. The area under the curve was used to estimate the diagnostic and prognostic value for COVID-19. Cox and logistic regression analyses were used to estimate the independent prognostic risk factors for the survival of patients with COVID-19. Results: Receiver operating characteristic (ROC) curves were used to determine the area under the curve (AUC) values for CEA, IL-6, CRP, PCT, Fer, D-dimer levels and L%, Neu%, and WBC to assess disease classification. The critical values for these markers to predict severe disease type were then determined. The hazard ratio of prognosis for risk of COVID-19 identified CEA, WBC, CRP, PCT, Fer, D-dimer, Neu%, and L% as independent prognostic factors. For the nomogram of overall survival (OS), the C-index was 0.84, demonstrating a good discriminative performance. Conclusions: An OS nomogram for the clinical diagnosis and treatment of COVID-19 was constructed using biomarkers. These data will be useful for the diagnosis, management, and therapy of COVID-19.