Prognostic Value of a Clinical Biochemistry-Based Nomogram for Coronavirus Disease 2019

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.

Automated summary

A clinical biochemistry-based nomogram was developed to predict the progression of COVID-19, with CEA, WBC, CRP, PCT, Fer, D-dimer, Neu%, and L% identified as independent prognostic factors. The nomogram constructed using these biomarkers offers guidance for the diagnosis and treatment of COVID-19.