Therapeutics and Clinical Risk Management (May 2022)
Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China
Abstract
Junyi Jiang,1– 5,* WeiJun Zhong,1– 4,* WeiHua Huang,1– 4 Yongchao Gao,1– 4 Yijing He,1– 4 Xi Li,1– 4 Zhaoqian Liu,1– 4 Honghao Zhou,1– 4 Yacheng Fu,1,6 Rong Liu,1– 4 Wei Zhang1– 4 1Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China; 3Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China; 4National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China; 5Aier Eye Institute, Changsha, Hunan, People’s Republic of China; 6Cofoe Medical Technology Co., Ltd, Changsha, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Zhang; Rong Liu, Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China, Tel +86 731 84805380, Fax +86 731 82354476, Email [email protected]; [email protected]: To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).Patients and Methods: A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.Results: A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.Conclusion: We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.Keywords: nomogram, COVID-19, SARS-CoV-2, C-reactive protein, predictive model
