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Bai WH, Yang JJ, Liu Z, Ning WS, Mao Y, Zhou CL, Cheng L. Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19. Heliyon 2024; 10:e31380. [PMID: 38803927 PMCID: PMC11129089 DOI: 10.1016/j.heliyon.2024.e31380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Objective Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19). Methods Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort. Result In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027-2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253-2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042-1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707-3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003-1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001-1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440-0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641-0.923) and 0.814 (95 % CI: 0.702-0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765-0.962) and 0.875 (95 % CI: 0.741-0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value. Conclusion In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.
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Affiliation(s)
- Wen-Hui Bai
- Department of Hepatobiliary Surgery, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430200, China
| | - Jing-Jing Yang
- Department of Critical Care Medicine, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430200, China
| | - Zhou Liu
- Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430000, China
| | - Wan-Shan Ning
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, China
| | - Yong Mao
- Department of Vascular Surgery, North Campus of Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 201900, China
| | - Chen-Liang Zhou
- Department of Critical Care Medicine, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430200, China
| | - Li Cheng
- Department of Critical Care Medicine, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430200, China
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Jiang J, Zhong W, Huang W, Gao Y, He Y, Li X, Liu Z, Zhou H, Fu Y, Liu R, Zhang W. Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China. Ther Clin Risk Manag 2022; 18:579-591. [PMID: 35607424 PMCID: PMC9123913 DOI: 10.2147/tcrm.s361936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose 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.
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Affiliation(s)
- Junyi Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
- Aier Eye Institute, Changsha, Hunan, People’s Republic of China
| | - WeiJun Zhong
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - WeiHua Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yongchao Gao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yijing He
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yacheng Fu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Cofoe Medical Technology Co., Ltd, Changsha, People’s Republic of China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
- Correspondence: 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 ;
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