Dong, Yi-Min, Sun, Jia, Li, Yi-Xin, Chen, Qian, Liu, Qing-Quan, Sun, Zhou, Pang, Ran, Chen, Fei, Xu, Bing-Yang, Manyande, Anne ORCID: https://orcid.org/0000-0002-8257-0722, Clark, Taane G, Li, Jin-Ping, Orhan, Ilkay Erdogan, Tian, Yu-Ke, Wang, Tao, Wu, Wei and Ye, Da-Wei (2020) Development and validation of a nomogram for assessing survival in patients with COVID-19 pneumonia. Clinical Infectious Diseases. ISSN 1058-4838

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Abstract

Background: The outbreak of coronavirus disease (COVID-19) in 2019 has spread worldwide and continues to cause great threat to peoples’ health as well as put pressure on the accessibility of medical systems. Early prediction of survival of hospitalized patients will help the clinical management of COVID-19, but such a prediction model which is reliable and valid is still lacking.
Methods: We retrospectively enrolled 628 confirmed cases of COVID-19 using positive RT-PCR tests for SARS-CoV-2 in Tongji Hospital in Wuhan, China. These patients were randomly grouped into a training cohort (60%) and a validation cohort (40%). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of patients with COVID-19. A nomogram based on the three variables was built for clinical use. Areas under the ROC curves (AUC), concordance index (C-index) and calibration curve were used to evaluate the efficiency of the nomogram in both the training and validation cohorts.
Results: Hypertension, higher neutrophil-to-lymphocyte ratio and increased NT-proBNP value were found to be significantly associated with poorer prognosis in hospitalized patients with COVID-19. The three predictors were further used to build a prediction nomogram. The C-index of the nomogram in the training and validation cohorts was 0.901 and 0.892, respectively. The AUC in the training cohort was 0.922 for 14- day and 0.919 for 21-day probability of in-hospital survival, while in the validation cohort was 0.922 and 0.881, respectively. Moreover, the calibration curve for 14- day and 21-day survival also showed high coherence between the predicted and actual probability of survival.
Conclusion: We managed to build a predictive model and constructed a nomogram for predicting in-hospital survival of patients with COVID-19. This model represents good performance and might be utilized clinically in the management of COVID-19.
Keywords:
Coronavirus; COVID-19; nomogram; prediction; survival

Item Type:	Article
Identifier:	10.1093/cid/ciaa963
Additional Information:	This is a pre-copyedited, author-produced version of an article accepted for publication in Clinical Infectious Diseases following peer review. The version of record [Yi-Min Dong, Jia Sun, Yi-Xin Li, Qian Chen, Qing-Quan Liu, Zhou Sun, Ran Pang, Fei Chen, Bing-Yang Xu, Anne Manyande, Taane G Clark, Jin-Ping Li, Ilkay Erdogan Orhan, Yu-Ke Tian, Tao Wang, Wei Wu, Da-Wei Ye, Development and Validation of a Nomogram for Assessing Survival in Patients with COVID-19 Pneumonia, Clinical Infectious Diseases, , ciaa963, https://doi.org/10.1093/cid/ciaa963] is available online at: https://academic.oup.com/cid/article/doi/10.1093/cid/ciaa963/5869870
Keywords:	Coronavirus; COVID-19; nomogram; prediction; survival
Subjects:	Medicine and health > Health promotion and public health > Infection prevention
Related URLs:	Publisher
Depositing User:	Anne Manyande
Date Deposited:	28 Jul 2020 17:20
Last Modified:	04 Nov 2024 11:47
URI:	https://repository.uwl.ac.uk/id/eprint/7215

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