COVID-19 Research Paper Volume 12, Issue 21 pp 20982—20996
A predictive model for the severity of COVID-19 in elderly patients
- 1 Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- 2 National Clinical Research Center for Geriatric Disorders, Changsha, China
- 3 Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
- 4 Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
- 5 Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- 6 School of Computer Science and Engineering, Central South University, Changsha, China
- 7 Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
Received: April 30, 2020 Accepted: August 15, 2020 Published: November 10, 2020https://doi.org/10.18632/aging.103980
How to Cite
Copyright: © 2020 Zeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Elderly patients with coronavirus disease 2019 (COVID-19) are more likely to develop severe or critical pneumonia, with a high fatality rate. To date, there is no model to predict the severity of COVID-19 in elderly patients. In this study, patients who maintained a non-severe condition and patients who progressed to severe or critical COVID-19 during hospitalization were assigned to the non-severe and severe groups, respectively. Based on the admission data of these two groups in the training cohort, albumin (odds ratio [OR] = 0.871, 95% confidence interval [CI]: 0.809 - 0.937, P < 0.001), d-dimer (OR = 1.289, 95% CI: 1.042 - 1.594, P = 0.019) and onset to hospitalization time (OR = 0.935, 95% CI: 0.895 - 0.977, P = 0.003) were identified as significant predictors for the severity of COVID-19 in elderly patients. By combining these predictors, an effective risk nomogram was established for accurate individualized assessment of the severity of COVID-19 in elderly patients. The concordance index of the nomogram was 0.800 in the training cohort and 0.774 in the validation cohort. The calibration curve demonstrated excellent consistency between the prediction of our nomogram and the observed curve. Decision curve analysis further showed that our nomogram conferred significantly high clinical net benefit. Collectively, our nomogram will facilitate early appropriate supportive care and better use of medical resources and finally reduce the poor outcomes of elderly COVID-19 patients.