Research Paper|Volume 12, Issue 21|pp 20982—20996

A predictive model for the severity of COVID-19 in elderly patients

Furong Zeng^1,^2,^3,⁴, Guangtong Deng^1,^2,^3,⁴, Yanhui Cui⁵, Yan Zhang⁵, Minhui Dai⁵, Lingli Chen⁵, Duoduo Han⁵, Wen Li⁵, Kehua Guo⁶, Xiang Chen^1,^2,^3,⁴, Minxue Shen^1,^2,^3,^4,⁷, Pinhua Pan⁵

¹Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
²National Clinical Research Center for Geriatric Disorders, Changsha, China
³Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
⁴Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
⁵Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
⁶School of Computer Science and Engineering, Central South University, Changsha, China
⁷Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China

* Equal contribution

Received: April 30, 2020Accepted: August 15, 2020Published: November 10, 2020

https://doi.org/10.18632/aging.103980

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.

Abstract

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.