COVID-19 Research Paper Advance Articles
Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China
- 1 Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- 2 Department of Pulmonary Disease, The Third People’s Hospital of Yichang, Yichang, Hubei, China
- 3 Department of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- 4 Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
Received: August 20, 2020 Accepted: November 8, 2020 Published: December 9, 2020https://doi.org/10.18632/aging.202261
How to Cite
Copyright: © 2020 Chen 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.
Objectives: We aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors.
Methods: A total of 370 patients with COVID-19 were consecutively enrolled at The Third People’s Hospital of Yichang from January to March, 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI).
Results: 326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (p <0.05) relative to mild or ordinary COVID-19. Due to strong pairwise correlations, only five factors, including diagnostic delay (OR, 95% CI, p: 1.08, 1.02 to 1.17, 0.048), albumin (0.82, 0.75 to 0.91, <0.001), lactate dehydrogenase (1.56, 1.14 to 2.13, 0.011), white blood cell (1.27, 1.08 to 1.50, 0.004), and neutrophil (1.40, 1.16 to 1.70, <0.001), were retained for model construction and performance assessment. The nomogram model based on the five factors had good prediction capability and accuracy (C-index: 90.6%).
Conclusions: Our findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.