4.5 Article

Prognosis models for severe and critical COVID-19 based on the Charlson and Elixhauser comorbidity indices

Journal

INTERNATIONAL JOURNAL OF MEDICAL SCIENCES
Volume 17, Issue 15, Pages 2257-2263

Publisher

IVYSPRING INT PUBL
DOI: 10.7150/ijms.50007

Keywords

Charlson comorbidity index; comorbidity; Corona Virus Disease; Elixhauser comorbidity index; length of stay; outcome

Funding

  1. National Natural Science Foundation of China [ZY202001-2]
  2. Ministry of Science and Technology [81873949, 81671968]
  3. Major Science and Technology Project of Wenzhou Science and Technology Bureau [WKJ-ZJ-1909]
  4. [2018ZY002]

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Background: Corona Virus Disease 2019 (COVID-19) has become a global pandemic. This study established prognostic scoring models based on comorbidities and other clinical information for severe and critical patients with COVID-19. Material and Methods: We retrospectively collected data from 51 patients diagnosed as severe or critical COVID-19 who were admitted between January 29, 2020, and February 18, 2020. The Charlson (CCI), Elixhauser (ECI), and ageand smoking-adjusted Charlson (ASCCI) and Elixhauser (ASECI) comorbidity indices were used to evaluate the patient outcomes. Results: The mean hospital length of stay (LOS) of the COVID-19 patients was 22.82 +/- 12.32 days; 19 patients (37.3%) were hospitalized for more than 24 days. Multivariate analysis identified older age (OR 1.064, P = 0.018, 95%CI 1.011-1.121) and smoking (OR 3.696, P = 0.080, 95%CI 0.856-15.955) as positive predictors of a long LOS. There were significant trends for increasing hospital LOS with increasing CCI, ASCCI, and ASECI scores (OR 57.500, P = 0.001, 95%CI 5.687-581.399; OR 71.500, P = 0.001, 95%CI 5.689-898.642; and OR 19.556, P = 0.001, 95%CI 3.315-115.372, respectively). The result was similar for the outcome of critical illness (OR 21.333, P = 0.001, 95%CI 3.565-127.672; OR 13.000, P = 0.009, 95%CI 1.921-87.990; OR 11.333, P = 0.008, 95%CI 1.859-69.080, respectively). Conclusions: This study established prognostic scoring models based on comorbidities and clinical information, which may help with the graded management of patients according to prognosis score and remind physicians to pay more attention to patients with high scores.

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