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Multicenter Study
. 2020 Jul 7:148:e146.
doi: 10.1017/S0950268820001533.

Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China

Affiliations
Multicenter Study

Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China

Kandi Xu et al. Epidemiol Infect. .

Abstract

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596-7.323), age of 40-69 years (OR = 1.586, 95% CI: 0.824-3.053), hypertension (OR = 3.372, 95% CI: 2.185-5.202), ALT >50 μ/l (OR = 3.304, 95% CI: 2.107-5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292-12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42-3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012-1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009-1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585-36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588-95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.

Keywords: COVID-19; determinant; ordinal logistic regression; severity.

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Conflict of interest statement

We declare no competing interests.

Figures

Fig. 1.
Fig. 1.
Kaplan−Meier estimate of OS of COVID-19 patients according to severity of illness.

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References

    1. Huang C et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet (London, England) 395, 497–506. - PMC - PubMed
    1. Chen N et al. (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet (London, England) 395, 507–513. - PMC - PubMed
    1. World Health Organization (2020) Coronavirus disease (COVID-19) situation report -137. https://www.who.int/docs/default-source/sri-lanka-documents/20200605-cov... (Accessed 5 June 2020).
    1. Zhou F et al. (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet (London, England) 395, 1054–1062. - PMC - PubMed
    1. Wu ZY et al. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. Published online: 24 Feb 2020. doi: 10.1001/jama.2020.2648. - DOI - PubMed

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