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. 2021 Nov 18:2021:7711056.
doi: 10.1155/2021/7711056. eCollection 2021.

The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

Zhichuang Lian  1 , Yafang Li  1 , Wenyi Wang  2 , Wei Ding  2 , Zongxin Niu  2 , Xiaohong Yang  2 , Chao Wu  2
Affiliations

The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

Zhichuang Lian et al. Emerg Med Int. .

Abstract

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients' admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466-18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227-87.021), cough (OR = 5517, 95% CI 0.258-65.024), and venous thrombosis (OR = 7322, 95% CI 0.278-95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients' ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was -3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients' conditions and providing early intervention for those with risk factors.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Analysis of the proportion of single symptoms in confirmed COVID-19 patients.
Figure 2
Figure 2
Underlying diseases in COVID-19 patients.
Figure 3
Figure 3
Underlying diseases in confirmed COVID-19 patients of different severities (62% of the severe and critical cases had underlying diseases, compared to 8.9% of the mild and common cases).
Figure 4
Figure 4
ROC curve analysis of risk factors predicting severe COVID-19.
Figure 5
Figure 5
ROC curve of risk factors for predicting COVID-19 patients developing severe illnesses.
See this image and copyright information in PMC

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References

    1. Rubin E. J., Baden L. R., Morrissey S., Campion E. W. Medical Journals and the 2019-nCoV outbreak. New England Journal of Medicine . 2020;382(9):p. 866. doi: 10.1056/nejme2001329. - DOI - PubMed
    1. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nature Microbiology . 2020;5(4):536–544. - PMC - PubMed
    1. Zhang J. J. Y., Lee K. S., Ang L. W., Leo Y. S., Young B. E. Risk factors for severe disease and efficacy of treatment in patients infected with COVID-19: a systematic review, meta-analysis, and meta-regression analysis. Clinical Infectious Diseases . 2020;71(16):2199–2206. doi: 10.1093/cid/ciaa576. - DOI - PMC - PubMed
    1. China Health Commission Issued. New Coronavirus pneumonia diagnosis and treatment plan. Chinese Medical Information Guide . 2020;35(6):p. 23.
    1. Nogrady B. What the data say about asymptomatic COVID infections. Nature . 2020;587(7835):534–535. doi: 10.1038/d41586-020-03141-3. - DOI - PubMed

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