Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Aug 6;14(1):18197.
doi: 10.1038/s41598-024-68946-y.

A predictive model to explore risk factors for severe COVID-19

Fen-Hong Qian #  1 , Yu Cao #  2 , Yu-Xue Liu  2 , Jing Huang  2 , Rong-Hao Zhu  2
Affiliations

A predictive model to explore risk factors for severe COVID-19

Fen-Hong Qian et al. Sci Rep. .

Abstract

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the "rms" package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

Keywords: COVID-19; Clinical characteristics; Neutrophil-to-lymphocyte ratio; Nomogram; Predictive models; Risk factors.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flowchart of patient selection for this study.
Figure 2
Figure 2
Flowchart of the data analysis phase.
Figure 3
Figure 3
LASSO regression path plot: LASSO regression path plot for variable selection obtained by R software.
Figure 4
Figure 4
Tenfold cross-validation results of LASSO regression: show the relationship between log (λ), mean square error (MSE), and the number of variables in the model.
Figure 5
Figure 5
A logistic regression prediction model for severe COVID-19.
Figure 6
Figure 6
A nomogram to predict the severity of COVID-19.
Figure 7
Figure 7
ROC curves for the nomogram. (A): Training group; (B): Validation group.
Figure 8
Figure 8
Calibration curve for predicting the probability of COVID-19 severity. (A): Training group; (B): Validation group.
Figure 9
Figure 9
Decision curve analysis in the prediction of COVID-19 severity. (A): Training group; (B): Validation group.
See this image and copyright information in PMC

References

    1. The species Severe acute respiratory syndrome-related coronavirus. classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol.5, 536–544. 10.1038/s41564-020-0695-z (2020). 10.1038/s41564-020-0695-z - DOI - PMC - PubMed
    1. Roland, L. T., Gurrola, J. G. 2nd., Loftus, P. A., Cheung, S. W. & Chang, J. L. Smell and taste symptom-based predictive model for COVID-19 diagnosis. Int. Forum. Allergy Rhinol.10, 832–838. 10.1002/alr.22602 (2020). 10.1002/alr.22602 - DOI - PMC - PubMed
    1. Chen, J. et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci. Rep.10, 19196. 10.1038/s41598-020-76282-0 (2020). 10.1038/s41598-020-76282-0 - DOI - PMC - PubMed
    1. Albataineh, Z., Aldrweesh, F. & Alzubaidi, M. A. COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm. Cluster Comput.10.1007/s10586-023-03972-5 (2023). 10.1007/s10586-023-03972-5 - DOI - PMC - PubMed
    1. Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ369, m1328. 10.1136/bmj.m1328 (2020). 10.1136/bmj.m1328 - DOI - PMC - PubMed

LinkOut - more resources