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

Publication date: Aug 06, 2024

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.

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Concepts Keywords
China Adult
Coronavirus Aged
Healthcare China
Severe Clinical characteristics
COVID-19
COVID-19
Female
Humans
Male
Middle Aged
Neutrophil-to-lymphocyte ratio
Nomogram
Nomograms
Predictive models
Retrospective Studies
Risk Factors
Risk factors
ROC Curve
SARS-CoV-2

Semantics

Type Source Name
disease MESH COVID-19
disease MESH death
disease MESH infections
disease VO laboratory test
drug DRUGBANK Saquinavir
disease MESH pulmonary inflammation
drug DRUGBANK Dichloroacetic Acid
drug DRUGBANK Tropicamide
drug DRUGBANK Coenzyme M
disease IDO blood
disease MESH inflammation
disease VO erythrocyte
drug DRUGBANK L-Alanine
drug DRUGBANK Urea
drug DRUGBANK Nitrogen
drug DRUGBANK Creatinine
drug DRUGBANK Cholesterol
drug DRUGBANK Prothrombin
drug DRUGBANK Thrombin
drug DRUGBANK Fibrinogen Human
drug DRUGBANK Creatine
disease MESH viral pneumonia
disease MESH influenza
disease VO Viruses
disease MESH sore throat
disease IDO infection
disease VO effective
disease MESH clinical significance
disease MESH aids
drug DRUGBANK Methionine
disease VO time
disease MESH coronavirus infection
disease MESH acute pancreatitis
disease MESH acute cholecystitis
disease MESH liver abscess
disease IDO history
drug DRUGBANK Ethanol
disease MESH hypertension
disease MESH heart diseases
disease MESH kidney disease
disease MESH cancer
disease VO stomach
disease VO manufacturer
disease VO USA
disease IDO process
disease VO effectiveness
drug DRUGBANK Oxygen
disease IDO symptom
disease VO age
disease MESH Unconsciousness
disease VO organ
disease VO efficiency
disease IDO intervention
drug DRUGBANK Polyethylene glycol
drug DRUGBANK Pentaerythritol tetranitrate
disease MESH abnormalities
disease MESH comorbidity
disease IDO production
disease VO population
disease MESH Severe acute respiratory syndrome
disease MESH Allergy
disease IDO algorithm
disease MESH idiopathic pulmonary fibrosis
disease IDO cell
drug DRUGBANK Nivolumab
disease MESH psoriasis
drug DRUGBANK Troleandomycin
disease MESH critical illness
disease VO protocol
disease MESH Leukemia
disease MESH complications
disease MESH Cardiovascular disease
disease MESH autoimmune diseases
disease MESH Shock
disease MESH respiratory syncytial virus infection
disease VO publication

Original Article

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