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Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China
Xin Chen1, Feng Peng1, Xiaoni Zhou2
1Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Insights
This study identified five key factors predicting severe coronavirus disease 2019 (COVID-19). A nomogram model using these factors accurately predicts severe COVID-19 risk.
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Medicine
Background:
- Coronavirus disease 2019 (COVID-19) poses a significant global health threat.
- Identifying risk factors for severe or critical COVID-19 is crucial for timely intervention.
- Predictive models can aid in stratifying patient risk and optimizing resource allocation.
Purpose of the Study:
- To identify potential risk factors associated with severe or critical COVID-19.
- To develop and validate a prediction model for severe COVID-19 based on identified risk factors.
Main Methods:
- A cohort of 370 COVID-19 patients was analyzed.
- Propensity score matching and statistical adjustments were employed to identify significant factors.
- A nomogram model was constructed using five independent risk factors.
Main Results:
- Five factors were significantly associated with severe or critical COVID-19: diagnostic delay, albumin, lactate dehydrogenase, white blood cell count, and neutrophil count.
- The nomogram model demonstrated good prediction capability with a C-index of 90.6%.
Conclusions:
- Diagnostic delay, albumin levels, lactate dehydrogenase, white blood cell count, and neutrophil count are significant independent predictors of severe COVID-19.
- The developed nomogram model offers a valuable tool for predicting severe COVID-19 risk.
Objectives:
We aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors.
Methods:
A total of 370 patients with COVID-19 were consecutively enrolled at The Third People's Hospital of Yichang from January to March 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI).
Results:
326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (p <0.05) relative to mild or ordinary COVID-19. Due to strong pairwise correlations, only five factors, including diagnostic delay (OR, 95% CI, p: 1.08, 1.02 to 1.17, 0.048), albumin (0.82, 0.75 to 0.91, <0.001), lactate dehydrogenase (1.56, 1.14 to 2.13, 0.011), white blood cell (1.27, 1.08 to 1.50, 0.004), and neutrophil (1.40, 1.16 to 1.70, <0.001), were retained for model construction and performance assessment. The nomogram model based on the five factors had good prediction capability and accuracy (C-index: 90.6%).
Conclusions:
Our findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.
