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Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule
Ming Tang1, Xia-Xia Yu2, Jia Huang1
1Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Insights
This study identifies key predictors for severe COVID-19, aiding early intensive care unit (ICU) admission decisions. The developed model accurately predicts critical illness risk in COVID-19 patients.
Area of Science:
- Infectious Diseases
- Critical Care Medicine
- Epidemiology
Background:
- The COVID-19 pandemic caused significant morbidity and mortality.
- Early identification of critically ill patients is essential for effective management.
Purpose of the Study:
- To create predictive rules for identifying COVID-19 patients needing intensive care unit (ICU) admission upon hospital entry.
- To develop a tool for early risk stratification of COVID-19 patients.
Main Methods:
- Retrospective analysis of 361 COVID-19 patients using reverse transcription-polymerase chain reaction.
- Multivariate logistic regression to build a predictive model, validated on an external dataset of 126 patients.
- Performance evaluation using Area Under the Receiver Operating Curve (AUROC), goodness-of-fit, and sensitivity/specificity analysis. A nomogram was utilized for visualization.
Main Results:
- Six independent predictors for severe COVID-19 were identified: BMI, delayed admission (>5 days), fever, Charlson index, low PaO2/FiO2 ratio, and high neutrophil/lymphocyte ratio.
- The predictive model demonstrated high accuracy with AUROC values of 0.941 and 0.936 in the derivation and validation datasets, respectively.
- The model showed good calibration and significant correlations between identified factors and severe COVID-19 outcomes.
Conclusions:
- The developed predictive model shows significant potential for accurately assessing COVID-19 severity.
- This tool can assist intensive care unit (ICU) clinicians in making timely and informed decisions for patient management.
Background:
The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.
Aim:
To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.
Methods:
This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model.
Results:
Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good.
Conclusion:
The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.
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