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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation a nomogram for predicting the risk of severe COVID-19: A multi-center study in Sichuan,
Yiwu Zhou1,2, Yanqi He3, Huan Yang3
1Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Insights
This study developed a nomogram to predict severe coronavirus disease 2019 (COVID-19) risk using clinical data. The model effectively identifies patients at high risk for severe outcomes.
Area of Science:
- Infectious Diseases
- Epidemiology
- Medical Informatics
Background:
- Coronavirus disease 2019 (COVID-19) rapidly spread globally since December 2019.
- Accurate risk stratification for severe COVID-19 is crucial for patient management.
Purpose of the Study:
- To develop and validate a practical nomogram for predicting the risk of severe COVID-19.
- To identify key clinical predictors for severe disease progression.
Main Methods:
- A cohort of 366 COVID-19 patients was analyzed.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were used.
- Internal validation was performed using bootstrapping.
Main Results:
- The nomogram incorporated seven predictors: body temperature, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease.
- The model demonstrated good discrimination (C-index: 0.863) and calibration.
- Internal validation yielded a C-index of 0.839, indicating clinical usefulness.
Conclusions:
- An early warning model for severe COVID-19 was established using readily available admission clinical characteristics.
- This nomogram aids in predicting severe COVID-19 and identifying at-risk patients.
Background:
Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19.
Methods:
A cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping.
Results:
The median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801-0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful.
Conclusion:
We established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.
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