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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and Validation of a Nomogram for Predicting the Disease Progression of Nonsevere Coronavirus Disease 2019
Xue-Lian Li1, Cen Wu2, Jun-Gang Xie3
1Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning Province, China.
Insights
A new nomogram can predict coronavirus disease 2019 (COVID-19) progression in nonsevere patients using simple data. This tool aids early identification of high-risk COVID-19 cases for timely treatment.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Public Health
Background:
- Most coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality.
- Early detection and treatment are crucial for severe COVID-19 outcomes.
- Predicting progression in nonsevere COVID-19 is vital for resource allocation and patient management.
Purpose of the Study:
- To develop a predictive nomogram for COVID-19 disease progression.
- To utilize easily obtainable data from primary medical institutions for prediction.
- To identify nonsevere COVID-19 patients at high risk of progressing to severe disease.
Main Methods:
- Retrospective, multicenter cohort study of 495 COVID-19 patients.
- Patients randomized into development (2:1) and validation cohorts.
- Nomogram developed using initial medical evaluation data; performance tested on validation cohort.
Main Results:
- A nine-factor nomogram was developed to predict COVID-19 progression.
- The nomogram demonstrated strong predictive performance with AUCs of 0.875 (development) and 0.821 (validation).
- Good concordance index and well-fitted calibration curves confirmed nomogram reliability.
Conclusions:
- The simplified nomogram can predict nonsevere COVID-19 progression.
- Early identification of high-risk COVID-19 cases is facilitated.
- Enables timely therapeutic choices based on predicted disease severity.
Background And Objectives:
The majority of coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality and need early detection and treatment. We aimed to develop a nomogram to predict the disease progression of nonsevere COVID-19 based on simple data that can be easily obtained even in primary medical institutions.
Methods:
In this retrospective, multicenter cohort study, we extracted data from initial simple medical evaluations of 495 COVID-19 patients randomized (2:1) into a development cohort and a validation cohort. The progression of nonsevere COVID-19 was recorded as the primary outcome. We built a nomogram with the development cohort and tested its performance in the validation cohort.
Results:
The nomogram was developed with the nine factors included in the final model. The area under the curve (AUC) of the nomogram scoring system for predicting the progression of nonsevere COVID-19 into severe COVID-19 was 0.875 and 0.821 in the development cohort and validation cohort, respectively. The nomogram achieved a good concordance index for predicting the progression of nonsevere COVID-19 cases in the development and validation cohorts (concordance index of 0.875 in the development cohort and 0.821 in the validation cohort) and had well-fitted calibration curves showing good agreement between the estimates and the actual endpoint events.
Conclusions:
The proposed nomogram built with a simplified index might help to predict the progression of nonsevere COVID-19; thus, COVID-19 with a high risk of disease progression could be identified in time, allowing an appropriate therapeutic choice according to the potential disease severity.
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