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Updated: Jul 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic
Yan Wu1, Wei Tan2, Yifeng Liu2
1Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430081, China. jzg100@126.com.
Purpose:
Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community.
Patients And Methods:
The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model.
Results:
The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471-0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful.
Conclusion:
This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients.
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