1. Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community.
- Author
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Wu, Yan, Tan, Wei, Liu, Yifeng, Li, Yongli, Zou, Jiali, Zhang, Jinsong, and Huang, Wenjuan
- Subjects
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NOMOGRAPHY (Mathematics) , *DISEASE management , *PREDICTION models , *CHRONIC diseases , *COMMUNITY health services , *LOGISTIC regression analysis - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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