Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community
2023
Yan Wu | Wei Tan | Yifeng Liu | Yongli Li | Jiali Zou | Jinsong Zhang | Wenjuan Huang
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.
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