Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community

Purpose Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community.Patients and methodsThe 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.ResultsThe 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.ConclusionThis 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.

Automated summary

This study developed and validated a prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community. The model showed good discrimination and calibration in the verification group and demonstrated clinical usefulness.