Development and validation of a nomogram to predict hypertension in patients with Sleep-disordered breathing: A Chinese Community-Based Study

A predictive model for SDB-related hypertension based on Chinese Community is not available. A total of 1081 patients diagnosed with sleep-disordered breathing (SDB) were included in this study, all of whom were confirmed to have SDB through the use of a reliable type IV wearable sleep-monitoring device. The variables included in the model were determined using the least absolute shrinkage and selection operator (LASSO) regression technique. Binary logistic results were used to develop a nomogram. The LASSO method detected six variables that were included in the final prediction model: waist circumference, age, oxygen desaturation index (ODI), diabetes, family history of hypertension, and apnea. The area under the curve (AUC) for this prediction model was 0.763 (95% CI: 0.728–0.798). Both the internal and external calibration curves for hypertension in individuals at risk of SDB showed that the predictive nomogram model was highly accurate. The research conducted in this study has yielded a prediction model that exhibits impressive accuracy in predicting hypertension associated with SDB. <fig><object-id>erj;64/suppl_68/PA5339/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> <fig><object-id>erj;64/suppl_68/PA5339/F2</object-id><object-id>F2</object-id><object-id>F2</object-id><graphic></graphic></fig>