Abstract
OBJECTIVE: Hypertension increases the prevalence of depression to a certain extent and identification and diagnosis of depression frequently pose challenges for clinicians. The study aimed to construct and validate a scoring model predicting the prevalence of depression with hypertension. METHODS: 6124 individuals with hypertension were utilized from the 2007 to 2020 National Health and Nutrition Examination Survey database (NHANES), including 645 subjects that were assessed to have depressive symptoms, 390 in the development group and 255 in the validation group. Univariable and multivariable analyses were applied to analyze the impact of each parameter on depression with hypertension, resulting in establishment of a predictive model. Finally, the discriminability, calibration ability, and clinical efficacy of the model were verified for both the derivation set and validation set. RESULTS: Ten variables comprised this model: age, gender, race, poverty to income ratio (PIR), smoke, sleep hours, exercise, diabetes, congestive heart failure, stroke. The area under the receiver operating characteristic curve for the derivation and validating sets was 0.790 and 0.723, respectively, which showed excellent discriminability. The model also fitted well with the actual prevalence of depression with hypertension in calibration and decision curve analysis (DCA) demonstrated that the depression model was practically useful. CONCLUSION: This scoring model may provide an additional perspective for evaluating the underlying risk factors of depression for hypertensive individuals.