Development, validation, and visualization of a novel depression screening tool for hypertensive patients based on...

Hypertension and depression have a well-recognized bidirectional association, with comorbidity reducing antihypertensive medication adherence and elevating adverse health outcomes; depression affects 30% to 40% of hypertensive patients, far higher than the general population. However, validated, hypertension-specific depression screening tools based on machine learning (ML) are currently lacking. This study aimed to develop, validate, and visualize an ML-based depression screening tool for adult hypertensive patients. A cross-sectional study was conducted using 2005-2020 National Health and Nutrition Examination Survey data of 6687 hypertensive patients aged ≥20 years (depression defined by Patient Health Questionnaire-9 score ≥ 10). Least absolute shrinkage and selection operator regression was used for feature selection, data were split into a 7:3 training/test set, and 7 ML models were constructed. Model performance was assessed by area under the receiver operating characteristic curve, calibration, and clinical utility (decision curve analysis); SHapley Additive exPlanations values ensured interpretability, and a web-based tool was built from the optimal model. The random forest model was identified as the optimal screening tool (area under the receiver operating characteristic curve = 0.761), with good calibration and favorable net clinical benefit (threshold probability: 0.07-0.39). Twelve key predictors were established, with sleep disturbances as the top factor, followed by poverty-to-income ratio and age. An interactive online screening tool (https://chunyuzhang28.shinyapps.io/Hypertension/) was developed, enabling rapid depression risk prediction for hypertensive patients via input of the 12 predictors. This random forest-based ML screening tool is a valid and practical tool for clinical depression risk assessment in adult hypertensive patients. Clinicians can use it to stratify depression risk in hypertensive patients, identify high-risk subgroups (e.g., those with sleep disturbances or low poverty-to-income ratio), and implement targeted early intervention and integrated management strategies for hypertension and depression, thus optimizing clinical decision-making for comorbidity care.