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TL;DR
The final model, incorporating eleven predictors like age, sex, and poverty-to-income ratio, showed good predictive performance with an AUC of 0.757 in the training set and 0.724 in the validation set.
PLOS ONE · 8 authors, 4 centres
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The final model, incorporating eleven predictors like age, sex, and poverty-to-income ratio, showed good predictive performance with an AUC of 0.757 in the training set and 0.724 in the validation set.
Using logistic regression on the training set, eleven independent predictors were identified: age, sex, race, marital status, education level, sleep time on workdays, poverty-to-income ratio, smoking, alcohol consumption, sedentary time, and heart failure status. A nomogram was constructed and internally validated. Calibration curves showed good agreement. The findings suggest the nomogram could be a clinically useful tool to identify hypertensive patients at higher risk of depression.