Development and Validation of a Nomogram to Predict Depression Risk in Patients with Cardiovascular Disease

Area of Science:

• Cardiology
• Psychiatry
• Public Health

Background:

• Cardiovascular disease (CVD) affects millions, with a significant portion also experiencing depression.
• Early identification of depression in CVD patients is crucial for effective management and improved outcomes.

Purpose of the Study:

• To develop and validate a predictive nomogram for assessing depression risk in individuals with cardiovascular disease.
• To identify key clinical and demographic factors associated with depression in the CVD population.

Main Methods:

• A cross-sectional study utilizing data from 6702 patients with CVD from the National Health and Nutrition Examination Survey (2007-2018).
• Data was split into training (75%) and validation (25%) cohorts.
• Logistic regression analysis was employed to identify risk factors and build a web-based dynamic nomogram, subsequently validated.

Main Results:

• Eleven risk factors were identified and incorporated into the nomogram: blood cadmium concentration, sedentary time, eosinophil count, marital status, work limitations, sleep disorders, asthma, gastrointestinal illness, cognitive issues, ethnicity, and cotinine.
• The nomogram demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.852 in the training cohort and 0.856 in the validation cohort.
• High sensitivity and specificity were observed in both cohorts, indicating robust clinical utility.

Conclusions:

• The developed nomogram shows significant potential for the early screening of depression risk among patients with cardiovascular disease.
• This tool can aid clinicians in identifying at-risk individuals for timely intervention, potentially improving patient prognosis.