Assessing the depression risk in the U.S. adults using nomogram.

Area of Science:

• Medical research
• Public health
• Psychiatry

Background:

• Depression is a prevalent and serious mental health condition.
• Awareness of individual depression risk is often low.
• Predictive models are needed to assess depression risk in the general population.

Purpose of the Study:

• To develop and validate predictive models for depression risk in US adults.
• To provide tools for early identification and intervention of depression.
• To enhance personalized treatment decision-making for depression.

Main Methods:

• Utilized National Health and Nutrition Examination Survey (NHANES) data (2007-2012).
• Employed logistic regression to identify risk factors and construct nomograms.
• Validated models using internal and external cohorts and statistical measures like C-index and AUC.

Main Results:

• Two depression risk nomogram models were developed, incorporating various demographic and lifestyle factors.
• Both models demonstrated good discrimination and calibration, with high C-index and AUC values.
• Decision curve analysis confirmed the practical utility of the developed prediction models.

Conclusions:

• The study successfully created accurate and effective depression risk prediction models.
• These models can assist the US non-institutionalized population in making informed treatment decisions.
• Personalized risk prediction for depression is achievable through these validated tools.