Development and validation of a predictive nomogram for antenatal depression in China

Area of Science:

• Reproductive Health
• Mental Health
• Public Health

Background:

• Antenatal depression poses significant risks to maternal and infant well-being.
• A lack of tailored predictive tools for antenatal depression exists in China.
• This study addresses the need for a specific predictive model for the Chinese population.

Purpose of the Study:

• To develop and validate a predictive nomogram for antenatal depression.
• To identify key predictors of antenatal depression in Chinese pregnant women.
• To provide a tool for early risk assessment and intervention.

Main Methods:

• Cross-sectional study of 3694 pregnant women in Suzhou, China (March 2017-December 2019).
• Data collection included demographics, clinical characteristics, Edinburgh Postnatal Depression Scale, and psychological assessments.
• LASSO and logistic regression analyses were used for model development and internal validation.

Main Results:

• 12.8% of participants (473/3694) screened positive for antenatal depression.
• Neuroticism, negative coping strategies, and anxiety were identified as significant predictors.
• The nomogram achieved an AUC of 0.90 in the validation set, showing good calibration and clinical utility.

Conclusions:

• The developed nomogram is a valuable tool for early detection of antenatal depression in Chinese pregnant women.
• The model supports personalized care and timely intervention.
• Future research should include longitudinal studies and diverse population validation for enhanced robustness.