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

BACKGROUND: Antenatal depression is a significant public health concern, with detrimental effects on maternal and infant health. However, there is currently a lack of comprehensive predictive tools specifically tailored for antenatal depression in China. The present study aimed to develop and validate a predictive nomogram for antenatal depression in the Chinese population. METHODS: A cross-sectional study was conducted in multiple healthcare settings in Suzhou, Jiangsu Province, China, spanning from March 2017 to December 2019. A total of 3694 pregnant women were included in the study. Demographic and clinical characteristics were collected using structured questionnaires. The Edinburgh Postnatal Depression Scale and various psychological assessments were used to assess antenatal depression and associated factors. LASSO regression and logistic regression analyses were employed to develop the predictive model, and internal validation was performed to assess its performance. RESULTS: Among the 3694 participants, 473 (12.8%) pregnant women were positive for antenatal depression. The developed predictive Model incorporated neuroticism, negative coping strategies, and anxiety as significant predictors. The nomogram demonstrated accurate risk assessment capabilities, with an AUC of 0.90 in the validation set. The model exhibited good calibration and clinical utility. LIMITATIONS: Limitations of this paper include the cross-sectional design, biases like self-reporting and non-randomized sampling, highlighting the need for future longitudinal studies and diverse population validation to enhance the model's robustness in varied sociocultural contexts. CONCLUSION: The predictive model for antenatal depression in the Chinese population provides a valuable tool for early detection, intervention, and personalized care for pregnant women.