Risk factors and predictive model for prenatal depression: A large retrospective study in China.

Background: Prenatal depression, associated with adverse effects on mothers and fetuses, has received little attention. We conducted a large-sample study to investigate the risk factors of, and develop a predictive model for, prenatal depression in the Chinese population.

Methods: This study enrolled 14,329 pregnant women who delivered at the West China Second University Hospital, Sichuan University from January 2017 to December 2020. Participants were divided into a training or validation cohort. Multiple variables were collected and selected using univariate logistic regression and least absolute shrinkage and selection operator penalty regression. After multivariate logistic analysis, a predictive model was developed and validated internally and externally.

Results: Nine variables (employment, planned pregnancy, pregnancy number, conception methods, gestational diabetes mellitus, twin pregnancy, placenta previa, umbilical cord encirclement, and educational attainment) were identified as independent risk factors for prenatal depression. Receiver operating characteristic curves in both the training and validation cohorts showed excellent discrimination of the predictive model (the area under the curve: 0.746 and 0.732, respectively).

Limitations: The results of this retrospective study may be affected by confounding and information bias. Some important variables were excluded, such as family history of mental disorders. The study was conducted in China; its results may not be generalizable to other regions.

Conclusion: Our study identified nine significant risk factors for prenatal depression and constructed an accurate predictive model. This model could be applied as a clinical decision aid for individualized risk estimates and prevention of prenatal depression.