Pre-pregnancy sleep duration and postpartum depression: a multicenter study in Japan

Risk Factors of Postpartum Depression Among Korean Women: An Analysis Based on the Korean Pregnancy Outcome Study (KPOS)

BACKGROUND

Postpartum depression (PPD) can negatively affect infant well-being and child development. Although the frequency and risk factors of PPD symptoms might vary depending on the country and culture, there is limited research on these risk factors among Korean women. This study aimed to elucidate the potential risk factors of PPD throughout pregnancy to help improve PPD screening and prevention in Korean women.

METHODS

The pregnant women at 12 gestational weeks (GW) were enrolled from two obstetric specialized hospitals from March 2013 to November 2017. A questionnaire survey was administered at 12 GW, 24 GW, 36 GW, and 4 weeks postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale, and PPD was defined as a score of ≥ 10.

RESULTS

PPD was prevalent in 16.3% (410/2,512) of the participants. Depressive feeling at 12 GW and postpartum factors of stress, relationship with children, depressive feeling, fear, sadness, and neonatal intensive care unit admission of baby were significantly associated with a higher risk of PPD. Meanwhile, high postpartum quality of life and marital satisfaction at postpartum period were significantly associated with a lower risk of PPD. We developed a model for predicting PPD using factors as mentioned above and it had an area under the curve of 0.871.

CONCLUSION

Depressive feeling at 12 GW and postpartum stress, fear, sadness, relationship with children, low quality of life, and low marital satisfaction increased the risk of PPD. A risk model that comprises significant factors can effectively predict PPD and can be helpful for its prevention and appropriate treatment.

Machine learning prediction models for postpartum depression: A multicenter study in Japan

AIM

Postpartum depression (PPD) and perinatal mental health care are of growing importance worldwide. Here we aimed to develop and validate machine learning models for the prediction of PPD, and to evaluate the usefulness of the recently adopted 2-week postpartum checkup in some parts of Japan for the identification of women at high risk of PPD.

METHODS

A multicenter retrospective study was conducted using the clinical data of 10 013 women who delivered at ≥35 weeks of gestation at 12 maternity care hospitals in Japan. PPD was defined as an Edinburgh Postnatal Depression Scale score of ≥9 points at 4 weeks postpartum. We developed prediction models using conventional logistic regression and four machine learning algorithms based on the information that can be routinely collected in daily clinical practice. The model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In the machine learning models developed using clinical data before discharge, the AUROCs were similar to those in the conventional logistic regression models (AUROC, 0.569-0.630 vs. 0.626). The incorporation of additional 2-week postpartum checkup data into the model significantly improved the predictive performance for PPD compared to that without in the Ridge regression and Elastic net (AUROC, 0.702 vs. 0.630 [p < 0.01] and 0.701 vs. 0.628 [p < 0.01], respectively).

CONCLUSIONS

Our machine learning models did not achieve better predictive performance for PPD than conventional logistic regression models. However, we demonstrated the usefulness of the 2-week postpartum checkup for the identification of women at high risk of PPD.