Prediction of peripartum depression by oxytocin levels, Edinburgh Postnatal Depression Scale scores and lack of social support: a prospective study.
Saved in:
| Title: | Prediction of peripartum depression by oxytocin levels, Edinburgh Postnatal Depression Scale scores and lack of social support: a prospective study. |
|---|---|
| Authors: | Lu, Yanyan (AUTHOR), Qi, Xiaoxuan (AUTHOR), Cheng, Qing (AUTHOR), Sun, Jing (AUTHOR) |
| Source: | Journal of Psychosomatic Obstetrics & Gynecology. Dec2025, Vol. 46 Issue 1, p1-9. 9p. |
| Subjects: | Oxytocin, Edinburgh Postnatal Depression Scale, Nomography (Mathematics), Predictive validity, Prognosis, Postpartum depression, Logistic regression analysis, Social support |
| Abstract: | Purpose: This study aimed to determine predictive factors for peripartum depression (PPD). Methods: We collected pre-natal sociodemographic data, blood markers and psychosocial questionnaires from 237 mothers. We detected the oxytocin (OXT) concentrations in saliva and administered the Edinburgh Postnatal Depression Scale (EPDS) within 1–2 days postpartum. The multivariable logistic regression analysis model was applied to analyze the factors related to PPD at 4 weeks postpartum. A nomogram model was constructed and evaluated. Results: Multivariable logistic regression showed that OXT levels (OR = 0.984, 95% CI 0.971–0.997), EPDS scores (OR = 1.284, 95% CI 1.154–1.428), and lack of social support (OR = 3.562, 95% CI 1.444–8.784) were predictive factors for PPD (all p < 0.05). A nomogram model was constructed based on these factors. The model's C-index was 0.846 (95% CI 0.777–0.908). The calibration curve indicated good agreement between the predicted probabilities of PPD and the actual probabilities (Hosmer-Lemeshow test p = 0.928). The area under the Receiver Operating Characteristic (ROC) curve was 0.820 (95% CI 0.749–0.891). The results of the decision curve analysis showed that the model had good clinical utility. Conclusions: The predictive model can be used to identify high-risk parturients of PPD early. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Psychosomatic Obstetrics & Gynecology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Purpose: This study aimed to determine predictive factors for peripartum depression (PPD). Methods: We collected pre-natal sociodemographic data, blood markers and psychosocial questionnaires from 237 mothers. We detected the oxytocin (OXT) concentrations in saliva and administered the Edinburgh Postnatal Depression Scale (EPDS) within 1–2 days postpartum. The multivariable logistic regression analysis model was applied to analyze the factors related to PPD at 4 weeks postpartum. A nomogram model was constructed and evaluated. Results: Multivariable logistic regression showed that OXT levels (OR = 0.984, 95% CI 0.971–0.997), EPDS scores (OR = 1.284, 95% CI 1.154–1.428), and lack of social support (OR = 3.562, 95% CI 1.444–8.784) were predictive factors for PPD (all p < 0.05). A nomogram model was constructed based on these factors. The model's C-index was 0.846 (95% CI 0.777–0.908). The calibration curve indicated good agreement between the predicted probabilities of PPD and the actual probabilities (Hosmer-Lemeshow test p = 0.928). The area under the Receiver Operating Characteristic (ROC) curve was 0.820 (95% CI 0.749–0.891). The results of the decision curve analysis showed that the model had good clinical utility. Conclusions: The predictive model can be used to identify high-risk parturients of PPD early. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 0167482X |
| DOI: | 10.1080/0167482X.2025.2519381 |
