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A nomogram for predicting postpartum post-traumatic stress disorder: a prospective cohort study.
Xiaoting Wang1, Jiaxin Ren1, Linping Kang2
1School of Nursing, Jinzhou Medical University, No.40, Section 3, Songpo Road, Linghe District, Jinzhou, Liaoning Province, China.
This study identified key risk factors for postpartum post-traumatic stress disorder (PTSD), developing a nomogram tool to help clinicians screen high-risk pregnant women for early intervention.
Area of Science:
- Obstetrics and Gynecology
- Psychiatry
- Public Health
Background:
- Postpartum post-traumatic stress disorder (PTSD) is a significant psychological stress disorder with long-term consequences.
- Postpartum PTSD receives less attention in China compared to other postpartum psychiatric disorders.
- Early identification and intervention are crucial for managing postpartum PTSD.
Purpose of the Study:
- To investigate the risk factors associated with postpartum PTSD.
- To develop a convenient and rapid nomogram screening tool for identifying high-risk pregnant women.
- To aid clinical staff in timely preventative and management measures for postpartum PTSD.
Main Methods:
- A questionnaire survey was conducted with 602 pregnant women hospitalized for delivery.
- Logistic regression analysis identified independent risk factors and protective factors for postpartum PTSD.
- A nomogram prediction model was developed and validated internally and externally using statistical methods.
Main Results:
- The incidence of postpartum PTSD was 11.1%.
- Independent risk factors included poor sleep self-assessment, cesarean section, instrumental delivery, and fear of labor.
- A high Quality of Relationship Index score was a protective factor.
- The nomogram model demonstrated high accuracy (AUC 0.928 in training, 0.907 in validation) and clinical utility.
Conclusions:
- The developed nomogram model effectively predicts postpartum PTSD risk.
- Early identification of high-risk pregnant women is possible through this screening tool.
- Timely preventive interventions can be implemented based on the nomogram's predictions.
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