ArticleBMC psychiatry2025
Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas.Frontiers in psychiatry · 2026Article
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7 authors.
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Abstract
backgroundWhile traditional logistic regression emphasizes main effects with limited capacity for interaction detection, emerging decision trees excel in uncovering complex associations. However, no studies have yet integrated both approaches to investigate postpartum posttraumatic stress disorder (PP-PTSD). This study aims to explore the factors associated with postpartum posttraumatic stress disorder (PP-PTSD) in Chinese women using decision tree and logistic regression models, while also comparing the predictive performance of both approaches.
methodsThis cross-sectional study recruited postpartum women using convenience sampling between June 2021 and December 2022. PTSD was assessed using the City Birth Trauma Scale (City BiTS). The Perceived Social Support Scale (PSSS), Simplified Coping Style Questionnaire (SCSQ), Pregnancy Stress Rating Scale (PSRS), and Connor-Davidson Resilience Scale (CD-RISC) were employed to evaluate perceived social support, psychological coping strategies, pregnancy stress and resilience, respectively. Decision tree and logistic regression models were applied to identify factors associated with PTSD.
resultsAmong 704 valid participants, 36 (5.11%) screened positive for PP-PTSD. Logistic regression identified postpartum duration, sleep quality, pregnancy stress, family support, and positive coping as significant predictors of PP-PTSD (p < 0.05). The decision tree model highlighted postpartum sleep quality as the primary determinant, followed by pregnancy stress and postpartum duration. While both models achieved perfect sensitivity (100%), logistic regression demonstrated superior overall performance, with a 2.28% higher classification accuracy (97.73% vs. 95.45%) and enhanced specificity (97.9% vs. 88.9%). The AUC values further validated this advantage (0.992 vs. 0.968).
conclusionsThis study utilized Logistic Regression and Decision Tree models to identify key factors influencing PP-PTSD, which include postpartum duration, sleep quality, pregnancy stress, family support, and positive coping. The identified modifiable factors enable targeted PP-PTSD prevention, with Logistic Regression providing high-accuracy screening tools and Decision Trees simplifying risk assessment in community settings.
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