Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women
Abstract Background While 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...
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| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-07261-w |
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| author | Xiao Fei Nie Lan Lan Xu Wen Ping Guo Jin Hui Li Li Cheng Tao Tao Zhang Jun-Yan Li |
| author_facet | Xiao Fei Nie Lan Lan Xu Wen Ping Guo Jin Hui Li Li Cheng Tao Tao Zhang Jun-Yan Li |
| author_sort | Xiao Fei Nie |
| collection | DOAJ |
| description | Abstract Background While 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. Methods This 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. Results Among 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). Conclusions This 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. |
| format | Article |
| id | doaj-art-440257e9ae9e4b7495f80f707b697fd9 |
| institution | Kabale University |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-440257e9ae9e4b7495f80f707b697fd92025-08-24T11:42:48ZengBMCBMC Psychiatry1471-244X2025-08-0125111210.1186/s12888-025-07261-wUnveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese womenXiao Fei Nie0Lan Lan Xu1Wen Ping Guo2Jin Hui Li3Li Cheng4Tao Tao Zhang5Jun-Yan Li6School of nursing, Hubei University of MedicineSchool of nursing, Hubei University of MedicineShiyan RenMin Hospital (Affiliated Hospital of Hubei University of Medicine)School of nursing, Hubei University of MedicineSchool of nursing, Hubei University of MedicineSchool of nursing, Hubei University of MedicineSchool of nursing, The University of Hong KongAbstract Background While 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. Methods This 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. Results Among 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). Conclusions This 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.https://doi.org/10.1186/s12888-025-07261-wLogistic regressionDecision tree modelsPTSDRisk factors |
| spellingShingle | Xiao Fei Nie Lan Lan Xu Wen Ping Guo Jin Hui Li Li Cheng Tao Tao Zhang Jun-Yan Li Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women BMC Psychiatry Logistic regression Decision tree models PTSD Risk factors |
| title | Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women |
| title_full | Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women |
| title_fullStr | Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women |
| title_full_unstemmed | Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women |
| title_short | Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women |
| title_sort | unveiling postpartum ptsd predicting risk factors using decision trees and logistic regression in chinese women |
| topic | Logistic regression Decision tree models PTSD Risk factors |
| url | https://doi.org/10.1186/s12888-025-07261-w |
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