Abstract
BACKGROUND: Currently, the factors impacting postpartum post-traumatic stress disorder (PP-PTSD) remain unclear. Therefore, this study aimed to screen the PP-PTSD risk factors and to develop an effective and user-friendly column chart prediction model (nomogram), thereby providing a basis for early clinical diagnosis and prompt intervention. METHODS: This retrospective study collected 180 postpartum women between January 2023 and December 2023. Based on the occurrence of PP-PTSD, study participants were divided into two groups: a control group (No-PP-PTSD) and an observation group (PP-PTSD). The logistic regression analysis were used to identify independent risk factors for this condition, and nomogram models were developed by incorporating these items. Furthermore, we applied the calibration plots, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve to assess the predictive performance of the nomogram. RESULTS: Multivariable logistic regression analysis identified working condition (p = 0.008), relationship with the second primary caregiver of the child (p < 0.001), type of pregnancy (p < 0.001), pregnancy mode (p < 0.001), newborns sent to the ICU (p < 0.001), postpartum anxiety (p = 0.002), and plan pregnancy (p = 0.001) as independent risk factors for PP-PTSD. CONCLUSIONS: We developed a user-friendly and scientifically robust nomogram model for predicting PP-PTSD risk in postpartum women. This predicting tool has the potential to assist clinicians in making informed decisions concerning PP-PTSD among postpartum women.