# Assessing Factors Affecting Postpartum Post-Traumatic Stress Disorder and Development of Risk Prediction Nomogram Model

## TL;DR

This study identifies risk factors for postpartum post-traumatic stress disorder and creates a prediction model to help clinicians diagnose and intervene early.

## Contribution

The study introduces a new nomogram model for predicting PP-PTSD risk based on identified independent risk factors.

## Key findings

- Working condition, relationship with the second caregiver, and pregnancy type are significant risk factors for PP-PTSD.
- The developed nomogram model shows strong predictive performance for PP-PTSD.
- The model can aid in early clinical diagnosis and intervention for postpartum women at risk.

## Abstract

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.

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.

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.

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.