Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas - Summary - MDSpire
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Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas
To analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM) and provide a robust evidence base for targeted preventive interventions.
Approach:
Data Collection: Assessment of PP-PTSD symptoms at six weeks postpartum using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with multidimensional variables collected.
Key Findings:
Incidence of PP-PTSD was 25.18% in the training cohort.
Key predictors included social support, depression, neonatal caregiving style, husband’s participation, and sleep quality.
Gradient Boosting model showed the best performance with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595.
Interpretation:
The study elucidated the multidimensional mechanisms underlying PP-PTSD, highlighting the importance of psychological and social factors.
Limitations:
Need for multicentre validation and the development of clinically implementable tools.
Conclusion:
The Gradient Boosting prediction model demonstrated robust performance, providing a foundation for early identification and intervention for PP-PTSD in primiparous women.
