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Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis
Shaoying Zhang1,2, Liping Kang3, Shilai Yang1
1Nursing School, Quanzhou Medical College, Quanzhou, Fujian, 362011, China.
BMC Psychiatry
|May 30, 2026
Summary
Prediction models for postpartum post-traumatic stress disorder (PP-PTSD) show promise but have significant quality issues. Future research must improve study design and external validation for reliable clinical use.
Area of Science:
- Reproductive Health
- Mental Health Research
- Clinical Prediction Models
Background:
- Numerous studies aim to predict postpartum post-traumatic stress disorder (PP-PTSD) risk.
- The quality and clinical utility of existing PP-PTSD prediction models remain unclear.
Purpose of the Study:
- To systematically review and critically appraise prediction models for PP-PTSD.
- To assess the methodological quality and applicability of current PP-PTSD prediction models.
Main Methods:
- Comprehensive literature search across multiple databases (Web of Science, PubMed, Embase, etc.) up to March 2026.
- Utilized the Prediction Model Risk of Bias Assessment Tool (PROBAST) for quality appraisal.
- Extracted Area Under the Curve (AUC) for meta-analysis of externally validated models.
Main Results:
- Included 16 studies with 29 PP-PTSD prediction models; 13 models employed machine learning.
- All studies exhibited a high risk of bias, mainly due to data quality and reporting deficiencies.
- Validated logistic regression models demonstrated a pooled AUC of 0.86, with moderate heterogeneity.
Conclusions:
- Existing PP-PTSD prediction models have preliminary potential but significant methodological flaws.
- Future research should adhere to TRIPOD guidelines, emphasizing rigorous design and external validation.
- Improved models are needed for reliable application across diverse populations.
