SynthesisArchives of women's mental health2026
Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.
Synthesis in Archives of women's mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
backgroundPostpartum post-traumatic stress disorder acts as an invisible scar, eroding maternal mental health and exerting long-term effects on infant development. Existing screening methods have limitations, hindering early prevention. Predictive models offer a promising approach for early identification of postpartum post-traumatic stress disorder. This study aims to establish a current evidence baseline and outline future directions through a systematic review and meta-analysis.
methodsWe systematically searched ten databases-PubMed, Web of Science, Embase, Cochrane Library, PsycINFO, CINAHL, Scopus, and three Chinese databases (CNKI, Wanfang, CBM)-from inception to August 12, 2025. Studies developing and/or validating multivariable prediction models for post-traumatic stress disorder within 12 months postpartum were included. The PROBAST tool was used to assess risk of bias and applicability. Random-effects models were employed for meta-analysis of validated model performance (AUC, sensitivity, specificity).
resultsSeventeen studies involving 17,236 participants were included. The pooled AUC was 0.84 (95% CI: 0.80-0.88), with a pooled sensitivity of 0.77 and specificity of 0.82. Frequently identified predictors included mode of delivery, level of social support, pregnancy complications, and fear of childbirth. Considerable heterogeneity was observed (I² = 89.7%), and calibration and external validation were insufficiently reported.
conclusionThis review demonstrates that prediction models for postpartum post-traumatic stress disorder show promising predictive accuracy and development potential. However, methodological flaws and high heterogeneity constrain their generalizability. Future research should adhere to TRIPOD guidelines, conduct large-sample, multi-center external validations, and strengthen calibration and clinical utility assessments. Such efforts could help translate prediction models into clinically usable tools, shifting the healthcare paradigm from reactive symptom management to proactive, individualized risk prevention, thereby reducing maternal trauma and optimizing family outcomes.
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