Archives of women's mental health · 2026
Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализОбзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shi Huijing, Liu Jinyang, Yang Chaochao, Xue Kexin, Pan Liping, Zeng Yanli
Аннотация
BACKGROUND: Postpartum 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.
METHODS: We 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).
RESULTS: Seventeen 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.
CONCLUSION: This 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.
METHODS: We 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).
RESULTS: Seventeen 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.
CONCLUSION: This 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.