BMC psychiatry · 2026
Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание 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.
Zhang Shaoying, Kang Liping, Yang Shilai, Su Xiaojuan, Lin Rongjin, Shariff Noorsuzana Mohd
Аннотация
BACKGROUND: Despite the increasing number of studies on prediction models for identifying the risk of postpartum post-traumatic stress disorder (PP-PTSD), the quality and clinical applicability of these models have not been clarified yet.
OBJECTIVES: To systematically review and appraise the prediction models for PP-PTSD.
METHODS: From inception to March 30, 2026, Web of Science, PubMed, the Cochrane Library, Embase, Scopus, CNKI, WANFANG and VIP were searched for primary studies involving development or validation of prediction models. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the quality of the literature for issues of bias and applicability. The area under the curve (AUC) of externally validated models was extracted for meta-analysis. The protocol for this study is registered with PROSPERO (registration number: CRD42025630468).
RESULTS: Sixteen studies comprising 29 models met the inclusion criteria. Thirteen studies were from China, and 13 models applied machine learning. All studies were judged to have high risk of bias, primarily due to data source quality and inadequate reporting of outcomes and analysis. Fourteen studies(87.5%) were rated as having low concern regarding applicability. Seven studies (43.8%) performed external validation, including only two studies validating for ML models. The pooled AUC of these validated logistic regression models was 0.86 (95% CI: 0.79-0.90). Moderate heterogeneity was observed across these studies (I2 = 70.8%).
CONCLUSION: Current PP-PTSD prediction models show preliminary predictive potential but suffer from significant methodological limitations. Future research needs to follow the TRIPOD guidelines and obtain reliable models through rigorous study design and external validation. This will facilitate external validation of the models in the next step, so that it can be promoted to diverse populations.
OBJECTIVES: To systematically review and appraise the prediction models for PP-PTSD.
METHODS: From inception to March 30, 2026, Web of Science, PubMed, the Cochrane Library, Embase, Scopus, CNKI, WANFANG and VIP were searched for primary studies involving development or validation of prediction models. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the quality of the literature for issues of bias and applicability. The area under the curve (AUC) of externally validated models was extracted for meta-analysis. The protocol for this study is registered with PROSPERO (registration number: CRD42025630468).
RESULTS: Sixteen studies comprising 29 models met the inclusion criteria. Thirteen studies were from China, and 13 models applied machine learning. All studies were judged to have high risk of bias, primarily due to data source quality and inadequate reporting of outcomes and analysis. Fourteen studies(87.5%) were rated as having low concern regarding applicability. Seven studies (43.8%) performed external validation, including only two studies validating for ML models. The pooled AUC of these validated logistic regression models was 0.86 (95% CI: 0.79-0.90). Moderate heterogeneity was observed across these studies (I2 = 70.8%).
CONCLUSION: Current PP-PTSD prediction models show preliminary predictive potential but suffer from significant methodological limitations. Future research needs to follow the TRIPOD guidelines and obtain reliable models through rigorous study design and external validation. This will facilitate external validation of the models in the next step, so that it can be promoted to diverse populations.