SynthesisThe Australian & New Zealand journal of obstetrics & gynaecology2022
Predicting postpartum haemorrhage: A systematic review of prognostic models.
Synthesis in The Australian & New Zealand journal of obstetrics & gynaecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prognostic accuracy of clinical markers of postpartum bleeding in predicting maternal mortality or severe morbidity: a WHO individual participant data meta-analysis.Lancet (London, England) · 2025Pooled it
- Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review.BMJ open · 2025Pooled it
- Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study.BMC pregnancy and childbirth · 2023Trial
- Risk factors for postpartum hemorrhage after pregnancy termination for fetal malformations: a retrospective study and internally validated prediction model.Archives of gynecology and obstetrics · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Evaluation of the Association Between Postpartum Hemorrhage and Antidepressant Use: A Mendelian Randomization Study.Drugs - real world outcomes · 2025Article
- Placental biomarkers in second trimester maternal serum are associated with postpartum hemorrhage: a secondary analysis of the NuMoM2b dataset.medRxiv : the preprint server for health sciences · 2025Article
- Quantifying the Accuracy of Clinician Risk Assessment for Postpartum Hemorrhage.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2024Article
- Development of antepartum risk prediction model for postpartum hemorrhage in Lagos, Nigeria: A prospective cohort study (Predict-PPH study).International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2024Article
- External Validation of Prognostic Models in Critical Care: A Cautionary Tale From COVID-19 Pneumonitis.Critical care explorations · 2024Article
- Challenges and Opportunities for Data Science in Women's Health.Annual review of biomedical data science · 2023Review
- Comparing visual estimation and hematocrit change in the assessment of blood loss among women undergoing cesarean delivery in a tertiary facility in northern Uganda.Therapeutic advances in reproductive healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostpartum haemorrhage (PPH) remains a leading cause of maternal mortality and morbidity worldwide, and the rate is increasing. Using a reliable predictive model could identify those at risk, support management and treatment, and improve maternal outcomes.
aimsTo systematically identify and appraise existing prognostic models for PPH and ascertain suitability for clinical use. MATERIALS AND
methodsMEDLINE, CINAHL, Embase, and the Cochrane Library were searched using combinations of terms and synonyms, including 'postpartum haemorrhage', 'prognostic model', and 'risk factors'. Observational or experimental studies describing a prognostic model for risk of PPH, published in English, were included. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist informed data extraction and the Prediction Model Risk of Bias Assessment Tool guided analysis.
resultsSixteen studies met the inclusion criteria after screening 1612 records. All studies were hospital settings from eight different countries. Models were developed for women who experienced vaginal birth (n = 7), caesarean birth (n = 2), any type of birth (n = 2), hypertensive disorders (n = 1) and those with placental abnormalities (n = 4). All studies were at high risk of bias due to use of inappropriate analysis methods or omission of important statistical considerations or suboptimal validation.
conclusionsNo existing prognostic models for PPH are ready for clinical application. Future research is needed to externally validate existing models and potentially develop a new model that is reliable and applicable to clinical practice.
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