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Predicting postpartum haemorrhage: A systematic review of prognostic models

BACKGROUND: Postpartum 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. AIMS: To systematically identif...

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Autores principales: Carr, Bethany L., Jahangirifar, Maryam, Nicholson, Ann E., Li, Wentao, Mol, Ben W., Licqurish, Sharon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087871/
https://www.ncbi.nlm.nih.gov/pubmed/35918188
http://dx.doi.org/10.1111/ajo.13599
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author Carr, Bethany L.
Jahangirifar, Maryam
Nicholson, Ann E.
Li, Wentao
Mol, Ben W.
Licqurish, Sharon
author_facet Carr, Bethany L.
Jahangirifar, Maryam
Nicholson, Ann E.
Li, Wentao
Mol, Ben W.
Licqurish, Sharon
author_sort Carr, Bethany L.
collection PubMed
description BACKGROUND: Postpartum 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. AIMS: To systematically identify and appraise existing prognostic models for PPH and ascertain suitability for clinical use. MATERIALS AND METHODS: MEDLINE, 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. RESULTS: Sixteen 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. CONCLUSIONS: No 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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spelling pubmed-100878712023-04-12 Predicting postpartum haemorrhage: A systematic review of prognostic models Carr, Bethany L. Jahangirifar, Maryam Nicholson, Ann E. Li, Wentao Mol, Ben W. Licqurish, Sharon Aust N Z J Obstet Gynaecol Systematic Review BACKGROUND: Postpartum 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. AIMS: To systematically identify and appraise existing prognostic models for PPH and ascertain suitability for clinical use. MATERIALS AND METHODS: MEDLINE, 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. RESULTS: Sixteen 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. CONCLUSIONS: No 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. John Wiley and Sons Inc. 2022-08-02 2022-12 /pmc/articles/PMC10087871/ /pubmed/35918188 http://dx.doi.org/10.1111/ajo.13599 Text en © 2022 The Authors. Australian and New Zealand Journal of Obstetrics and Gynaecology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Obstetricians and Gynaecologists. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Systematic Review
Carr, Bethany L.
Jahangirifar, Maryam
Nicholson, Ann E.
Li, Wentao
Mol, Ben W.
Licqurish, Sharon
Predicting postpartum haemorrhage: A systematic review of prognostic models
title Predicting postpartum haemorrhage: A systematic review of prognostic models
title_full Predicting postpartum haemorrhage: A systematic review of prognostic models
title_fullStr Predicting postpartum haemorrhage: A systematic review of prognostic models
title_full_unstemmed Predicting postpartum haemorrhage: A systematic review of prognostic models
title_short Predicting postpartum haemorrhage: A systematic review of prognostic models
title_sort predicting postpartum haemorrhage: a systematic review of prognostic models
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087871/
https://www.ncbi.nlm.nih.gov/pubmed/35918188
http://dx.doi.org/10.1111/ajo.13599
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