Systematic Review of Clinical Prediction Models for the Risk of Emergency Caesarean Births.
emergency caesareans
maternal Health
prediction
prognostic
risk factors
Journal
BJOG : an international journal of obstetrics and gynaecology
ISSN: 1471-0528
Titre abrégé: BJOG
Pays: England
ID NLM: 100935741
Informations de publication
Date de publication:
10 Sep 2024
10 Sep 2024
Historique:
revised:
07
08
2024
received:
13
05
2024
accepted:
10
08
2024
medline:
11
9
2024
pubmed:
11
9
2024
entrez:
11
9
2024
Statut:
aheadofprint
Résumé
Globally, caesarean births (CB), including emergency caesareans births (EmCB), are rising. It is estimated that nearly a third of all births will be CB by 2030. Identify and summarise the results from studies developing and validating prognostic multivariable models predicting the risk of EmCBs. Ultimately understanding the accuracy of their development, and whether they are operationalised for use in routine clinical practice. Studies were identified using databases: MEDLINE, CINAHL, Cochrane Central and Scopus with a search strategy tailored to models predicting EmCBs. Prospective studies developing and validating clinical prediction models, with two or more covariates, to predict risk of EmCB. Data were extracted onto a proforma using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). In total, 8083 studies resulted in 56 unique prediction modelling studies and seven validating studies, with a total of 121 different predictors. Frequently occurring predictors included maternal height, maternal age, parity, BMI and gestational age. PROBAST highlighted 33 studies with low overall bias, and these all internally validated their model. Thirteen studies externally validated; only eight of these were graded an overall low risk of bias. Six models offered applications that could be readily used, but only one provided enough time to offer a planned caesarean birth (pCB). These well-refined models have not been recalibrated since development. Only one model, developed in a relatively low-risk population, with data collected a decade ago, remains useful at 36 weeks for arranging a pCB. To improve personalised clinical conversations, there is a pressing need for a model that accurately predicts the timely risk of an EmCB for women across diverse clinical backgrounds. PROSPERO registration number: CRD42023384439.
Sections du résumé
BACKGROUND
BACKGROUND
Globally, caesarean births (CB), including emergency caesareans births (EmCB), are rising. It is estimated that nearly a third of all births will be CB by 2030.
OBJECTIVES
OBJECTIVE
Identify and summarise the results from studies developing and validating prognostic multivariable models predicting the risk of EmCBs. Ultimately understanding the accuracy of their development, and whether they are operationalised for use in routine clinical practice.
SEARCH STRATEGY
METHODS
Studies were identified using databases: MEDLINE, CINAHL, Cochrane Central and Scopus with a search strategy tailored to models predicting EmCBs.
SELECTION CRITERIA
METHODS
Prospective studies developing and validating clinical prediction models, with two or more covariates, to predict risk of EmCB.
DATA COLLECTION AND ANALYSIS
METHODS
Data were extracted onto a proforma using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
RESULTS
RESULTS
In total, 8083 studies resulted in 56 unique prediction modelling studies and seven validating studies, with a total of 121 different predictors. Frequently occurring predictors included maternal height, maternal age, parity, BMI and gestational age. PROBAST highlighted 33 studies with low overall bias, and these all internally validated their model. Thirteen studies externally validated; only eight of these were graded an overall low risk of bias. Six models offered applications that could be readily used, but only one provided enough time to offer a planned caesarean birth (pCB). These well-refined models have not been recalibrated since development. Only one model, developed in a relatively low-risk population, with data collected a decade ago, remains useful at 36 weeks for arranging a pCB.
CONCLUSION
CONCLUSIONS
To improve personalised clinical conversations, there is a pressing need for a model that accurately predicts the timely risk of an EmCB for women across diverse clinical backgrounds.
TRIAL REGISTRATION
BACKGROUND
PROSPERO registration number: CRD42023384439.
Identifiants
pubmed: 39256942
doi: 10.1111/1471-0528.17948
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : National Institute for Health and Care Research
ID : NIHR302530
Investigateurs
Sheelagh McGuiness
(S)
Anna Davies
(A)
Dame Tina Lavender
(DT)
Christy Burden
(C)
Jonathan Ives
(J)
Simon Grant
(S)
Sherif Abdel-Fattah
(S)
Danya Bakhbakhi
(D)
Andrew Demetri
(A)
Mairead Black
(M)
Sam Finnikin
(S)
Amie Wilson
(A)
Alexandra Freeman
(A)
Pete Blair
(P)
Kate Birchenall
(K)
Joanne Johnson
(J)
Amber Marshall
(A)
Informations de copyright
© 2024 The Author(s). BJOG: An International Journal of Obstetrics and Gynaecology published by John Wiley & Sons Ltd.
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