We have located links that may give you full text access.
Comparative Study
Journal Article
Observational Study
Vaginal birth after caesarean section prediction models: a UK comparative observational study.
OBJECTIVE: Primarily, to assess the performance of three statistical models in predicting successful vaginal birth in patients attempting a trial of labour after one previous lower segment caesarean section (TOLAC). The statistically most reliable models were subsequently subjected to validation testing in a local antenatal population.
STUDY DESIGN: A retrospective observational study was performed with study data collected from the Northern Ireland Maternity Service Database (NIMATs). The study population included all women that underwent a TOLAC (n=385) from 2010 to 2012 in a regional UK obstetric unit. Data was collected from the Northern Ireland Maternity Service Database (NIMATs). Area under the curve (AUC) and correlation analysis was performed.
RESULTS: Of the three prediction models evaluated, AUC calculations for the Smith et al., Grobman et al. and Troyer and Parisi Models were 0.74, 0.72 and 0.65, respectively. Using the Smith et al. model, 52% of women had a low risk of caesarean section (CS) (predicted VBAC >72%) and 20% had a high risk of CS (predicted VBAC <60%), of whom 20% and 63% had delivery by CS. The fit between observed and predicted outcome in this study cohort using the Smith et al. and Grobman et al. models were greatest (Chi-square test, p=0.228 and 0.904), validating both within the population.
CONCLUSION: The Smith et al. and Grobman et al. models could potentially be utilized within the UK to provide women with an informed choice when deciding on mode of delivery after a previous CS.
STUDY DESIGN: A retrospective observational study was performed with study data collected from the Northern Ireland Maternity Service Database (NIMATs). The study population included all women that underwent a TOLAC (n=385) from 2010 to 2012 in a regional UK obstetric unit. Data was collected from the Northern Ireland Maternity Service Database (NIMATs). Area under the curve (AUC) and correlation analysis was performed.
RESULTS: Of the three prediction models evaluated, AUC calculations for the Smith et al., Grobman et al. and Troyer and Parisi Models were 0.74, 0.72 and 0.65, respectively. Using the Smith et al. model, 52% of women had a low risk of caesarean section (CS) (predicted VBAC >72%) and 20% had a high risk of CS (predicted VBAC <60%), of whom 20% and 63% had delivery by CS. The fit between observed and predicted outcome in this study cohort using the Smith et al. and Grobman et al. models were greatest (Chi-square test, p=0.228 and 0.904), validating both within the population.
CONCLUSION: The Smith et al. and Grobman et al. models could potentially be utilized within the UK to provide women with an informed choice when deciding on mode of delivery after a previous CS.
Full text links
Related Resources
Trending Papers
Challenges in Septic Shock: From New Hemodynamics to Blood Purification Therapies.Journal of Personalized Medicine 2024 Februrary 4
Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.International Journal of Molecular Sciences 2024 April 4
The 'Ten Commandments' for the 2023 European Society of Cardiology guidelines for the management of endocarditis.European Heart Journal 2024 April 18
A Guide to the Use of Vasopressors and Inotropes for Patients in Shock.Journal of Intensive Care Medicine 2024 April 14
Diagnosis and Management of Cardiac Sarcoidosis: A Scientific Statement From the American Heart Association.Circulation 2024 April 19
Essential thrombocythaemia: A contemporary approach with new drugs on the horizon.British Journal of Haematology 2024 April 9
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app