Practice variation of vaginal birth after cesarean and the influence of risk factors at patient level: a retrospective cohort study

Vankan, Emy; Schoorel, Ellen; Kuijk, Sander M. J. van; Mol, Ben W.; Nijhuis, Jan G.; Aardenburg, Robert; Alink, Marleen; Boer, Karin; Delemarre, Friso M.C.; Dirksen, Carmen D.; Dooren, Ivo M.A. van; Franssen, Maureen; Kaplan, Mesrure; Kleiverda, Gunilla; Kuppens, Simone; Kwee, Anneke; Langenveld, Josje; Lim, F. T. H.; Melman, Sonja; Sikkema, Marko; Smits, Luc; Visser, Harry; Woiski, Mallory; Scheepers, Hubertina C.J.; Hermens, Rosella

doi:10.1111/aogs.13059

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…In the Netherlands in 2010, rates of successful trial of labour after caesarean varied between hospitals from 50 to 90 per cent for all women who started a trial of labour, while their identifiable risk factors did not differ. [ 11 ] A comparable variation has been described for the US. [ 12 ] This suggests that circumstances other than strictly medical risk factors influence the chance of a successful trial of labour.…”

Section: Introductionmentioning

confidence: 76%

Decision-making during trial of labour after caesarean; a qualitative study with gynaecologists

2018

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ObjectiveThe attempt of a woman to deliver vaginally after having had a caesarean in a previous pregnancy is increasingly common in current obstetric practice. During a trial of labour after caesarean, gynaecologists consider whether continuing vaginal birth is safe or, alternately, whether a repeat caesarean is advised. There is large variation in the success rates of women with comparable medical risk factors, requiring better insight in how this assessment is made. As a window of opportunity to intervene in this unexplained variation in practice in specific, and in the globally rising caesarean rate in general, our aim was to increase understanding of gynaecologists’ decision-making during trial of labour.Study designWe conducted a constructivist grounded theory study, interviewing Dutch gynaecologists. Data collection and analysis were performed concurrently. Initial convenience sampling shifted to theoretical sampling as the study progressed. Data collection continued until theoretical sufficiency was reached. We applied open and axial codes to transcripts of the interviews, and then assembled the axial codes into themes that built up to an emerging theoretical framework.ResultsNine gynaecologists were interviewed. Data indicated they continuously weighed the chance of a successful outcome of trial of labour against the likelihood of adversities. Patients’ opinions, aspects of progress of labour and gynaecologists’ personal stances regarding trial of labour played a role in the decision-making process; these factors are influenced by organisational affordances and culture. Variation in the assessment of individuals’ chances of success and variable thresholds for a repeat caesarean added to the complexity of the decision-making.ConclusionThis study pieced together patient-, delivery-, physician- and society-related factors that result in vitally important decisions during trial of labour after caesarean; it reveals the complexity as well as the repetitive patterns involved in this process. Exposing these factors offers opportunities to incorporate the decision-making process in targeted educational interventions, with the aim of modifying the underlying assumptions and concepts in order to reduce practice variation.

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Section: Introductionmentioning

confidence: 76%

Decision-making during trial of labour after caesarean; a qualitative study with gynaecologists

2018

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“…Though various research definitions of obstetric risk exist, these often reflect fetal risks or are based on maternal social and medical conditions that do not necessarily indicate a need for higher acuity care to protect maternal health. [1][2][3] Clinical guidelines, though useful in practice, may not be appropriate for research sample selection because they 1) do not differentiate 3 between maternal and fetal risk, 2) focus on identifying women at low risk rather than defining high maternal risk, and 3) rely on clinical information not available in administrative data. 4,5 We were concerned that any conventional sample selection method we used would overestimate the clinical maternal risk, causing misclassification bias in our study.…”

Section: Introductionmentioning

confidence: 99%

Methodology for sampling women at high maternal risk in administrative data

Vanderlaan

Dunlop

Rochat

et al. 2019

Preprint

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“…As part of a project examining the effects of regionalization of maternal care, we realized there was no standard for identifying a sample of women likely to be transferred to higher acuity care based on risks to maternal health. Though various research definitions of obstetric risk exist, these often reflect fetal risks or are based on maternal social and medical conditions that do not necessarily indicate a need for higher acuity care to protect maternal health [1][2][3]. Clinical guidelines, though useful in practice, may not be appropriate for research sample selection because they 1) do not differentiate between maternal and fetal risk, 2) focus on identifying women at low risk rather than defining high maternal risk, and 3) rely on clinical information not available in administrative data [4,5].…”

Section: Introductionmentioning

confidence: 99%

Methodology for sampling women at high maternal risk in administrative data

Vanderlaan

Dunlop

Rochat

et al. 2019

Preprint

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Background In population level studies, the conventional practice of categorizing women into low and high maternal risk samples relies upon ascertaining the presence of various comorbid conditions in administrative data. Two problems with the conventional method include variability in the recommended comorbidities to consider and inability to distinguish between maternal and fetal risks. High maternal risk sample selection may be improved by using the Obstetric Comorbidity Index (OCI), a system of risk scoring based on weighting comorbidities associated with maternal end organ damage. The purpose of this study was to compare the net benefit of using OCI risk scoring vs the conventional risk identification method to identify a sample of women at high maternal risk in administrative data. Methods This was a net benefit analysis using linked delivery hospitalization discharge and vital records data for women experiencing singleton births in Georgia from 2008-2012. We compared the value identifying a sample of women at high maternal risk using the OCI score to the conventional method of dichotomous identification of any comorbidities. Value was measured by the ability to select a sample of women designated as high maternal risk who experienced severe maternal morbidity or mortality. Results The high maternal risk sample created with the OCI had a small but positive net benefit (+ 0.6), while the conventionally derived sample had a negative net benefit indicating the sample selection performed worse than identifying no woman as high maternal risk. Conclusions The OCI can be used to select women at high maternal risk in administrative data. The OCI provides a consistent method of identification for women at risk of maternal morbidity and mortality and avoids confounding all obstetric risk factors with specific maternal risk factors. Using the OCI may help reduce misclassification as high maternal risk and improve the consistency in identifying women at high maternal risk in administrative data.

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Practice variation of vaginal birth after cesarean and the influence of risk factors at patient level: a retrospective cohort study

Abstract: A large practice variation exists in elective repeat cesarean section and vaginal birth after cesarean rates that can only partially be explained by risk factors at patient level.

Cited by 11 publications

References 13 publications

Decision-making during trial of labour after caesarean; a qualitative study with gynaecologists

Decision-making during trial of labour after caesarean; a qualitative study with gynaecologists

Methodology for sampling women at high maternal risk in administrative data

Methodology for sampling women at high maternal risk in administrative data

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