Risk management of vaginal birth after cesarean section (Review)

Cărăuleanu, Alexandru; Vasilache, Ingrid-Andrada; Nemescu, Dragoş; Socolov, Demetra

doi:10.3892/etm.2021.10545

Cited by 2 publications

(3 citation statements)

References 52 publications

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“…In this paper, it was established that vaginal birth after caesarean section is associated with lower rates of bleeding, thromboembolism, and infection, as well as a shorter recovery period compared to women who choose elective repeat caesarean section. Thus, it was highlighted that vaginal birth after caesarean section may reduce the risk of maternal consequences following multiple caesarean sections [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…The adaptation to the extrauterine life of the newborn is different in caesarean section compared to vaginal delivery, since the caesarean section abruptly interrupts this natural process, so that the stimulus to initiate breathing is not the passage through the birth canal but tactile stimulation or positive pressure ventilation [ 8 ]. Some meta-analyses even state that caesarean sections significantly increase the risk of respiratory infections and asthma in the newborn [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Applying the Modified Ten-Group Robson Classification in a Spanish Tertiary Hospital

Gutiérrez-Martínez,

Fernández-Martínez,

Adánez-García

et al. 2023

JCM

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Background: Caesarean section is necessary to save the lives of mothers and newborns at times, but it is important to perform it only when it is essential due to all the risks involved. This study aimed to examine the rate of caesarean sections performed at a tertiary hospital using the Robson classification to detect methods for the detection of and/or reduction in these caesarean section rates. Methods: A descriptive, cross-sectional study of a retrospective database was carried out. Results: A total of 10,317 births were assessed. The Robson classification was used to assess these interventions and verify whether the indication for performed caesarean sections was appropriate. In total, 2036 births by caesarean section were performed in the whole sample. The annual caesarean section rate varied between 18.67% and 21.18%. Conclusions: Caesarean sections increased by about 20% in 2021 compared to 2020 even though the trend over the years of study was decreasing. Vaginal delivery after caesarean section is a reasonable and safe option. Caesarean section rates could be improved, mostly in Robson’s Group 2. The Robson classification facilitated progress in the implementation of measures aimed at improving care and adjusting caesarean section rates.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying the Modified Ten-Group Robson Classification in a Spanish Tertiary Hospital

Gutiérrez-Martínez,

Fernández-Martínez,

Adánez-García

et al. 2023

JCM

View full text Add to dashboard Cite

show abstract

“…Likewise, the proportion of women with previous CD is stable around 8.6% [1]. Local guidelines and practice regarding trial of labor after a cesarean (TOLAC) is heterogeneous worldwide [2,3]. This is re ected in the high variability of the number of TOLAC.…”

Section: Introductionmentioning

confidence: 99%

Prediction of vaginal birth after previous cesarean delivery using explainable machine learning models

Thagaard,

Stentoft-Larsen,

Iglesias

et al. 2024

Preprint

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Purpose This study aims to train a novel explainable machine learning method (QLattice) to predict successful vaginal birth after cesarean and compare the performance of these models with other known machine learning- and logistic regression models. Methods A Danish cohort study including 11 017 women with a prior cesarean giving birth during year 2004–2016 was used to train and evaluate three machine learning algorithms (LASSO, Random Forest, and QLattice). Grobmans logistic regression model was used as baseline. Two models were developed (antenatal and prelabor). Results Overall, 4 897 (44,4%) women had a trial of labor, with 3 441 (70.3%) women delivering vaginally. In the antenatal model predictive variables were epidural (OR = 0.53), breech presentation (OR = 1.70), mothers height (OR = 1.03), pre-pregnancy BMI (OR = 0.95), any vaginal birth (OR = 7.74), and vaginal birth before cesarean (0.24). In the prelabor model induction of labor (OR = 0.59), primary rupture of membranes (OR = 0.52), and infant weight (OR = 0.52) were additional predictors. For the antenatal prediction models, the area under the curve (AUC) for the different methods were QLattice 0.73 (0.70–0.76), LASSO with 62 features 0.75 (0.72–0.78), Random Forest 0.74 (0.71–0.77), and Grobman 0.68 (0.65–0.71). For the Prelabor model, AUC were for QLattice 0.77 (0.74–0.80), LASSO with 67 features 0.77 (0.74–0.80), Random Forest 0.75 (0.72–0.78), and Grobman 0.70 (0.66–0.73). Conclusions QLattice performs equal to other machine learning algorithms contributing further by giving explainable insight with fewer variables. Future prospective studies will reveal whether individual decision support tools, based on these models can improve outcomes in women with previous cesarean.

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Risk management of vaginal birth after cesarean section (Review)

Cited by 2 publications

References 52 publications

Applying the Modified Ten-Group Robson Classification in a Spanish Tertiary Hospital

Applying the Modified Ten-Group Robson Classification in a Spanish Tertiary Hospital

Prediction of vaginal birth after previous cesarean delivery using explainable machine learning models

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