Prediction models of vaginal birth after cesarean delivery: A systematic review

Deng, Bo; Li, Yan; Chen, Jia-Yin; Guo, J.; Tan, Jing; Yang, Yang; Liu, Ning

doi:10.1016/j.ijnurstu.2022.104359

Cited by 4 publications

(7 citation statements)

References 57 publications

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“…Our study distinguishes itself by achieving a comparable predictive performance with only seven parameters, a significant reduction compared to that in existing research. 4 , 5 Our strategy consisted of a two-fold approach: during the training phase, we employed boosting techniques and applied bootstrapping for rigorous testing. 31 , 32 Our results demonstrated that when analyzing tabular data, the tree-based models consistently outperformed the regression models.…”

Section: Discussionmentioning

confidence: 99%

“… 2 , 3 Nevertheless, the decision-making processes regarding VBAC are complex and multifaceted, necessitating a careful consideration of various medical, obstetric, and individual factors. 4 , 5 …”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) has emerged as a powerful tool in healthcare, offering the potential to support medical professionals and patients in making informed decisions. 4 , 5 Drawing on previous research findings, 6 – 9 we created an innovative web-based platform serving as the basis for a decision-aid (DA) system for birth choices aimed at fostering shared decision-making (SDM). 10 Through this DA platform, we strive to enhance understanding, boost confidence, and improve communication between medical professionals and pregnant women.…”

Section: Introductionmentioning

confidence: 99%

“…Although several VBAC prediction models have been developed, their generalizability and applicability remain unclear 4 , 5 ; thus, high-quality internal and external validation studies are needed. Most importantly, none of these VBAC prediction models intergrade the AI technique into the DA platform.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

Yang,

Wang,

Lin

et al. 2024

DIGITAL HEALTH

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Background Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM). Materials and Methods Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification. Results Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86–0.94) and an area under the curve of 0.89 (95% CI: 0.86–0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis. Conclusion Using AI techniques offers a more accurate assessment of VBAC risks, boosting women’s confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

Yang,

Wang,

Lin

et al. 2024

DIGITAL HEALTH

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“…Chai et al [ 45 ] use the DMAIC cycle and Lean Six Sigma methodology to identify causes and thus reduce the rate of CSs, while Verhoeven et al [ 46 ] use logistic regression-based models to discriminate whether or not to perform CS from induced labor. The review conducted by Deng et al [ 47 ] shows us that logistic regression models are the most widely used models in the literature to study and predict VD after CS, using predictors such as body mass index, previous vaginal delivery, and maternal age. As performed by Ehrenberg et al [ 48 ], model predictors could also be used to identify major risk factors to analyze the impact of Diabetes or Obesity on the risk of performing CS.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

et al. 2023

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Caesarean section (CS) rate has seen a significant increase in recent years, especially in industrialized countries. There are, in fact, several causes that justify a CS; however, evidence is emerging that non-obstetric factors may contribute to the decision. In reality, CS is not a risk-free procedure. The intra-operative, post-pregnancy risks and risks for children are just a few examples. From a cost point of view, it must be considered that CS requires longer recovery times, and women often stay hospitalized for several days. This study analyzed data from 12,360 women who underwent CS at the “San Giovanni di Dio e Ruggi D’Aragona” University Hospital between 2010 and 2020 by multiple regression algorithms, including multiple linear regression (MLR), Random Forest, Gradient Boosted Tree, XGBoost, and linear regression, classification algorithms and neural network in order to study the variation of the dependent variable (total LOS) as a function of a group of independent variables. We identify the MLR model as the most suitable because it achieves an R-value of 0.845, but the neural network had the best performance (R = 0.944 for the training set). Among the independent variables, Pre-operative LOS, Cardiovascular disease, Respiratory disorders, Hypertension, Diabetes, Haemorrhage, Multiple births, Obesity, Pre-eclampsia, Complicating previous delivery, Urinary and gynaecological disorders, and Complication during surgery were the variables that significantly influence the LOS. Among the classification algorithms, the best is Random Forest, with an accuracy as high as 77%. The simple regression model allowed us to highlight the comorbidities that most influence the total LOS and to show the parameters on which the hospital management must focus for better resource management and cost reduction.

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Construction and evaluation of nursing-sensitive quality indicators for vaginal birth after cesarean: A Delphi study based on Chinese population

Liu,

Zhang

et al. 2023

Heliyon

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Prediction models of vaginal birth after cesarean delivery: A systematic review

Cited by 4 publications

References 57 publications

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

Construction and evaluation of nursing-sensitive quality indicators for vaginal birth after cesarean: A Delphi study based on Chinese population

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