Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

Tarimo, Clifford Silver; Bhuyan, Soumitra S.; Li, Quanman; Mahande, Michael Johnson; Wu, Jian; Fu, Xiaoli

doi:10.1136/bmjopen-2021-051925

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…Regarding IOL, the choice of cervical ripening methods varied according to WHO recommendations and Bishop scores. These included oxytocin infusion, prostaglandin analogues and extra‐amniotic balloon catheters 1 , 48 , 49 . Premature rupture of membranes was defined as membrane rupture at term before labor onset.…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning for fetal ultrasound and maternal–fetal data to predict mode of delivery after labor induction

Ferreira,

Simões,

Pereira

et al. 2024

Sci Rep

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Providing adequate counseling on mode of delivery after induction of labor (IOL) is of utmost importance. Various AI algorithms have been developed for this purpose, but rely on maternal–fetal data, not including ultrasound (US) imaging. We used retrospectively collected clinical data from 808 subjects submitted to IOL, totaling 2024 US images, to train AI models to predict vaginal delivery (VD) and cesarean section (CS) outcomes after IOL. The best overall model used only clinical data (F1-score: 0.736; positive predictive value (PPV): 0.734). The imaging models employed fetal head, abdomen and femur US images, showing limited discriminative results. The best model used femur images (F1-score: 0.594; PPV: 0.580). Consequently, we constructed ensemble models to test whether US imaging could enhance the clinical data model. The best ensemble model included clinical data and US femur images (F1-score: 0.689; PPV: 0.693), presenting a false positive and false negative interesting trade-off. The model accurately predicted CS on 4 additional cases, despite misclassifying 20 additional VD, resulting in a 6.0% decrease in average accuracy compared to the clinical data model. Hence, integrating US imaging into the latter model can be a new development in assisting mode of delivery counseling.

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Section: Methodsmentioning

confidence: 99%

Ensemble learning for fetal ultrasound and maternal–fetal data to predict mode of delivery after labor induction

Ferreira,

Simões,

Pereira

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model

Ferreira,

Simões,

Correia

et al. 2024

Acta Obstet Gynecol Scand

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IntroductionInduction of labor, often used for pregnancy termination, has globally rising rates, especially in high‐income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context.Material and MethodsWe aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10‐fold cross‐validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia.ResultsOur sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783–0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565–0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery.ConclusionsThis prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling.

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Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data

Wang,

Liao,

Zhang

et al. 2024

Clin. Exp. Obstet. Gynecol.

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Background: Postpartum haemorrhage (PPH) is a serious complication and a cause of maternal mortality after delivery. This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. Methods: The clinical data of women who gave birth at Wenzhou People’s Hospital from 1 January 2021, to 30 March 2022, were retrospectively analysed, and the women were divided into a high haemorrhage group (337 patients) and a low haemorrhage group (431 patients) based on the amount of blood loss. Machine learning algorithms were used to identify the features associated with postpartum haemorrhage from multiple clinical variables using feature selection methods, such as recursive feature elimination (RFE), recursive feature elimination with cross-validation (RFECV), and SelectKBest, and to establish prediction models. Results: For all women, the features associated with postpartum haemorrhage were ‘age’, ‘newborn weight’, ‘gestational week’, ‘perineal laceration’, and ‘caesarean section’. The prediction model established by the random forest classifier performed best, with an F1 score of 0.73 and an area under the curve (AUC) of 0.84. For women who underwent caesarean section or had a vaginal delivery, the features associated with postpartum haemorrhage risk were different. The risk factors for postpartum haemorrhage in women who underwent caesarean section were ‘age’, ‘parity’, ‘preterm birth’, and ‘placenta previa’. The prediction model established by the random forest classifier performed best, with an F1 value of 0.96 and an AUC of 0.95. The risk factors for postpartum haemorrhage in women with vaginal delivery were ‘age’, ‘parity’, ‘gestational week’, ‘diabetes’, ‘assisted reproduction’, ‘hypertension (preeclampsia)’, and ‘multiple pregnancy’. The prediction model established by the AdaBoost classifier performed best, with an F1 value of 0.65 and an AUC of 0.76. Conclusions: Machine learning algorithms can effectively identify the features associated with postpartum haemorrhage risk from clinical variables and establish accurate prediction models, offering a novel approach for clinicians to assess the risk of and prevent postpartum haemorrhage.

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Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

Cited by 4 publications

References 65 publications

Ensemble learning for fetal ultrasound and maternal–fetal data to predict mode of delivery after labor induction

Ensemble learning for fetal ultrasound and maternal–fetal data to predict mode of delivery after labor induction

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model

Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data

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