Deriving a prediction model for emergency cesarean delivery following induction of labor in singleton term pregnancies

Dorwal, Manisha; Yadav, Garima; Singh, Pratibha; Kathuria, Priyanka; Gothwal, Meenakshi; Ghuman, Navdeep Kaur; Shekhar, Shashank

doi:10.1002/ijgo.14403

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Similar to our findings in this study, nulliparity has been recognized in more than one model as a predictor for CS delivery, 26 , 30 , 35 especially if it is associated with certain intrapartum findings such as, mild to moderate contractions, slow cervical dilatation and high fetal station, in addition to advanced maternal age, high pre-pregnancy BMI and shorter maternal height. 15 …”

Section: Discussionsupporting

confidence: 88%

Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women

Wahabi,

Fayed,

Elmorshedy

et al. 2023

IJWH

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Background The worldwide rate of cesarean section (CS) is increasing. Development of prediction models for a specific population may improve the unmet need for CS as well as reduce the overuse of CS. Objective To explore risk factors associated with emergency CS, and to determine the accuracy of predicting it. Methods A retrospective analysis of the medical records of women who delivered between January 1, 2021-December 2022 was conducted, relevant maternal and neonatal data were retrieved. Results Out of 1793 deliveries, 447 (25.0%) had emergency CS. Compared to control, the risk of emergency CS was higher in primiparous women (OR 2.13, 95% CI 1.48 to 3.06), in women with higher Body mass index (BMI) (OR 1.77, 95% CI 1.27 to 2.47), in association with history of previous CS (OR 4.81, 95% CI 3.24 to 7.15) and in women with abnormal amniotic fluid (OR 2.30, 95% CI 1.55 to 3.41). Additionally, women with hypertensive disorders had a 176% increased risk of emergency CS (OR 2.76, 95% CI 1.35–5.63). Of note, the risk of emergency CS was more than three times higher in women who delivered a small for gestational age infant (OR 3.29, 95% CI 1.93–5.59). Based on the number of risk factors, a prediction model was developed, about 80% of pregnant women in the emergency CS group scored higher grades compared to control group. The area under the curve was 0.72, indicating a good discriminant ability of the model. Conclusion This study identified several risk factors associated with emergency CS in pregnant Saudi women. A prediction model showed 72% accuracy in predicting the likelihood of emergency CS. This information can be useful to individualize the risk of emergency CS, and to implement appropriate measures to prevent unnecessary CS.

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Section: Discussionsupporting

confidence: 88%

Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women

Wahabi,

Fayed,

Elmorshedy

et al. 2023

IJWH

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“…In our study, maternal characteristics related to CS outcomes were compatible with literature findings (see Table 1 ). In our dataset, women submitted to unplanned CS were older, shorter, with higher BMI and lower Bishop scores compared with the VD group 6 , 26 , 35 . Fetal US characteristics such as EFW and fetal biometry measures were also significantly larger for fetuses who underwent CS, which is also compatible with literature 36 – 38 .…”

Section: Discussionmentioning

confidence: 85%

“…Recent studies use electronic medical information on maternal and fetal characteristics to construct prediction models regarding mode of delivery after IOL 26 – 28 . However, very few use ML with the same goal 2 , 29 .…”

Section: Discussionmentioning

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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“…The final model included three significant covariates, parity, cervical dilation, and membrane integrity, which significantly influenced the efficacy of oxytocin. These three indicators are commonly used as variables in predictive models for the success rate of labor induction 20–24 . Parity showed a negative correlation with k in , indicating that the UCF production rate in multiparas was lower than that in nulliparas, suggesting weaker uterine activity in multiparas.…”

Section: Discussionmentioning

confidence: 99%

“…These three indicators are commonly used as variables in predictive models for the success rate of labor induction. [20][21][22][23][24] Parity showed a negative correlation with k in , indicating that the UCF production rate in multiparas was lower than that in nulliparas, suggesting weaker uterine activity in multiparas. This contradicts our traditional understanding as multiparas typically have higher induction success rates and shorter induction times.…”

Section: Other Findingsmentioning

confidence: 94%

Optimal starting dosing regimen of intravenous oxytocin for labor induction based on the population kinetic‐pharmacodynamic model of uterine contraction frequency

Yu,

Chen,

Zhao

et al. 2024

Pharmacotherapy

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BackgroundIntravenous oxytocin is commonly used for labor induction. However, a consensus on the initial dosing regimen is lac with conflicting research findings and varying guidelines. This study aimed to develop a population kinetic‐pharmacodynamic (K‐PD) model for oxytocin‐induced uterine contractions considering real‐world data and relevant influencing factors to establish an optimal starting dosing regimen for intravenous oxytocin.MethodsThis retrospective study included pregnant women who underwent labor induction with intravenous oxytocin at Peking University Third Hospital in 2020. A population K‐PD model was developed to depict the time course of uterine contraction frequency (UCF), and covariate screening identified significant factors affecting the pharmacokinetics and pharmacodynamics of oxytocin. Model‐based simulations were used to optimize the current starting regimen based on specific guidelines.ResultsData from 77 pregnant women with 1095 UCF observations were described well by the K‐PD model. Parity, cervical dilation, and membrane integrity are significant factors influencing the effectiveness of oxytocin. Based on the model‐based simulations, the current regimens showed prolonged onset times and high infusion rates. This study proposed a revised approach, beginning with a rapid infusion followed by a reduced infusion rate, enabling most women to achieve the target UCF within approximately 30 min with the lowest possible infusion rate.ConclusionThe K‐PD model of oxytocin effectively described the changes in UCF during labor induction. Furthermore, it revealed that parity, cervical dilation, and membrane integrity are key factors that influence the effectiveness of oxytocin. The optimal starting dosing regimens obtained through model simulations provide valuable clinical references for oxytocin treatment.

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Deriving a prediction model for emergency cesarean delivery following induction of labor in singleton term pregnancies

Cited by 4 publications

References 12 publications

Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women

Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women

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

Optimal starting dosing regimen of intravenous oxytocin for labor induction based on the population kinetic‐pharmacodynamic model of uterine contraction frequency

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