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

Iolanda Ferreira1,2, Joana Simões3, João Correia3

  • 1Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal.

Acta Obstetricia Et Gynecologica Scandinavica
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

A new model predicts vaginal delivery after labor induction with 79.4% accuracy (AUROC). This tool helps assess cesarean section risk and personalize counseling for expectant mothers undergoing induction of labor.

Keywords:
cesarean sectioninduction of labormachine learningmode of deliverypredictive modelsvaginal delivery

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Area of Science:

  • Obstetrics and Gynecology
  • Computational Medicine
  • Reproductive Health

Background:

  • Induction of labor (IOL) rates are increasing globally, particularly in high-income countries with higher rates of maternal comorbidities.
  • This trend raises concerns about a potential rise in cesarean section (CS) rates following IOL.
  • Improved counseling on delivery mode is crucial for pregnant women undergoing labor induction.

Purpose of the Study:

  • To develop a computational learning-based prognostic model for predicting vaginal delivery after labor induction.
  • To create a secondary model predicting CS due to labor dystocia or abnormal fetal heart rate.
  • To evaluate the feature importance of maternal clinical predictors for these outcomes.

Main Methods:

  • A prognostic model was developed using computational learning on data from 2434 singleton term pregnancies undergoing IOL.
  • Internal validation used 10-fold cross-validation, and external validation utilized an independent dataset.
  • SHAP values were employed to determine the importance of influential maternal clinical features.

Main Results:

  • The vaginal delivery prediction model achieved an AUROC of 0.794 in independent validation, with high specificity (0.910) and sensitivity (0.766).
  • Key predictors for vaginal delivery included Bishop score, previous term deliveries, maternal height, interpregnancy interval, and prior eutocic delivery.
  • The CS prediction model showed an AUROC of 0.590 with high specificity (0.893).

Conclusions:

  • The developed prognostic model demonstrates strong predictive capability for vaginal delivery after labor induction (AUROC=0.794).
  • Identifying key influencing features allows for better risk assessment of CS.
  • This tool can enhance personalized counseling for patients regarding their delivery mode after induction of labor.