Derivation and validation of a model predicting the likelihood of vaginal birth following labour induction

Abstract

Background There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail to result in vaginal births, requiring cesarean deliveries. This has negative clinical, emotional and resource implications. The importance of predicting the success of labour induction to enable shared decision-making has been recognized, but existing models are limited in scope and generalizability. Our objective was to derive and internally validate a clinical prediction model that uses variables readily accessible through maternal demographic data, antenatal history, and cervical examination to predict the likelihood of vaginal birth following IOL. Methods Data was extracted from electronic medical records of consecutive pregnant women who were induced between April and December 2016, at Mount Sinai Hospital, Toronto, Canada. A multivariable logistic regression model was developed using 16 readily accessible variables identified through literature review and expert opinion, as predictors of vaginal birth after IOL. The final model was internally validated using 10-fold cross-validation. Results Of the 1123 cases of IOL, 290 (25.8%) resulted in a cesarean delivery. The multivariable logistic regression model found maternal age, parity, pre-pregnancy body mass index and weight, weight at delivery, and cervical dilation at time of induction as significant predictors of vaginal delivery following IOL. The prediction model was well calibrated (Hosmer-Lemeshow χ2 = 5.02, p = 0.76) and demonstrated good discriminatory ability (area under the receiver operating characteristic (AUROC) curve, 0.81 (95% CI 0.78 to 0.83)). Finally, the model showed good internal validity [AUROC 0.77 (95% CI 0.73 to 0.82)]. Conclusions We have derived and internally validated a well-performing clinical prediction model for IOL in a large and diverse population using variables readily accessible through maternal demographic data, antenatal history, and cervical examination. Once prospectively validated in diverse settings, and if shown to be acceptable to pregnant women and healthcare providers as well as clinically and cost-effective, this model has potential for widespread use in clinical practice and research for enhancing patient autonomy, improving induction outcomes, and optimizing allocation of resources. Electronic supplementary material The online version of this article (10.1186/s12884-019-2232-8) contains supplementary material, which is available to authorized users.

Inpatient versus outpatient induction of labour: a systematic review and meta-analysis

“…In addition, three trials investigated patient reported outcomes such as satisfaction and comfort. Fifth, the included studies, used a one-size-fits-all approach to labour induction, comparing a predetermined method in each arm, regardless of the cervical status, uterine activity, parity and other patient characteristics that are could influence the effectiveness and safety of labour induction [25][26][27]. Ideally, the method of choice for cervical ripening or labour induction should consider these parameters, and individualize care, to ensure the highest possible chance of a vaginal birth, with the lowest risk of adverse events.…”

Development And Validation Of Models To Predict Cesarean Delivery Among Low-Risk Nulliparous Women At Term: A Retrospective Study In China

“…The candidate predictor variables had to be easily accessible through characteristics' data. To identify predictor variables, a systematic review of the literature was conducted [8][9][10][11][12][14][15][16]. The following variables were recorded: maternal age, height, weight, baseline body mass index (BMI), weight change during pregnancy, gestational age at delivery, premature rupture of membranes (PROM), epidural analgesia, meconium-stained amniotic uid, induction methods(oxytocin, amniotomy, disposable cervical dilator balloon, prostaglandin(Propess or Misoprostol)), neonatal sex, and neonatal birth weight.…”

“…Currently, several published models studied on prediction of cesarean delivery were mostly focused on the white populations [8][9][10][11][12], whereas the data of the majority of Han ethnic backgrounds of the Chinese population are insu cient. To date, China lacks the relevant researches, and there is limited empirical evidence and clinical experience reported.…”

Development and validation of models to predict cesarean delivery among low-risk nulliparous women at term: a retrospective study in China

“…Sometimes spontaneous vaginal delivery doesn't go so well, resulting in pregnancies have to undergo the emergency operation. And yet, maternal and perinatal complications are higher when a failed trial of labor results in an emergency cesarean delivery [6][7][8].…”

“…Currently, several published models studied on prediction of cesarean delivery were mostly focused on the white populations [7,[11][12][13][14], whereas the data of the majority of Han ethnic backgrounds of the Chinese population are insu cient. To date, China lacks the relevant researches, and there is limited empirical evidence and clinical experience reported.…”