Deriving a Clinical Prediction Tool to Measure the Success of Labour Induction [12OP]

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Abstract

INTRODUCTION:

There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail, requiring emergency cesarean deliveries. This has negative clinical, emotional and financial implications. Our objective was to derive and internally validate a clinical prediction tool to determine the success of IOL.

METHODS:

Data was extracted from electronic medical records of consecutive pregnant women who were induced between April 26, 2016 and December 31, 2016, at Mount Sinai Hospital (Toronto, Canada). A multivariable logistic regression model was developed using variables identified as predictors of successful IOL by literature review and expert opinion. Repeated K-fold cross-validation was used to internally validate the model.

RESULTS:

Of the 916 cases of IOL, 249 (27%) failed. The multivariable logistic regression model found maternal age, parity, pre-pregnancy weight, pre-pregnancy body mass index, weight at delivery and cervical dilation at time of induction as significant predictors of successful IOL. The prediction tool was well calibrated (Hosmer-Lemeshow χ2=6.42, P=.60) and demonstrated good discriminatory ability (area under the receiver-operating characteristic curve [AUROC], 0.79 [95% CI 0.76–0.82]). Internal validation of the model showed a similar discriminatory ability (AUROC, 0.77 [95% CI 0.68–0.85).

CONCLUSION:

We have derived and internally validated a clinical prediction tool for IOL in a large and diverse population. Once prospectively validated in other settings, this has potential for widespread use in clinical practice and research, as well as for enhancing patient experience and allocation of healthcare resources.

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