• Valery A Danilack, Jennifer A Hutcheon, Elizabeth W Triche, David D Dore, Janet H Muri, Maureen G Phipps, and David A Savitz.
• Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island.
• J Womens Health (Larchmt). 2020 May 1; 29 (5): 656-669.

AbstractObjective: The goal of the study was to develop and validate a prediction model for cesarean delivery after labor induction that included factors known before the start of induction, unlike prior studies that focused on characteristics at the time of induction. Materials and Methods: Using 17,370 term labor inductions without documented medical indications occurring at 14 U.S. hospitals, 2007-2012, we created and evaluated a model predicting cesarean delivery. We assessed model calibration and discrimination, and we used bootstrapping for internal validation. We externally validated the model by using 2122 labor inductions from a hospital not included in the development cohort. Results: The model contained eight variables-gestational age, maternal race, parity, maternal age, obesity, fibroids, excessive fetal growth, and history of herpes-and was well calibrated with good risk stratification at the extremes of predicted probability. The model had an area under the curve (AUC) for the receiver operating characteristic curve of 0.82 (95% confidence interval 0.81-0.83), and it performed well on internal validation. The AUC in the external validation cohort was 0.82. Conclusion: This prediction model can help providers estimate a woman's risk of cesarean delivery when planning a labor induction.

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