Valery A Danilack1,2,3, Jennifer A Hutcheon4, Elizabeth W Triche5, David D Dore6,7, Janet H Muri8, Maureen G Phipps1,2,3, David A Savitz1,3,9. 1. Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island. 2. Division of Research, Women & Infants Hospital, Providence, Rhode Island. 3. Department of Obstetrics and Gynecology, Warren Alpert Medical School of Brown University, Providence, Rhode Island. 4. Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, Canada. 5. Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut. 6. United Health Group, Health Services Research, Boston, Massachusetts. 7. Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island. 8. National Perinatal Information Center, Inc., Providence, Rhode Island. 9. Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
Abstract
Objective: 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.
Objective: 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.
Authors: Francis P J M Vrouenraets; Frans J M E Roumen; Cary J G Dehing; Eline S A van den Akker; Maureen J B Aarts; Esther J T Scheve Journal: Obstet Gynecol Date: 2005-04 Impact factor: 7.661
Authors: Anjel Vahratian; Jun Zhang; James F Troendle; Anthony C Sciscione; Matthew K Hoffman Journal: Obstet Gynecol Date: 2005-04 Impact factor: 7.661
Authors: Rebecca F Hamm; Jennifer McCoy; Amal Oladuja; Hilary R Bogner; Michal A Elovitz; Knashawn H Morales; Sindhu K Srinivas; Lisa D Levine Journal: JAMA Netw Open Date: 2020-11-02