Are prediction models for vaginal birth after cesarean accurate?
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2019-05
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BACKGROUND:The use of trial of labor after cesarean delivery calculators in the prediction of successful vaginal birth after cesarean delivery gives physicians an evidence-based tool to assist with patient counseling and risk stratification. Before deployment of prediction models for routine care at an institutional level, it is recommended to test their performance initially in the institution's target population. This allows the institution to understand not only the overall accuracy of the model for the intended population but also to comprehend where the accuracy of the model is most limited when predicting across the range of predictions (calibration). OBJECTIVE:The purpose of this study was to compare 3 models that predict successful vaginal birth after cesarean delivery with the use of a single tertiary referral cohort before continuous model deployment in the electronic medical record. STUDY DESIGN:All cesarean births for failed trial of labor after cesarean delivery and successful vaginal birth after cesarean delivery at an academic health system between May 2013 and March 2016 were reviewed. Women with a history of 1 previous cesarean birth who underwent a trial of labor with a term (≥37 weeks gestation), cephalic, and singleton gestation were included. Women with antepartum intrauterine fetal death or fetal anomalies were excluded. The probability of successful vaginal birth after cesarean delivery was calculated with the use of 3 prediction models: Grobman 2007, Grobman 2009, and Metz 2013 and compared with actual vaginal birth after cesarean delivery success. Each model's performance was measured with the use of concordance indices, Brier scores, and calibration plots. Decision curve analysis identified the range of threshold probabilities for which the best prediction model would be of clinical value. RESULTS:Four hundred four women met the eligibility criteria. The observed rate of successful vaginal birth after cesarean delivery was 75% (305/404). Concordance indices were 0.717 (95% confidence interval, 0.659-0.778), 0.703 (95% confidence interval, 0.647-0.758), and 0.727 (95% confidence interval, 0.669-0.779), respectively. Brier scores were 0.172, 0.205, and 0.179, respectively. Calibration demonstrated that Grobman 2007 and Metz vaginal birth after cesarean delivery models were most accurate when predicted probabilities were >60% and were beneficial for counseling women who did not desire to have vaginal birth after cesarean delivery but had a predicted success rates of 60-90%. The models underpredicted actual probabilities when predicting success at <60%. The Grobman 2007 and Metz vaginal birth after cesarean delivery models provided greatest net benefit between threshold probabilities of 60-90% but did not provide a net benefit with lower predicted probabilities of success compared with a strategy of recommending vaginal birth after cesarean delivery for all women . CONCLUSION:When 3 commonly used vaginal birth after cesarean delivery prediction models are compared in the same population, there are differences in performance that may affect an institution's choice of which model to use.
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Harris, Benjamin S, R Phillips Heine, Jinyoung Park, Keturah R Faurot, Maeve K Hopkins, Andrew J Rivara, Hanna R Kemeny, Chad A Grotegut, et al. (2019). Are prediction models for vaginal birth after cesarean accurate?. American journal of obstetrics and gynecology, 220(5). pp. 492.e1–492.e7. 10.1016/j.ajog.2019.01.232 Retrieved from https://hdl.handle.net/10161/19757.
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![Park](https://scholars.duke.edu/profile-images/thumbnail200/1079262.jpg)
Jinyoung Park
My name is Jinyoung Park and I am currently a second-year Ph.D. student under the supervision of Dr. Rick Hoyle. My area of focus in research encompasses the topics of self-compassion, the notion of common humanity, and the perception of suffering.
![Jelovsek](https://scholars.duke.edu/profile-images/thumbnail200/0047346.jpg)
John E Jelovsek
Dr. Jelovsek is the F. Bayard Carter Distinguished Professor of OBGYN at Duke University and serves as Director of Data Science for Women’s Health. He is Board Certified in OBGYN by the American Board of OBGYN and in Female Pelvic Medicine & Reconstructive Surgery by the American Board of OBGYN and American Board of Urology. He has an active surgical practice in urogynecology based out of Duke Raleigh. He has expertise as a clinician-scientist in developing and evaluating clinical prediction models using traditional biostatistics and machine learning approaches. These “individualized” patient-centered prediction tools aim to improve decision-making regarding the prevention of lower urinary tract symptoms (LUTS) and other pelvic floor disorders after childbirth (PMID:29056536), de novo stress urinary incontinence and other patient-perceived outcomes after pelvic organ prolapse surgery, risk of transfusion during gynecologic surgery, and urinary outcomes after mid-urethral sling surgery (PMID: 26942362). He also has significant expertise in leading trans-disciplinary teams through NIH-funded multi-center research networks and international settings. As alternate-PI for the Cleveland Clinic site in the NICHD Pelvic Floor Disorders Network, he was principal investigator on the CAPABLe trial (PMID: 31320277), one of the largest multi-center trials for fecal incontinence studying anal exercises with biofeedback and loperamide for the treatment of fecal incontinence. He was the principal investigator of the E-OPTIMAL study (PMID: 29677302), describing the long-term follow up sacrospinous ligament fixation compared to uterosacral ligament suspension for apical vaginal prolapse. He was also primary author on research establishing the minimum important clinical difference for commonly used measures of fecal incontinence. Currently, he serves as co-PI in the NIDDK Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01DK097780-05) where he has been involved in studies in the development of Symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index-29 (LURN SI-29) and LURN SI-10 questionnaires for men and women with LUTS. He is also the site-PI for the PREMIER trial (1R01HD105892): Patient-Centered Outcomes of Sacrocolpopexy versus Uterosacral Ligament Suspension for the Treatment of Uterovaginal Prolapse.
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