Abstract
TREE ANALYSIS DAVID STAMILIO, ALISON CAHILL, ANTHONY ODIBO, WILLIAM SHANNON, JEFFREY PEIPERT, GEORGE MACONES, Washington University in St. Louis, St. Louis, Missouri OBJECTIVE: To predict uterine rupture in vaginal birth after cesarean (VBAC) patients using classification & regression tree (CART) analysis. Prior attempts to predict uterine rupture in VBAC using classic logistic regression modelling have been unsuccessful. The CART method can assess more complex risk factor relationships and provide more accurate & practical prediction rules by progressively splitting patients into intuitive risk factor subgroups. STUDY DESIGN: A multi-center retrospective cohort study of pregnant women with at least one prior cesarean between 1995 & 2000. This secondary analysis includes only patients that attempted VBAC. The primary outcome is symptomatic uterine rupture. We used univariate & multivariable methods to identify over 20 candidate risk factors and CART analysis to develop a multivariable prediction model for uterine rupture. RESULTS: Of 13,705 VBAC attempts, 128 cases (0.9%) of uterine rupture occurred. CART analysis identified 5 dominant risk factors applied in 5 specific pathways (or scenarios) to predict uterine rupture (see table). Only scenario 1 had a uterine rupture rate appreciably higher than baseline risk.
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