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- W2081944360 abstract "To construct a predictive nomogram for VBAC success that combines factors known at admission for delivery with those known at initiation of prenatal care (PNC), and to determine if this model is more predictive than a validated model (abstract 424, SMFM 2006) that uses only factors known at initiation of PNC. All women with one prior LTCS and a singleton who underwent a TOL at term were identified from 1999-2002 in a multicenter study. Using multivariable modeling, we constructed a predictive nomogram for VBAC success that included patient factors known at admission for delivery as well as since the initiation of PNC.The c-statistic was used to compare predictive ability of this nomogram with that of a nomogram based only on factors available at a first prenatal visit (PNV). 6833 women were available for the logistic regression analysis; the regression equation for VBAC success had multiple factors (table) and a c-statistic of 0.78. The c-statistic for this nomogram was significantly greater (P < .001) than that of the predictive nomogram (c-statistic = 0.75) that included factors only available at the first PNV. A prediction model for VBAC success that incorporates factors known at admission for delivery adds to the predictive accuracy of a model that uses factors available only at a first PNV." @default.
- W2081944360 created "2016-06-24" @default.
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- W2081944360 date "2006-12-01" @default.
- W2081944360 modified "2023-10-18" @default.
- W2081944360 title "The MFMU cesarean registry: Does information available on admission for delivery improve the accuracy of a prediction model for VBAC success?" @default.
- W2081944360 doi "https://doi.org/10.1016/j.ajog.2006.10.722" @default.
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