New Predictive Model For Vaginal Birth After Cesarean

A clear objective of this study has been demonstrated in the paper: To construct a new predictive model for vaginal birth after cesarean (VBAC), which incorporates the factors that can only be obtained as the pregnancy progresses and compare the new model with a previous model that only have variables available at the first prenatal visit. In this study, authors stated that in a previous model is limited to the variables which are available at the first prenatal visit, and they want to know whether incorporating pregnant woman’s most recent circumstances can refine the predicted probability of VBAC. Model and Variables: Logistic model has been use in this study. In previous model, only variables at first prenatal visit have been used. In this study, Grobman et. al developed the model by adding the variables that can only be obtained as the pregnancy progresses. Cross-validation method was used to evaluate the predictive power of models. Dataset was equally divided into two parts. Models were fitted on training set and tested on testing set. Results The significant factors in the model were presented in table 1. In Fig 1 and 2, calibration curves (empirical probabilities vs predicted probabilities) were presented. The predicted probabilities are the mid-points of five equal intervals (0%-20%, 21%-40%, ..., etc). The empirical probabilities were calculated by average all women in the same interval. The closer to the 45-degree line, the better the model is. Figure 1Show MoreRelatedNursing Essay41677 Words   |  167 PagesEducation and Development Oregon Health and Science University School of Nursing, Portland Troyen A. Brennan, Executive Vice President, Chief Medical Officer, CVS Caremark, Woonsocket, RI Robert E. Campbell, Vice Chairman (Retired), Johnson Johnson, New Brunswick, NJ Leah Devlin, Professor of the Practice, University of North Carolina School of Public Health, Raleigh Catherine Dower, Associate Director of Research, Center for the Health Professions, University of California, San Francisco Rosa Gonzalez-Guarda