G A Macones1, N Hausman, R Edelstein, D M Stamilio, S J Marder. 1. Department of Obstetrics and Gynecology, the Center for Clinical Epidemiology and Biostatistics, and the Leonard Davis Institute for Health Economics, University of Pennsylvania School of Medicine, Philadelphia, USA.
Abstract
OBJECTIVE: Our aim was to assess the utility and effectiveness of a neural network for predicting the likelihood of success of a trial of labor, relative to standard multivariate predictive models. STUDY DESIGN: We identified 100 failed trials of labor and 300 successful trials of labor in women with a prior cesarean delivery performed at our institution. Information was collected on >70 potential predictors of labor outcomes from the medical records, including demographic, historical, and past obstetric information, as well as information from the index pregnancy. Bivariate analyses comparing women in whom a trial of labor failed with those whose trial succeeded were performed. These initial analyses were used to select variables for inclusion into our muitivariate predictive model. From the same data we trained and tested a neural network, using a back-propagation algorithm. The test characteristics of the multivariate predictive model and the neural network were compared. RESULTS: From the bivariate analysis a history of substance abuse (adjusted odds ratio, 0.27; 95% confidence interval, 0.09-0.80), a successful prior vaginal birth after cesarean delivery (adjusted odds ratio, 0.13; 95% confidence interval, 0.05-0.31), cervical dilatation at admission (adjusted odds ratio, 0.53; 95% confidence interval, 0.31-0.88), and the need for labor augmentation (adjusted odds ratio, 2.15; 95% confidence interval, 1.14-4.06) were ultimately discovered to be important in predicting the likelihood of the success or failure of a trial of labor. With these variables in the predictive model the sensitivity of the derived rule for predicting failure was 77%, the specificity was 65%, and the overall accuracy was 69%. We also built a network using the 4 variables that were included in the final multivariate model. We were unable to achieve the same degree of sensitivity and specificity that we observed with the regression-based predictive model (sensitivity and specificity, 59% and 44%). CONCLUSION: In this study a standard multivariate model was better able to predict outcome in women ttempting a trial of labor.
OBJECTIVE: Our aim was to assess the utility and effectiveness of a neural network for predicting the likelihood of success of a trial of labor, relative to standard multivariate predictive models. STUDY DESIGN: We identified 100 failed trials of labor and 300 successful trials of labor in women with a prior cesarean delivery performed at our institution. Information was collected on >70 potential predictors of labor outcomes from the medical records, including demographic, historical, and past obstetric information, as well as information from the index pregnancy. Bivariate analyses comparing women in whom a trial of labor failed with those whose trial succeeded were performed. These initial analyses were used to select variables for inclusion into our muitivariate predictive model. From the same data we trained and tested a neural network, using a back-propagation algorithm. The test characteristics of the multivariate predictive model and the neural network were compared. RESULTS: From the bivariate analysis a history of substance abuse (adjusted odds ratio, 0.27; 95% confidence interval, 0.09-0.80), a successful prior vaginal birth after cesarean delivery (adjusted odds ratio, 0.13; 95% confidence interval, 0.05-0.31), cervical dilatation at admission (adjusted odds ratio, 0.53; 95% confidence interval, 0.31-0.88), and the need for labor augmentation (adjusted odds ratio, 2.15; 95% confidence interval, 1.14-4.06) were ultimately discovered to be important in predicting the likelihood of the success or failure of a trial of labor. With these variables in the predictive model the sensitivity of the derived rule for predicting failure was 77%, the specificity was 65%, and the overall accuracy was 69%. We also built a network using the 4 variables that were included in the final multivariate model. We were unable to achieve the same degree of sensitivity and specificity that we observed with the regression-based predictive model (sensitivity and specificity, 59% and 44%). CONCLUSION: In this study a standard multivariate model was better able to predict outcome in women ttempting a trial of labor.
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
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