预测剖宫产术后尝试经阴道分娩的产妇分娩试验结果：多元方法与神经网络的比较
Predicting outcomes of trials of labor in women attempting vaginal birth after cesarean delivery: a comparison of multivariate methods with neural networks.
作者信息
Macones G A, Hausman N, Edelstein R, Stamilio D M, Marder S J
机构信息
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.
出版信息
Am J Obstet Gynecol. 2001 Feb;184(3):409-13. doi: 10.1067/mob.2001.109386.
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.
目的
我们的目的是评估相对于标准多变量预测模型，神经网络在预测引产成功可能性方面的效用和有效性。
研究设计
我们在本机构对有剖宫产史的女性中确定了100例引产失败病例和300例引产成功病例。从病历中收集了超过70个可能的分娩结局预测因素的信息，包括人口统计学、病史和既往产科信息，以及本次妊娠的信息。对引产失败的女性与引产成功的女性进行了双变量分析。这些初始分析用于选择纳入我们多变量预测模型的变量。利用相同的数据，我们使用反向传播算法训练和测试了一个神经网络。比较了多变量预测模型和神经网络的测试特征。
结果
从双变量分析中发现，药物滥用史（调整比值比，0.27；95%置信区间，0.09 - 0.80）、既往剖宫产术后成功经阴道分娩（调整比值比，0.13；95%置信区间，0.05 - 0.31）、入院时宫颈扩张情况（调整比值比，0.53；95%置信区间，0.31 - 0.88）以及引产需要加强宫缩（调整比值比，2.15；95%置信区间，1.14 - 4.06）最终被发现对预测引产成功或失败的可能性很重要。在预测模型中纳入这些变量后，预测失败的推导规则的敏感性为77%，特异性为65%，总体准确率为69%。我们还使用最终多变量模型中包含的4个变量构建了一个网络。我们无法达到基于回归的预测模型所观察到的相同程度的敏感性和特异性（敏感性和特异性分别为59%和44%）。
结论
在本研究中，标准多变量模型在预测尝试引产的女性结局方面表现更佳。
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