Prediction of vaginal birth after cesarean delivery in term gestations: a calculator without race and ethnicity.
Prediction of vaginal birth after cesarean delivery in term gestations: a calculator without race and ethnicity.
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DOI：
10.1016/j.ajog.2021.05.021
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发表时间：
2021-12
影响因子：
9.8
通讯作者：
Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network
中科院分区：
文献类型：
--
作者：
Grobman WA;Sandoval G;Rice MM;Bailit JL;Chauhan SP;Costantine MM;Gyamfi-Bannerman C;Metz TD;Parry S;Rouse DJ;Saade GR;Simhan HN;Thorp JM Jr;Tita ATN;Longo M;Landon MB;Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network
研究人员试图获得工具，可以为临床医生提供一个容易获得的估计剖宫产后阴道分娩（VBAC）的机会，为那些进行剖宫产后分娩试验（TOLAC）。随后外部验证的一种工具来自母胎医学单位（MFMU）剖宫产登记的数据。然而，人们对这一工具包括种族和族裔等社会建构变量表示关注。开发一种准确的工具来预测VBAC，使用妊娠早期容易获得的数据，不包括种族/民族。这是MFMU网络剖宫产登记的二次分析。本分析的方法类似于推导出先前VBAC预测工具的分析。具体来说，如果在分娩和分娩入院时妊娠37 0/7周或之后分娩时有活的单胎头位胎儿，有TOLAC，既往有过一次低位横断面剖宫产史，则纳入本分析。只有在最初的产前检查中确定的信息才会被考虑纳入模型。模型选择和内部验证使用交叉验证程序进行，数据集随机且均匀地分为训练集和测试集。利用训练集识别与VBAC相关的因素，采用逐步倒推法建立logistic回归预测模型。生成最终模型，其中包括所有发现显著的变量（p<0.05）。采用c指数评估模型预测VBAC的准确性。使用独立测试集估计分类误差并验证从训练集开发的模型，并进行校准评估。最后的模型随后应用于总体分析人群。在符合纳入标准的11,687例患者中，有8636例（74%）发生VBAC。后向消除变量选择从训练集中产生一个模型，该模型包括产妇年龄、孕前体重、身高、既往剖宫产指征、产科史和慢性高血压。对于那些身高较高且有过阴道分娩经历的人来说，VBAC的可能性更大，尤其是那些阴道分娩发生在剖宫产之后的人。相反，年龄较大、体重较重、既往剖宫产指征为停止扩张或下降、有慢性高血压药物治疗史的患者发生VBAC的可能性明显较低。该模型在预测概率和经验概率之间有很好的校准，当应用于整体分析人群时，AUC为0.75 (95% CI: 0.74 - 0.77)，这与之前包含种族/民族的模型的AUC（0.75）相似。我们成功地推导了一个准确的模型（可在https://mfmunetwork.bsc.gwu.edu/web/mfmunetwork/vaginal-birth-after-cesarean-calculator上获得），该模型不包括种族或民族，用于估计VBAC概率。开发了一种不包括种族和民族的、经过良好校准的剖宫产后阴道分娩可能性预测模型。
Investigators have attempted to derive tools that could provide clinicians with an easily-obtainable estimate of the chance of vaginal birth after cesarean (VBAC) for those who undertake trial of labor after cesarean (TOLAC). One tool that subsequently was validated externally was derived from data from the Maternal-Fetal Medicine Units (MFMU) Cesarean Registry. Concern has been raised, however, that this tool includes the socially-constructed variables of race and ethnicity. To develop an accurate tool to predict VBAC, using data easily obtainable early in pregnancy, without the inclusion of race/ethnicity. This is a secondary analysis of the Cesarean Registry of the MFMU Network. The approach to the present analysis is similar to that of the analysis in which the prior VBAC prediction tool was derived. Specifically, individuals were included in this analysis if they were delivered on or after 37 0/7 weeks’ gestation with a live singleton cephalic fetus at the time of labor and delivery admission, had a TOLAC, and had history of one prior low-transverse cesarean delivery. Information was only considered for inclusion in the model if it was ascertainable at an initial prenatal visit. Model selection and internal validation were performed using a cross-validation procedure, with the dataset randomly and equally divided into a training set and a test set. The training set was used to identify factors associated with VBAC and build the logistic regression predictive model using stepwise backward elimination. A final model was generated that included all variables found to be significant (p<0.05). The accuracy of the model to predict VBAC was assessed using the c-index. The independent test set was used to estimate classification errors and validate the model that had been developed from the training set, and calibration was assessed. The final model was then applied to the overall analytic population. Of the 11,687 individuals who met inclusion criteria for this secondary analysis, VBAC occurred in 8636 (74%). The backward-elimination variable selection yielded a model from the training set that included maternal age, pre-pregnancy weight, height, indication for prior cesarean, obstetric history, and chronic hypertension. VBAC was significantly more likely for those who were taller and had a prior vaginal birth, particularly if that vaginal birth had occurred after the prior cesarean. Conversely, VBAC was significantly less likely among those whose age was older, whose weight was heavier, whose indication for prior cesarean was arrest of dilation or descent, and who had a history of medication-treated chronic hypertension. The model had excellent calibration between predicted and empirical probabilities and, when applied to the overall analytic population, an AUC of 0.75 (95% CI: 0.74 – 0.77), which is similar to the AUC of the previous model (0.75) that included race/ethnicity. We successfully derived an accurate model (available at https://mfmunetwork.bsc.gwu.edu/web/mfmunetwork/vaginal-birth-after-cesarean-calculator), which did not include race or ethnicity, for estimation of VBAC probability. A well-calibrated prediction model for likelihood of vaginal birth after cesarean that does not include race and ethnicity was developed.
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