Previous models for prediction of vaginal birth after cesarean (VBAC) relied on race and ethnicity, raising concern for bias. In response, the Maternal-Fetal Medicine Units Network (MFMU) created a new prediction model without race and ethnicity for individuals with one prior cesarean delivery. We performed a secondary analysis of the MFMU Cesarean Registry database to evaluate whether the MFMU VBAC prediction model without race and ethnicity could accurately predict VBAC for individuals with two prior cesarean deliveries. Overall, 353 individuals were included and 252 (71%) had VBAC. An area under the curve for the receiver operating curve of 0.74 (95% CI, 0.69-0.80) was reported for the predicted probabilities for VBAC, indicating that the model can be used for prediction of VBAC in this population.

中文翻译：

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在两次剖腹产的个体中验证剖腹产后阴道分娩的预测模型，不考虑种族和民族。


以前预测剖腹产后阴道分娩 (VBAC) 的模型依赖于种族和民族，引起了对偏见的担忧。作为回应，母胎医学单位网络 (MFMU) 针对曾进行过剖腹产的个体创建了一种不考虑种族和民族的新预测模型。我们对 MFMU 剖腹产登记数据库进行了二次分析，以评估不考虑种族和民族的 MFMU VBAC 预测模型是否可以准确预测有两次剖腹产史的个体的 VBAC。总体而言，共纳入 353 人，其中 252 人 (71%) 患有 VBAC。据报告，VBAC 的预测概率的受试者工作曲线下面积为 0.74（95% CI，0.69-0.80），表明该模型可用于预测该人群中的 VBAC。