Liu Guangpu, Zhang Jingya, Zhou Chaofan, Yang Ming, Yang Zhifen, Zhao Ling
The Forth Hospital of Hebei Medical University, Shijiazhuang, China.
Children's Hospital of Hebei Province, Shijiazhuang, China.
Arch Gynecol Obstet. 2024 Aug;310(2):729-737. doi: 10.1007/s00404-024-07524-z. Epub 2024 May 28.
This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram.
The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value.
A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753-0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model.
The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%.
本研究旨在验证Rossi列线图在中国人群中的有效性,然后纳入Bishop评分,以观察其是否会影响列线图的准确性。
应用Rossi预测模型,并在一家中国三级医疗中心2017年8月至2023年7月的回顾性队列中进行外部验证。为了对模型进行修订和更新,重新估计了所有预测因素(种族除外)的回归系数,然后添加了引产时的宫颈Bishop评分。使用受试者操作特征曲线和校准图来衡量每个模型的性能。决策曲线分析确定了每个预测模型具有临床价值的概率阈值范围。
共有721名女性符合纳入标准,其中183名(25.4%)接受了剖宫产。校准显示原始模型存在低估,曲线下面积(AUC)为0.789(95%置信区间[CI]0.753-0.825,p < 0.001)。对原始模型进行重新校准后,鉴别性能从0.789提高到0.803。此外,当将引产时的Bishop评分添加到重新校准的多变量模型中时,更新模型的鉴别能力进一步提高。事实上,更新后的模型显示出良好的校准和鉴别能力,AUC为0.811。决策曲线分析表明,所有模型(原始模型、重新校准模型和更新模型)在概率阈值为0至60%之间时提供了更高的净效益,这表明使用这些模型对处于确定概率阈值范围内的患者进行决策具有益处。更新模型的净效益高于原始模型和重新校准模型。
本研究中,Rossi等人开发的用于预测引产术后剖宫产的列线图在中国人群中得到了验证。更具体地说,并排除种族因素,纳入引产之前的Bishop评分,对中国人群进行调整后,模型性能更佳。当概率阈值在0至60%之间时,这三个模型(原始模型、重新校准模型和更新模型)提供了更高的净效益。