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首页> 外文期刊>BMC Anesthesiology >Development and validation of a predictive risk factor model for epidural re-siting in women undergoing labour epidural analgesia: a retrospective cohort study
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Development and validation of a predictive risk factor model for epidural re-siting in women undergoing labour epidural analgesia: a retrospective cohort study

机译:硬膜外分娩镇痛的妇女硬膜外复位的预测危险因素模型的开发和验证:一项回顾性队列研究

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Epidural catheter re-siting in parturients receiving labour epidural analgesia is distressing to the parturient and places them at increased complications from a repeat procedure. The aim of this study was to develop and validate a clinical risk factor model to predict the incidence of epidural catheter re-siting in labour analgesia. The data from parturients that received labour epidural analgesia in our centre during 2014–2015 was used to develop a predictive model for epidural catheter re-siting during labour analgesia. Multivariate logistic regression analysis was used to identify factors that were predictive of epidural catheter re-siting. The forward, backward and stepwise variable selection methods were applied to build a predictive model, which was internally validated. The final multivariate model was externally validated with the data collected from 10,170 parturients during 2012–2013 in our centre. Ninety-three (0.88%) parturients in 2014–2015 required re-siting of their epidural catheter. The training data set included 7439 paturients in 2014–2015. A higher incidence of breakthrough pain (OR?=?4.42), increasing age (OR?=?1.07), an increased pain score post-epidural catheter insertion (OR?=?1.35) and problems such as inability to obtain cerebrospinal fluid in combined spinal epidural technique (OR?=?2.06) and venous puncture (OR?=?1.70) were found to be significantly predictive of epidural catheter re-siting, while spontaneous onset of labour (OR?=?0.31) was found to be protective. The predictive model was validated internally on a further 3189 paturients from the data of 2014–2015 and externally on 10,170 paturients from the data of 2012–2013. Predictive accuracy of the model based on C-statistic were 0.89 (0.86, 0.93) and 0.92 (0.88, 0.97) for training and internal validation data respectively. Similarly, predictive accuracy in terms of C-statistic was 0.89 (0.86, 0.92) based on 2012–2013 data. Our predictive model of epidural re-siting in parturients receiving labour epidural analgesia could provide timely identification of high-risk paturients required epidural re-siting.
机译:在接受分娩硬膜外镇痛的产妇中,硬膜外导管重新定位使产妇感到痛苦,并使他们因重复手术而增加了并发症。这项研究的目的是开发和验证一种临床危险因素模型,以预测分娩镇痛中硬膜外导管置入的发生率。我们中心2014-2015年接受分娩硬膜外镇痛的产妇的数据用于建立分娩镇痛过程中硬膜外导管置入的预测模型。多因素逻辑回归分析用于确定预测硬膜外导管置入的因素。应用向前,向后和逐步的变量选择方法来构建预测模型,并在内部进行了验证。最终的多变量模型已在我们中心从2012-2013年期间从10,170名产妇收集的数据进行了外部验证。 2014-2015年有93名(0.88%)产妇需要重新安置硬膜外导管。训练数据集包括2014-2015年的7439个Paturients。突破性疼痛的发生率较高(ORα=?4.42),年龄增加(ORα=?1.07),硬膜外导管插入后疼痛评分的增加(ORα=?1.35)以及诸如无法获得脑脊液的问题。结合硬膜外硬膜外技术(OR?=?2.06)和静脉穿刺(OR?=?1.70)可以显着预测硬膜外导管的重新定位,而发现自发性分娩(OR?=?0.31)是保护性的。从2014-2015年的数据中进一步对3189个Paturients进行了内部验证,而从2012-2013年的数据中对10170个Paturients进行了外部验证。对于训练数据和内部验证数据,基于C统计量的模型的预测准确性分别为0.89(0.86,0.93)和0.92(0.88,0.97)。同样,根据2012-2013年数据,基于C统计量的预测准确性为0.89(0.86,0.92)。我们对接受分娩硬膜外镇痛的产妇进行硬膜外置换的预测模型可以及时识别需要硬膜外置换的高危患者。

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