PDF(Pubmed)
Abstract：
UNASSIGNED: Cervical insufficiency can lead to preterm birth and neonatal mortality. Emergency cervical cerclage is a surgical intervention aimed at preventing preterm birth in patients with cervical insufficiency. However, some patients may experience cerclage failure. This study aimed to identify the risk factors associated with cerclage failure and develop a predictive nomogram model for patients with cervical insufficiency undergoing emergency cervical cerclage.
UNASSIGNED: Data of 200 patients who underwent emergency cervical cerclage for cervical insufficiency were retrospectively analyzed. Patients were categorized into successful and failed groups based on their ability to take the infant home. Univariate and multivariate logistic regression analyses were performed to identify risk factors for cerclage failure. A nomogram model was developed based on multivariate logistic regression results, and its performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA).
UNASSIGNED: Univariate logistic regression analysis identified 11 potential risk factors for cerclage failure, including the presence of polycystic ovary syndrome (PCOS), vaginitis, cervical dilation, preoperative C-reactive protein, routine vaginal lavage after cervical cerclage, delivery, gestational age, extended days, chorioamnionitis, intrauterine infection, cervical laceration, and premature rupture of membranes. Multivariate logistic regression analysis revealed that PCOS, cervical dilation after cervical cerclage were independent risk factors for cerclage failure while routine vaginal lavage was a protective factor against failure. The nomogram predictive model demonstrated an area under the curve value of 0.975, indicating excellent discriminatory ability. The calibration plot showed good consistency between the nomogram predictions and actual observations. DCA demonstrated the strong clinical applicability of the nomogram.
UNASSIGNED: This study successfully identified risk factors associated with emergency cervical cerclage failure in patients with cervical insufficiency and developed a predictive nomogram model. This model can assist clinicians in making informed decisions and accurately predicting the risk of cerclage failure in these patients.
UNASSIGNED: Data of 200 patients who underwent emergency cervical cerclage for cervical insufficiency were retrospectively analyzed. Patients were categorized into successful and failed groups based on their ability to take the infant home. Univariate and multivariate logistic regression analyses were performed to identify risk factors for cerclage failure. A nomogram model was developed based on multivariate logistic regression results, and its performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA).
UNASSIGNED: Univariate logistic regression analysis identified 11 potential risk factors for cerclage failure, including the presence of polycystic ovary syndrome (PCOS), vaginitis, cervical dilation, preoperative C-reactive protein, routine vaginal lavage after cervical cerclage, delivery, gestational age, extended days, chorioamnionitis, intrauterine infection, cervical laceration, and premature rupture of membranes. Multivariate logistic regression analysis revealed that PCOS, cervical dilation after cervical cerclage were independent risk factors for cerclage failure while routine vaginal lavage was a protective factor against failure. The nomogram predictive model demonstrated an area under the curve value of 0.975, indicating excellent discriminatory ability. The calibration plot showed good consistency between the nomogram predictions and actual observations. DCA demonstrated the strong clinical applicability of the nomogram.
UNASSIGNED: This study successfully identified risk factors associated with emergency cervical cerclage failure in patients with cervical insufficiency and developed a predictive nomogram model. This model can assist clinicians in making informed decisions and accurately predicting the risk of cerclage failure in these patients.
摘要：
■宫颈机能不全可导致早产和新生儿死亡。紧急宫颈环扎术是一种旨在预防宫颈机能不全患者早产的外科手术。然而,一些患者可能会出现环扎失败。本研究旨在确定与环扎失败相关的危险因素，并建立宫颈机能不全患者行紧急宫颈环扎的预测列线图模型。
■对200例因宫颈机能不全而行紧急宫颈环扎术的患者资料进行回顾性分析。根据将婴儿带回家的能力，将患者分为成功组和失败组。进行单因素和多因素logistic回归分析以确定环扎失败的危险因素。基于多变量逻辑回归结果建立了列线图模型，并使用接收器工作特性曲线评估其性能，校准图,和决策曲线分析(DCA)。
■单变量逻辑回归分析确定了环扎失败的11个潜在危险因素，包括多囊卵巢综合征（PCOS）的存在，阴道炎,宫颈扩张,术前C反应蛋白,宫颈环扎术后常规阴道灌洗，delivery,胎龄,延长的天数，绒毛膜羊膜炎,宫内感染,宫颈裂伤,胎膜早破.多因素logistic回归分析显示，PCOS，宫颈环扎术后宫颈扩张是环扎失败的独立危险因素，而常规阴道灌洗是预防失败的保护因素。列线图预测模型显示曲线下面积值为0.975，表明具有出色的判别能力。校准图显示了列线图预测和实际观察之间的良好一致性。DCA显示了列线图的强大临床适用性。
■这项研究成功地确定了与宫颈机能不全患者的紧急宫颈环扎失败相关的危险因素，并开发了预测性列线图模型。该模型可以帮助临床医生做出明智的决定，并准确预测这些患者环扎失败的风险。
■对200例因宫颈机能不全而行紧急宫颈环扎术的患者资料进行回顾性分析。根据将婴儿带回家的能力，将患者分为成功组和失败组。进行单因素和多因素logistic回归分析以确定环扎失败的危险因素。基于多变量逻辑回归结果建立了列线图模型，并使用接收器工作特性曲线评估其性能，校准图,和决策曲线分析(DCA)。
■单变量逻辑回归分析确定了环扎失败的11个潜在危险因素，包括多囊卵巢综合征（PCOS）的存在，阴道炎,宫颈扩张,术前C反应蛋白,宫颈环扎术后常规阴道灌洗，delivery,胎龄,延长的天数，绒毛膜羊膜炎,宫内感染,宫颈裂伤,胎膜早破.多因素logistic回归分析显示，PCOS，宫颈环扎术后宫颈扩张是环扎失败的独立危险因素，而常规阴道灌洗是预防失败的保护因素。列线图预测模型显示曲线下面积值为0.975，表明具有出色的判别能力。校准图显示了列线图预测和实际观察之间的良好一致性。DCA显示了列线图的强大临床适用性。
■这项研究成功地确定了与宫颈机能不全患者的紧急宫颈环扎失败相关的危险因素，并开发了预测性列线图模型。该模型可以帮助临床医生做出明智的决定，并准确预测这些患者环扎失败的风险。
