He Chunna, Zheng Fengque, Lin Jiajing, Chen Saiqiong, Yang Weiwei, Huang Qinxi, Qin Huayi, Wei Jiahan, Li Jingjing
Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
Department of Reproductive Center, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
J Obstet Gynaecol. 2023 Dec;43(1):2142767. doi: 10.1080/01443615.2022.2142767. Epub 2022 Nov 10.
The aim of this study was to identify the high-risk factors for caesarean scar pregnancy (CSP) and establish a nomogram to predict the risk of caesarean scar pregnancy in pregnant women with a history of caesarean section. Among 1273 pregnant women with a history of caesarean section, 70% of the patients (892 patients, training sample) were randomly selected for analysis, and a prediction model was generated. The remaining patients (381 patients, validation sample) were validated for the model. Four high-risk factors for CSP were established, including: parity, number of previous abortions, uterus position, and early vaginal bleeding. The area under the curve of the nomogram for the training set was 0.867 and that for the validation set was 0.881, indicating good performance. Calibration curves for predicting CSP showed good calibrations. Decision curve analyses showed good application prospects for the model. Our results show that our nomogram for predicting CSP risks can be a practical tool to help in the early identification of CSP.Impact Statement The high-risk factors for "caesarean scar pregnancy", An simple nomogram could be constructed to predict the risk of the disease through these high-risk factors. This study can quickly predict whether the patient is a high-risk group for uterine scar pregnancy based on the patient's previous pregnancy, early vaginal bleeding and uterine position. Caesarean scar pregnancy was secondary Long-term complications after caesarean section that with a high risk of pregnancy. In this study, we established a nomogram based on the number of cases of CSP and a control group with a history of caesarean section delivery at term, The high-risk factors were assigned a certain risk value in the early stage, if the woman contains more high-risk factors, the higher the risk of developing CSP, it should be highly valued in the early stage, and the rate of visiting a doctor should be increased.
本研究的目的是确定剖宫产瘢痕妊娠（CSP）的高危因素，并建立一种列线图来预测有剖宫产史孕妇发生剖宫产瘢痕妊娠的风险。在1273例有剖宫产史的孕妇中，随机选取70%的患者（892例患者，训练样本）进行分析，并生成预测模型。其余患者（381例患者，验证样本）对该模型进行验证。确定了4个CSP的高危因素，包括：产次、既往流产次数、子宫位置和早期阴道出血。训练集列线图的曲线下面积为0.867，验证集为0.881，表明性能良好。预测CSP的校准曲线显示校准良好。决策曲线分析表明该模型具有良好的应用前景。我们的结果表明，我们用于预测CSP风险的列线图可以作为一种实用工具，有助于早期识别CSP。影响声明 “剖宫产瘢痕妊娠” 的高危因素，可通过这些高危因素构建一个简单的列线图来预测疾病风险。本研究可以根据患者既往妊娠情况、早期阴道出血情况和子宫位置，快速预测患者是否为子宫瘢痕妊娠的高危人群。剖宫产瘢痕妊娠是剖宫产术后的继发性远期并发症，妊娠风险高。在本研究中，我们基于CSP病例数和足月剖宫产史的对照组建立了列线图，在早期将高危因素赋予一定的风险值，如果女性包含的高危因素越多，发生CSP的风险越高，应在早期高度重视，并提高就诊率。
