Yi Ling, Huang Wenjing, Liu Qunying, Huang Yimei, Liu Yuxian
Department of Gynecology, Qingyuan City Women and Children Hospital, Guangdong, China.
J Obstet Gynaecol India. 2025 Apr;75(Suppl 1):300-309. doi: 10.1007/s13224-024-01980-y. Epub 2024 Apr 5.
To explore the risk factors for tubal rupture in tubal pregnancy, construct and validate a prediction model for tubal rupture.
Clinical data from 517 patients with tubal pregnancy from January 2020 to December 2022 were collected. The patients were divided into two groups: the tubal rupture group and the unruptured group. The general clinical data of both groups were analyzed using univariate analysis and multivariate logistic regression analysis. Subsequently, a risk prediction model was constructed.
Univariate analysis revealed that amenorrhea duration, maximum diameter of the mass, pregnancy site, serum β-HCG levels, and maximum diameter of pelvic hematocele were identified as potential risk factors for tubal pregnancy rupture. Multivariate logistic regression analysis confirmed these variables, except for the maximum diameter of pelvic hematocele, as independent risk factors for tubal pregnancy rupture. A prediction model for tubal pregnancy rupture was established and validated. The area under the receiver operating characteristic curve was 0.861 for the training set and 0.887 for the validation set, indicating good discriminative ability of the model. The calibration curves of the training set and validation set showed a good fit between the actual values and the predicted values. Moreover, the decision curve analysis suggested that the model had good clinical applicability. To facilitate the use of the nomogram, a web server was developed at https://ep10.shinyapps.io/DynNomapp/.
The prediction model for tubal pregnancy rupture, based on the four predictors: amenorrhea duration, pregnancy site, serum β-HCG levels, and maximum diameter of the mass, demonstrated good predictive efficacy.
探讨输卵管妊娠中输卵管破裂的危险因素，构建并验证输卵管破裂的预测模型。
收集2020年1月至2022年12月期间517例输卵管妊娠患者的临床资料。将患者分为两组：输卵管破裂组和未破裂组。采用单因素分析和多因素logistic回归分析对两组的一般临床资料进行分析。随后，构建风险预测模型。
单因素分析显示，闭经时间、包块最大直径、妊娠部位、血清β-HCG水平和盆腔血肿最大直径被确定为输卵管妊娠破裂的潜在危险因素。多因素logistic回归分析证实，除盆腔血肿最大直径外，这些变量是输卵管妊娠破裂的独立危险因素。建立并验证了输卵管妊娠破裂的预测模型。训练集的受试者工作特征曲线下面积为0.861，验证集为0.887，表明该模型具有良好的判别能力。训练集和验证集的校准曲线显示实际值与预测值之间拟合良好。此外，决策曲线分析表明该模型具有良好的临床适用性。为便于使用列线图，在https://ep10.shinyapps.io/DynNomapp/开发了一个网络服务器。
基于闭经时间、妊娠部位、血清β-HCG水平和包块最大直径这四个预测因素的输卵管妊娠破裂预测模型显示出良好的预测效果。
