Construction and Verification of Nomogram Model for Predicting the Risk of Caesarean Scar Pregnancy
10.11969/j.issn.1673-548X.2024.05.013
- VernacularTitle:预测剖宫产瘢痕妊娠发生风险列线图模型的建立与验证
- Author:
Xuzhen ZHAO
1
;
Xinyan XU
;
Xiangnan ZHANG
Author Information
1. 830000 乌鲁木齐,新疆医科大学公共卫生学院
- Keywords:
Caesarean scar pregnancy;
Influencing factors;
Nomogram model
- From:
Journal of Medical Research
2024;53(5):58-62,68
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and validate the risk prediction model for the occurrence of caesarean scar pregnancy(CSP)in women with re-pregnancy after cesarean section.Methods A total of 663 women with re-pregnancy after cesarean section in Urumqi Maternal and Child Health Hospital from 2018 to 2022 were collected,and randomly divided the training set(n=460)and the test set(n=203)according to 7∶3,the cases of the training set were divided into the CSP group(n=239)and the non-CSP group(n=221),and the risk factors for the occurrence of CSP were evaluated by univariate and multivariate Logistic regression analysis.Based on the a-bove results,a nomogram model was constructed,validated and evaluated in the test set and the training set,respectively.The predictive efficacy of the model was evaluated by area under the curve(AUC)of receiver operating characteristic(ROC)and the Hosmer-Leme-show test,and the clinical application value of the model was evaluated by clinical decision curve analysis(DCA).Results The results of multivariate Logistic regression analysis showed that the number of cesarean section＞1,posterior uterine position,the number of mis-carriages＞1,CSD,the history of miscarriage between the current pregnancy and the previous cesarean section were the risk factors for the occurrence of CSP(P＜0.05),and the timing of cesarean section was the protective factor for the occurrence of CSP in the course of labor(P＜0.05).Based on the above results,the nomogram prediction model was constructed,the AUC of the model in the training set was 0.813(95％CI:0.773-0.852),and the AUC of the model in the test set was 0.817(95％CI:0.755-0.878).Hosmer-Lemeshow goodness-of-fit test for the training set and the test set model was well fitted(x2=7.647,P=0.469;x=6.162,P=0.629).The calibration curve showed that the model had good consistency in predicting the occurrence of CSP in re-pregnancy after cesarean section,and the DCA curve showed that the model had high clinical efficacy in both the training set and the test set.Conclusion The prediction model constructed in this study can effectively predict the occurrence of CSP,which can provide references for early identification and pre-ventive treatment for high-risk populations.
