Analysis of risk factors for recurrent spontaneous abortion and construction of prediction model / 新乡医学院学报

ABSTRACT

Objective To analyze the risk factors of recurrent spontaneous abortion(RSA),construct a prediction model based on the risk factors,and evaluate its efficacy in predicting the occurrence of RSA.Methods A total of 324 pregnant women who were registered in the Department of Reproductive Medicine of the Third Affiliated Hospital of Xinxiang Medical University from January 2019 to October 2022 were selected as the research subjects,and they were divided into normal group(n=234)and abortion group(n=90)according to the presence or absence of RSA.Univariate and multivariate logistic regression were used to analyze the risk factors of RSA,a prediction model was constructed based on the selected risk factors,and predictive efficiency of the model was evaluated by drawing the receiver operating characteristic(ROC)curve.Results Univariate analysis showed that there were significant differences in 25-hydroxyvitamin D[25-(OH)D],progesterone(P),homocysteine(Hey),fibrinogen(FIB),D-dimer(D-D),thyroid-stimulating hormone(TSH),thyroglobulin antibody(TGAb),thyroid peroxidase antibody(TPOAb),pulse index(PI),resistance index(RI)and ratio of peak systolic velocity to lowest diastolic velocity(S/D)between the two groups(P＜0.05);there were no significant differences in age,pre-pregnancy body mass index,pre-pregnancy menstrual cycle,gestational sac size,free triiodothyronine(FT3)and free thyroxine(F4)between the two groups(P＞0.05).Multivariate logistic regression analysis showed that low 25-(OH)D and P,as well as high Hey,TPOAb,PI,RI and S/D,were risk factors for RSA in pregnant women(P＜0.05).The ROC curve analysis results showed that the sensitivity,accuracy,positive predictive value and area under the curve of the constructed model in predicting the occurrence of RSA were 92.60％,90.25％,89.26％and 0.914,respectively.Conclusion Decreased 25-(OH)D and P as well as increased Hcy,TPOAb,PI,RI and S/D are risk factors for RSA,and the constructed model based on these risk factors has high efficiency in predicting the occurrence of RSA.