Clinical pregnancy outcomes prediction in vitro fertilization women based on random forest prediction model: A nested case-control study.

Abstract

The present study aimed to analyze the risk factors influencing the in vitro fertilization embryo transfer (IVF-ET) pregnancy and to construct a prediction model for clinical pregnancy outcome in patients receiving IVF-ET based on the predictors. In this nested case-control study, the data of 369 women receiving IVF-ET were enrolled. Univariate and multivariate Logistic regression analyses were conducted to identify the potential predictors. Ten-fold cross validation method was used to validate the random forest model for predicting the clinical pregnancy. The receiver operating characteristic curve was drawn to evaluate the prediction ability of the model. The importance of variables was shown according to Mean Decrease Gini. The data delineated that age (odds ratio [OR]= 1.093, 95% confidence interval [CI]: 1.036-1.156, P = .0010), body mass index (BMI) (OR = 1.094, 95%CI: 1.021-1.176, P = .012), 3 cycles (OR = 0.144, 95%CI: 0.028-0.534, P = .008), hematocrit (HCT) (OR = 0.865, 95% CI: 0.791-0.943, P = .001), luteinizing hormone (LH) (OR = 0.678, 95%CI: 0.549-0.823, P < .001), progesterone (P) (OR = 2.126, 95%CI: 1.112-4.141, P = .024), endometrial thickness (OR = 0.132, 95%CI: 0.034-0.496, P = .003) and FSH (OR = 1.151, 95%CI: 1.043-1.275, P = .006) were predictors associated with the clinical pregnancy outcome of patients receiving IVF-ET. The results might provide a novel method to identify patients receiving IVF-ET with a high risk of poor pregnancy outcomes and provide interventions in those patients to prevent the occurrence of poor pregnancy outcomes.