Predicting risk of subsequent pregnancy loss among women with recurrent pregnancy loss: An immunological factor-based multivariable model.

Abstract

Studies on subsequent pregnancy loss prediction models specific for recurrent pregnancy loss (RPL) patients are very limited. This study aims to develop a risk predictive model based on the immunological parameters for the subsequent pregnancy loss risk in northwest Chinese RPL patients. Totally of 357 RPL patients recruited from Lanzhou University Second Hospital were included in this retrospective study. Univariate analysis was performed on RPL patients with outcomes of live birth or pregnancy loss. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables among baseline and clinical characteristics and to develop a pregnancy loss risk prediction model with all 357 RPL patients. The area under the curve (AUC), calibration curve and decision curve analyses were used to evaluate the performance of the prediction model; moreover, 10-fold cross-validation was used for internal validation. Ten factors of maternal age, age of menarche, previous pregnancy loss, IL-10, complement 4, IgA, antiprothrombin antibody IgG/IgM, rheumatoid factor IgA, and lupus anticoagulant (LA) 1/LA2 ratio were finally selected as variables for the prediction model of pregnancy loss risk. The AUC value and Hosmer-Lemeshow test p-value of the model were.707 and.599, respectively, indicating a satisfactory discrimination and calibration performance. Moreover, the clinical decision curve suggested this prediction model have a good positive net benefit. This is the first prediction model for the risk of subsequent pregnancy loss in northwest Chinese women with RPL, providing a user-friendly tool to clinicians for the early prediction and timely management of RPL patients.