Comparison of Machine Learning model with Cox regression for prediction of cumulative live birth rate after assisted reproductive techniques: An internal and external validation

Immunological parameters of maternal peripheral blood as predictors of future pregnancy outcomes in patients with unexplained recurrent pregnancy loss

INTRODUCTION

Unexplained recurrent pregnancy loss (URPL), affecting approximately 1%-5% of women, exhibits a strong association with various maternal factors, particularly immune disorders. However, accurately predicting pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.

MATERIAL AND METHODS

In this historical cohort study, we analyzed the medical records of URPL patients treated at Xiangya Hospital, Changsha, China, between January 2020 and October 2022. The primary outcomes included clinical pregnancy and miscarriage. Predictors included complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography findings, and serum lipids. Least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis was performed for model development. The model's performance, discriminatory, and clinical applicability were assessed using area under the curve (AUC), calibration curve, and decision curve analysis, respectively. Additionally, models were visualized by constructing dynamic and static nomograms.

RESULTS

In total, 502 patients with URPL were enrolled, of whom 291 (58%) achieved clinical pregnancy and 211 (42%) experienced miscarriage. Notable differences in complement, peripheral lymphocytes, and serum lipids were observed between the two outcome groups. Moreover, URPL patients with elevated peripheral NK cells (absolute counts and proportion), decreased complement levels, and dyslipidemia demonstrated a significantly increased risk of miscarriage. Four models were developed in this study, of which Model 2 demonstrated superior performance with only seven predictors, achieving an AUC of 0.96 (95% CI: 0.93-0.99) and an accuracy of 0.92. A web-based platform was established to visually present model 2 and to facilitate its utilization by clinicians in outpatient settings (available from: https://yingrongli.shinyapps.io/liyingrong/).

CONCLUSIONS

Our findings suggest that the implementation of such prediction models could serve as valuable tools for providing comprehensive information and facilitating clinicians in their decision-making processes.