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Background <p>Unexplained recurrent pregnancy loss (URPL), which affects approximately 1–5% of women, is strongly associated with immune factors. However, accurately predicting the pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.</p> Methods <p>In this retrospective cohort study, we analyzed the medical records of 140 patients with URPL treated at Xiangya Hospital, Changsha, China, between January 2020 and December 2021. Outcomes included clinical pregnancies and miscarriages during the follow-up period. Predictors included levels of complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography, and serum lipids. The logistic regression analysis was performed for model development. The receiver operating characteristic curve and the calibration curve were used to determine the discriminatory ability of the model. In addition, a nomogram was constructed to visually represent the models.</p> Results <p>Out of the 140 URLP patients, 92 (65.7%) achieved clinical pregnancy and 48(34.3%) experienced miscarriages. A multivariable logistic regression model which includes complement, peripheral lymphocytes, and cholesterol accurately predicts the miscarriage of the URPL patients. A nomogram was developed based on these parameters with an area under the curve of 88.2% (95<i>CI</i>%: 82.6%-93.8%).</p> Conclusions <p>In this study, a novel scoring system based on maternal immunology characteristics was constructed. The scoring system could serve as a valuable decision - support tools for predicting pregnancy outcomes of URPL patients.</p>

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Pregnancy outcomes prediction performance of a scoring system based on maternal immunological parameters in patients with unexplained recurrent pregnancy loss: a monocentric retrospective study

  • 期刊论文

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  • 发表日期：

    2026年5月13日

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  • Yingrong Li,
  • Weiru Zhang,
  • Quan Chen,
  • Xuan Wang,
  • Tingting Xie,
  • Tingting Cheng,
  • Xinhua Li

摘要

Background

Unexplained recurrent pregnancy loss (URPL), which affects approximately 1–5% of women, is strongly associated with immune factors. However, accurately predicting the pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.

Methods

In this retrospective cohort study, we analyzed the medical records of 140 patients with URPL treated at Xiangya Hospital, Changsha, China, between January 2020 and December 2021. Outcomes included clinical pregnancies and miscarriages during the follow-up period. Predictors included levels of complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography, and serum lipids. The logistic regression analysis was performed for model development. The receiver operating characteristic curve and the calibration curve were used to determine the discriminatory ability of the model. In addition, a nomogram was constructed to visually represent the models.

Results

Out of the 140 URLP patients, 92 (65.7%) achieved clinical pregnancy and 48(34.3%) experienced miscarriages. A multivariable logistic regression model which includes complement, peripheral lymphocytes, and cholesterol accurately predicts the miscarriage of the URPL patients. A nomogram was developed based on these parameters with an area under the curve of 88.2% (95CI%: 82.6%-93.8%).

Conclusions

In this study, a novel scoring system based on maternal immunology characteristics was constructed. The scoring system could serve as a valuable decision - support tools for predicting pregnancy outcomes of URPL patients.