Risk factors for early miscarriage in patients with thin endometrium receiving <italic style="font-style: italic">in vitro</italic> fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling

Hu Xinyue; Hu Yuling; Lv Xingyu; Ding Yubin; Li Tian; Zhong Zhaohui; Tang Xiaojun

doi:10.13406/j.cnki.cyxb.003464

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Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling

Clinical research | 更新时间：2025-10-22

- Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling
- Journal of Chongqing Medical University Vol. 49, Issue 4, Pages: 478-485(2024)
- 作者机构：
  
  1.重庆医科大学公共卫生学院,重庆 400016
  2.重庆医科大学附属第一医院生殖医学中心,重庆 400016
  3.四川锦欣西囡妇女儿童医院生殖医学科,四川 610000
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.003464
  CLC： R714.8
- Received：29 November 2023，
  
  Revised：2024-02-28，
  
  Accepted：29 February 2024，
  
  Published：28 April 2024
- 稿件说明：
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胡馨月,胡瑜凌,吕兴钰,等.基于机器学习预测模型探究薄型子宫内膜患者接受IVF/ICSI-ET治疗的早期流产风险因素[J].重庆医科大学学报,2024,49(4):478-485.

Hu Xinyue,Hu Yuling,Lv Xingyu,et al.Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling[J].Journal of Chongqing Medical University,2024,49(4):478-485.
胡馨月,胡瑜凌,吕兴钰,等.基于机器学习预测模型探究薄型子宫内膜患者接受IVF/ICSI-ET治疗的早期流产风险因素[J].重庆医科大学学报,2024,49(4):478-485. DOI： 10.13406/j.cnki.cyxb.003464.

Hu Xinyue,Hu Yuling,Lv Xingyu,et al.Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling[J].Journal of Chongqing Medical University,2024,49(4):478-485. DOI： 10.13406/j.cnki.cyxb.003464.

摘要

目的

基于多种机器学习方法，探讨薄子宫内膜患者在新鲜胚胎移植中发生早期流产的影响因素，并建立预测模型，为预防薄子宫内膜患者在进行新鲜胚胎移植中发生早期流产提供合理的指导思路。

方法

纳入了首次进行新鲜胚胎移植的薄子宫内膜患者1153例，通过LASSO回归和随机森林递归特征消除（recursive feature elimination，RFE）筛选特征，建立6种机器学习模型，通过交叉验证、准确度、敏感性、召回率、f1值、ROC曲线下面积及校准曲线比较不同模型的性能。SHAP图用于解释影响早期流产的因素。

结果

通过LASSO回归和随机森林RFE筛选出29个特征变量纳入六种机器学习模型，其中多层感知机模型对早期流产的区分度最佳，ROC曲线下面积为0.803（95%CI=0.772~0.834）。随机森林、XGBoost和AdaBoost模型的ROC曲线下面积都高于0.7。

结论

开发了薄子宫内膜患者在新鲜胚胎移植中是否发生早期流产的机器学习预测模型，各种评价指标的验证表明该模型的性能良好，有助于临床医生对该人群患者的早期诊断，为未来改善早期流产高危患者的妊娠结局提供指导思路。

Abstract

Objective

To investigate the influencing factors for early miscarriage in in patients with thin endometrium during fresh embryo transfer based on multiple machine learning methods，to establish a predictive model，and to provide reasonable ideas for preventing early miscarriage in patients with thin endometrium undergoing fresh embryo transfer.

Methods

A total of 1153 patients with thin endometrium who underwent fresh embryo transfer for the first time were enrolled in this study，and LASSO regression and random forest recursive feature elimination（RFE） were used for feature selection. Six machine learning models were developed and compared in terms of cross validation，accuracy，sensitivity，recall rate，f1 value，area under the ROC curve，and calibration curve. SHAP plots were used to elucidate the influencing factors for early miscarriage.

Results

A total of 29 feature variables were identified by LASSO regression and random forest RFE and were included in the six machine learning models，among which the multilayer perceptron model showed the best discriminatory ability for early miscarriage，with an area under the ROC curve of 0.803（95%CI=0.772-0.834）. The random forest，XGBoost，and AdaBoost models had an area under the ROC curve of >0.7.

Conclusion

This study establishes a machine learning-based predictive model for early miscarriage in patients with thin endometrium during fresh embryo transfer，and validation of various evaluation metrics shows that the model has good performance and can help clinicians to achieve the early diagnosis of patients，thereby providing ideas for improving the pregnancy outcome of patients at high risk of early miscarriage in the future.

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Keywords

references

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Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling

DOI：10.13406/j.cnki.cyxb.003464

摘要

Abstract

关键词

Keywords

references