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体外受精前第2天促甲状腺激素水平对治疗成功率和产科结局的影响:一项基于机器学习数据评估的回顾性单中心分析

The Influence of Pre-IVF Day 2 TSH Levels on Treatment Success and Obstetric Outcomes: A Retrospective Single-Center Analysis with Machine Learning-Based Data Evaluation.

作者信息

Nádasdi Bernadett, Vedelek Viktor, Bereczki Kristóf, Bukva Mátyás, Kozinszky Zoltan, Sinka Rita, Zádori János, Vágvölgyi Anna

机构信息

Department of Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6725 Szeged, Hungary.

Department of Genetics, Faculty of Science and Informatics, University of Szeged, 6726 Szeged, Hungary.

出版信息

J Clin Med. 2025 Jun 20;14(13):4407. doi: 10.3390/jcm14134407.

Abstract

Thyroid disorders, particularly thyroid autoimmunity, are increasingly prevalent among women of reproductive age and have been linked to fertility outcomes. While current endocrinology guidelines define distinct thyroid-stimulating hormone (TSH) target values for women undergoing assisted reproductive technology (ART), the optimal preconception TSH range for in vitro fertilization (IVF) success remains a topic of debate. This study aimed to assess the impact of baseline TSH levels within the recommended normal range on IVF outcomes, specifically clinical pregnancy and live birth rates. Additionally, we assessed the predictive value of procedural and preprocedural factors, including maternal body mass index (BMI) and TSH, using machine learning models. We conducted a retrospective, single-center cohort study at the Institute of Reproductive Medicine, University of Szeged, involving 996 women who underwent IVF, with or without intracytoplasmic sperm injection. Biometric, medical history, laboratory, and procedural factors were analyzed. Pregnancy and live birth predictions were modeled using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) algorithms. The significance of features in the RF and XGBoost models was assessed. SVM models achieved a mean accuracy of 72.26% in predicting pregnancy but were less effective for live birth classification. RF and XGBoost models demonstrated an area under the receiver operating characteristic curve of 0.76 and 0.74 for pregnancy and 0.67 and 0.61, respectively, for live birth. Key predictors included embryo score, maternal age, BMI, and specific hormone levels. Notably, male factors also contributed to outcome prediction. Analysis suggested that variations in maternal TSH within the normal range (0.3-4.0 mIU/L) had no significant impact on IVF success. Our study suggests that preconception TSH levels within the reference range do not significantly influence IVF success, which indirectly supports the validity of the current recommendations on this matter. While machine learning models demonstrated promising predictive performance, larger prospective studies are needed to refine thyroid function targets in ART, with a separate analysis of women with thyroid autoimmunity.

摘要

甲状腺疾病,尤其是甲状腺自身免疫性疾病,在育龄女性中越来越普遍,并且与生育结局有关。虽然当前内分泌学指南为接受辅助生殖技术(ART)的女性定义了不同的促甲状腺激素(TSH)目标值,但体外受精(IVF)成功的最佳孕前TSH范围仍是一个有争议的话题。本研究旨在评估推荐正常范围内的基线TSH水平对IVF结局的影响,特别是临床妊娠率和活产率。此外,我们使用机器学习模型评估了包括母体体重指数(BMI)和TSH在内的手术及术前因素的预测价值。我们在塞格德大学生殖医学研究所进行了一项回顾性单中心队列研究,纳入了996例行IVF(无论有无卵胞浆内单精子注射)的女性。分析了生物特征、病史、实验室及手术因素。使用支持向量机(SVM)、随机森林(RF)和极端梯度提升(XGBoost)算法对妊娠和活产进行预测建模。评估了RF和XGBoost模型中特征的显著性。SVM模型在预测妊娠方面的平均准确率为72.26%,但对活产分类的效果较差。RF和XGBoost模型对妊娠的受试者工作特征曲线下面积分别为0.76和0.74,对活产的曲线下面积分别为0.67和0.61。关键预测因素包括胚胎评分、母体年龄、BMI和特定激素水平。值得注意的是,男性因素也对结局预测有贡献。分析表明,母体TSH在正常范围(0.3 - 4.0 mIU/L)内的变化对IVF成功没有显著影响。我们的研究表明,参考范围内的孕前TSH水平不会显著影响IVF成功,这间接支持了当前关于此事的建议的有效性。虽然机器学习模型显示出有前景的预测性能,但需要更大规模的前瞻性研究来完善ART中的甲状腺功能目标,并对患有甲状腺自身免疫性疾病的女性进行单独分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fa/12250441/2b04b912b797/jcm-14-04407-g001.jpg

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