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

<b>Background:</b> 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. <b>Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.

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Publication Details

Journal
Journal of Clinical Medicine
Published
2025-06-20
DOI
https://doi.org/10.3390/jcm14134407
Primary Topic
Thyroid Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Bernadett Nádasdi, Anna Vágvölgyi, János Zádori, Viktor Vedelek et al.
2 links
Journal of Clinical Medicine
Thyroid Disorders and Treatments
article

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

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

Abstract

<b>Background:</b> 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. <b>Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.

Journal of Clinical MedicineVol. 14(13)
University of Szeged (HU), HUN-REN Szegedi Biológiai Kutatóközpont (HU), Hungarian Research Network (HU)
Nemzeti Kutatási és Technológiai Hivatal, Szegedi Tudományegyetem
Good health and well-being
Openalex Percentile: Top 79%
Thyroid Disorders and Treatments
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