IVF Outcome Data with Vaginal and Semen Microbiological Cultures from 475 Couples

Anna Vágvölgyi's retrospective cohort study includes data from 475 couples undergoing IVF at the University of Szeged between January 2022 and December 2023. The dataset contains maternal demographics, reproductive history, hormone levels, ovarian stimulation characteristics, endometrial thickness, and results of vaginal and semen microbiological cultures. Machine learning models (SVM, RF, XGBoost) were applied to explore the predictive value of combined clinical and microbial features for clinical pregnancy.

Use Cases

• Predicting IVF clinical pregnancy outcomes based on combined clinical and microbial features.
• Analyzing associations between vaginal Lactobacillus colonization and pathogen presence.
• Investigating the prevalence and antibiotic resistance status of specific pathogens (e.g., Candida albicans, Enterococcus faecalis) in infertile couples.
• Evaluating the predictive power of maternal age versus microbial data for IVF success using machine learning models.

Strengths

• Includes data from 475 couples, a substantial cohort for analysis.
• Records results of microbiological cultures for both vaginal discharge and semen samples, including Lactobacillus colonization, pathogen distribution, and antibiotic resistance status.
• Integrates clinical parameters (demographics, hormone levels) with microbial data for machine learning analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data is from a single-center study (University of Szeged) and may reflect geographic bias.

Provenance

Source

Anna Vágvölgyi, University of Szeged.

Collection Method

Retrospective, single-center cohort study.

Time Range

January 2022 to December 2023.

Freshness

Last updated 2026-04-29 05:58:00; freshness should be verified.

Geography

Hungary (University of Szeged).