Dataset Information

Pregnancy rates after slow-release insemination (SRI) and standard bolus intrauterine insemination (IUI) - A multicentre randomised, controlled trial.

ABSTRACT: This multicentre, randomised, controlled cross-over trial was designed to investigate the effect of intra-uterine slow-release insemination (SRI) on pregnancy rates in women with confirmed infertility or the need for semen donation who were eligible for standard bolus intra-uterine insemination (IUI). Data for a total of 182 women were analysed after randomisation to receive IUI (n?=?96) or SRI (n?=?86) first. The primary outcome was serological pregnancy defined by a positive beta human chorionic gonadotropin test, two weeks after insemination. Patients who did not conceive after the first cycle switched to the alternative technique for the second cycle: 44 women switched to IUI and 58 switched to SRI. In total, there were 284 treatment cycles (IUI: n?=?140; SRI: n?=?144). Pregnancy rates following SRI and IUI were 13.2% and 10.0%, respectively, which was not statistically significant (p?=?0.202). A statistically significant difference in pregnancy rates for SRI versus IUI was detected in women aged under 35 years. In this subgroup, the pregnancy rate with SRI was 17% compared to 7% with IUI (relative risk 2.33; p?=?0.032) across both cycles. These results support the hypothesis that the pregnancy rate might be improved with SRI compared to standard bolus IUI, especially in women aged under 35 years.

SUBMITTER: Marschalek J

PROVIDER: S-EPMC7206062 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Pregnancy rates after slow-release insemination (SRI) and standard bolus intrauterine insemination (IUI) - A multicentre randomised, controlled trial.

Marschalek Julian J Egarter Christian C Vytiska-Binsdorfer Elisabeth E Obruca Andreas A Campbell Jackie J Harris Philip P van Santen Maarten M Lesoine Bernd B Ott Johannes J Franz Maximilian M

Scientific reports 20200507 1

This multicentre, randomised, controlled cross-over trial was designed to investigate the effect of intra-uterine slow-release insemination (SRI) on pregnancy rates in women with confirmed infertility or the need for semen donation who were eligible for standard bolus intra-uterine insemination (IUI). Data for a total of 182 women were analysed after randomisation to receive IUI (n = 96) or SRI (n = 86) first. The primary outcome was serological pregnancy defined by a positive beta human chorion ...[more]

PMID: 32382043

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Project description:BackgroundThis study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.MethodsThe data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development.ResultsIn this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success.ConclusionThe results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy.

Dataset Information

Pregnancy rates after slow-release insemination (SRI) and standard bolus intrauterine insemination (IUI) - A multicentre randomised, controlled trial.

Publications

Pregnancy rates after slow-release insemination (SRI) and standard bolus intrauterine insemination (IUI) - A multicentre randomised, controlled trial.

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