Evaluating The Effective Factors in Pregnancy after Intrauterine Insemination: A Retrospective Study

Pregnancy among Women Undergoing Intrauterine Insemination in a Centre

Introduction

Intrauterine insemination is often performed as the first-line treatment option in many subfertility cases. Successful intrauterine insemination treatment among indicated couples helps to reduce unnecessary in-vitro fertilisation treatment. Although widely practised, the data on pregnancy after intrauterine insemination treatment is limited. The aim of this study was to find out the prevalence of pregnancy among women undergoing intrauterine insemination in a centre.

Methods

A descriptive cross-sectional study was conducted at a centre among women undergoing intrauterine insemination. Data from 15 May 2017 to 15 April 2021 were collected between 16 June 2022 to 1 July 2022 from the hospital records. Ethical approval was taken from the Nepal Health Research Council. Systematic sampling technique was applied to select the appropriate sample. The point estimate was calculated at a 95% Confidence Interval.

Results

Among 225 women undergoing intrauterine insemination, pregnancy was achieved in 23 (10.22%) (6.26-14.18, 95% Confidence Interval). The mean age of pregnant women was 29.17±3.34 years and the mean duration of infertility was 3.93±2.90 years. More than half of the pregnant women 13 (56.52%) had undergone insemination with the husband's semen whereas the remaining 10 women (43.48%) had undergone insemination with donor semen.

Conclusions

The prevalence of pregnancy among women undergoing intrauterine insemination was similar to other studies done in similar settings.

Keywords

artificial insemination; Nepal; pregnancy rate.

Using artificial intelligence to predict the intrauterine insemination success rate among infertile couples

Background

To evaluate the use of artificial intelligence (AI) in predicting the success rate of intrauterine insemination (IUI) treatment among infertile couples and also to determine the importance of each of the parameters affecting IUI success. This study was a retrospective cohort study in which information from 380 infertile couples undergoing IUI treatment (190 cases resulting in positive pregnancy test and 190 cases of failed IUI) including underlying factors, female factors, sperm parameters at the beginning of the treatment cycle, and fertility results were collected from 2013 to 2019 and evaluated to determine the effectiveness of AI in predicting IUI success.

Results

We used the most important factors influencing the success of IUI as a neural network input. With the help of a three-layer neural network, the accuracy of the AI to predict the success rate of IUI was 71.92% and the sensitivity and specificity were 76.19% and 66.67%, respectively. The effect of each of the predictive factors was obtained by calculating the ROC curve and determining the cut-off point.

Conclusions

The morphology, total motility, and progressive motility of the sperm were found to be the most important predictive factors for IUI success. In this study, we concluded that by predicting IUI success rate, artificial intelligence can help clinicians choose individualized treatment for infertile couples and to shorten the time to pregnancy.

Indication of intravaginal insemination for infertility treatment in couples with sexual dysfunction

PURPOSE

To analyze the usefulness of intravaginal insemination (IVI) for the infertility treatment in couples with sexual dysfunction before applying assisted reproductive technology (ART).

METHODS

Among 208 couples who presented sexual dysfunction, 144 couples underwent IVI procedures. The profiles of pregnant and non-pregnant patients were compared.

RESULTS

Of 144 patients, 58 women conceived successfully (40.3% pregnancy rate). Between the pregnant and non-pregnant cases, the husband's age and infertility period were significantly higher (P = .0104) and longer (P = .0027) in the unsuccessful cases than the successful ones. The husbands who could not impregnate had a significantly higher ratio of sperm abnormalities (P = .0048). Among the 57 successful cases who underwent IVI treatment, 38 (66.7%) patients became pregnant within 3 times of the procedure, while 48 (84.2%) patients conceived within 6 times.

CONCLUSION

The authors can propose the following inclusion IVI criteria for couples with sexual dysfunction: (a) younger husband (36 years old or less) which may be most important, (b) infertility duration of 3 years or less, (c) normal sperm condition, and (d) IVI trial for 3 times (maximum of 6 times). Since IVI appears to be a simple, noninvasive, and inexpensive way for couples with sexual dysfunction, it can be attempted before ART application.

CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

Background

Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features.

Methods

For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome.

Results

Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome.

Conclusions

The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

Intrauterine insemination cycles: prediction of success and thresholds for poor prognosis and futile care

PURPOSE

We aimed to define intrauterine insemination (IUI) cycle characteristics associated with viable birth, identify thresholds below which IUI treatments are consistent with very poor prognosis and futile care, and develop a nomogram for individualized application.

METHODS

This retrospective cohort study evaluated couples using fresh partner ejaculate for IUI from January 2005 to September 2017. Variables included female age, semen characteristics, and ovarian stimulation type. Using cycle-level data, we evaluated the association of these characteristics with the probability of viable birth by fitting generalized regression models for a binary outcome with a logit link function, using generalized estimating equation methodology to account for the correlation between cycles involving the same patient.

RESULTS

The cohort consisted of 1117 women with 2912 IUI cycles; viable birth was achieved in 275 (9.4%) cycles. Futile care (viable birth rate < 1%) was identified for women age > 43, regardless of stimulation type or inseminate motility (IM). Very poor prognosis (viable birth rate < 5%) was identified for women using oral medications or Clomid plus gonadotropins who were (1) age < 35 with IM < 49%, (2) age 35-37 with IM < 56%, or (3) age ≥ 38, and (4) women age ≥ 38 using gonadotropins only with IM < 60%. A clinical prediction model and nomogram was developed with an optimism-corrected c-statistic of 0.611.

CONCLUSIONS

The present study highlights the impact of multiple clinical factors on IUI success, identifies criteria consistent with very poor prognosis and futile care, and provides a nomogram to individualize counseling regarding the probability of a viable birth.

Electroacupuncture facilitates implantation by enhancing endometrial angiogenesis in a rat model of ovarian hyperstimulation

Controlled ovarian hyperstimulation (COH) impairs the synchronized development of endometrium and embryo, resulting in the failure of embryo implantation. Here, we investigated what effects electroacupuncture had on embryo implantation in COH rats. Female rats were randomly assigned to four groups: normal (N), model (M), electroacupuncture (EA), and electroacupuncture pretreatment (PEA). Rats in groups M, EA, PEA were injected with pregnant mare serum gonadotropin (PMSG) and human chorionic gonadotropin to establish the COH model. Rats in group EA received electroacupuncture treatment from the PMSG injection day to the 3rd day of pregnancy (D3), while those in group PEA received electroacupuncture treatment for 3 days before the PMSG day and continuing to D3. Furthermore, another 30 female rats who received the same treatment as the rats in group PEA were injected with siVEGFR2 into uterine lumen. The endometrial microvascular density (MVD) and the expression levels of vascular endothelial growth factor-A, angiopoietin-1, and fibroblast growth factor-2 were significantly lower in groups M than in groups N and PEA. The percentage of dolichos biflorus agglutinin positive uterine natural killer cells in groups N, EA and PEA was higher than that in group M. After the siVEGFR2 injection, the protein expression levels of vascular endothelial growth factor receptor 2 (VEGFR2), PI3K, p-AKT and p-ERK, the embryo number and the MVD were significantly reduced. In conclusion, electroacupuncture can facilitate embryo implantation in COH rats by activating the VEGFR2/PI3K/AKT and VEGFR2/ERK signaling pathways which have a positive relationship with endometrial angiogenesis.

Does different subfertility etiology affect pregnancy rates in intrauterineinsemination cycles?

Background/aim

To investigate the relationship between subfertility etiologies and success rates in controlled ovarian stimulation and intrauterine insemination (COS–IUI) cycles.

Materials and methods

The medical records of 218 couples who applied to a university-based fertility center were analyzed retrospectively. Detailed infertility examination data and pregnancy outcomes were compared according to different subfertility etiologies. The study groups with regard to subfertility etiologies were minimal–mild endometriosis, unexplained infertility, and mild male infertility. The primary outcome measure was live birth rate.

Results

There were no statistically significant differences between the groups regarding demographics except for total motile sperm count. Live birth rates in the male infertility group were comparable to the endometriosis and unexpected infertility groups (6.6%, 11.9%, and 10.3%, respectively; P = 0.63).

Conclusion

The success rate of the mild male subfertility group following COS–IUI cycles for live birth rates was similar to those of the endometriosis and unexplained subfertility groups.

The relationship between semen parameters in processed and unprocessed semen with intrauterine insemination success rates

Objective:

To evaluate the relationship between semen parameters and intrauterine insemination (IUI) success rates.

Material and Methods:

This retrospective study was conducted during a 4-year period (2011-2015) on the medical records of 350 couples admitted to the infertility center of Beast Hospital in Tehran. The participants’ data such as age, duration of infertility, semen parameters [including volume, concentration, motility, normal morphology and total motile sperm count (TMSC)] before and after sperm processing, as well as the IUI results were extracted from the patients’ records. Only the first IUI cycle of the couples was considered. The main outcome criterion for the IUI success was serum positive beta human chorionic gonadtotropin 14 days after IUI. The collected data were analyzed using the Mann-Whitney U test, chi-square, and Fisher’s exact tests.

Results:

The overall pregnancy rate for each couple was reported as 23.42% (82/350). There was no significant difference in the mean age of the couple and infertility duration between the groups who achieved pregnancy and those who failed. The two groups showed no significant differences in pre and post processing of semen parameters (including volume, concentration and TMSC). Sperm motility and normal sperm morphology before and after sperm processing were significantly different between the two groups, respectively (p=0.023 before sperm processing and p=0.032 after) (p=0.032 before sperm processing and p=0.007 after).

Conclusion:

Sperm motility and normal sperm morphology have an effect in IUI success.

Prognostic factors in intrauterine insemination cycles

Objective

This study aimed to evaluate the clinical pregnancy rate of intrauterine insemination cycles in relation to patient age, cause of infertility, ovulation induction method, number of mature follicles and sperm with progressive motility.

Methods

This retrospective observational study included 237 intrauterine insemination cycles performed from 2011 to 2015 at the Assisted Reproduction Service of the Hospital das Clínicas of the Ribeirão Preto Medical School, University of São Paulo. Student's t-test was used to compare quantitative variables and the chi-square test was used to compare qualitative variables.

Results

Patient age was inversely and significantly correlated with pregnancy rates (p=0.001) (Pregnant women = 32.56±5.64 years, non-pregnant women = 36.64±5.03 years). Cause of infertility, ovulation induction method, number of mature follicles and sperm with progressive motility were not associated with pregnancy rates. The overall clinical pregnancy rate was 7.59%. In the subgroup of patients (n=102 cycles) considered ideal for intrauterine insemination (age ≤35 years, unexplained infertility, ovarian factor infertility or minimal endometriosis, and a partner with sperm count ≥2.5×10⁶ retrieved on the day of insemination) the pregnancy rate was 12.74%.

Conclusion

In the studied group, female patient age was the only variable significantly correlated with intrauterine insemination success rates.