Should intrauterine inseminations still be proposed in cases of unexplained infertility? Retrospective study and literature review

The definition of unexplained infertility: A systematic review

BACKGROUND

There is no consensus on tests required to either diagnose unexplained infertility or use for research inclusion criteria. This leads to heterogeneity and bias affecting meta-analysis and best practice advice.

OBJECTIVES

This systematic review analyses the variability of inclusion criteria applied to couples with unexplained infertility. We propose standardised criteria for use both in future research studies and clinical diagnosis.

SEARCH STRATEGY

CINAHL and MEDLINE online databases were searched up to November 2022 for all published studies recruiting couples with unexplained infertility, available in full text in the English language.

DATA COLLECTION AND ANALYSIS

Data were collected in an Excel spreadsheet. Results were analysed per category and methodology or reference range.

MAIN RESULTS

Of 375 relevant studies, only 258 defined their inclusion criteria. The most commonly applied inclusion criteria were semen analysis, tubal patency and assessment of ovulation in 220 (85%), 232 (90%), 205 (79.5%) respectively. Only 87/220 (39.5%) studies reporting semen analysis used the World Health Organization (WHO) limits. Tubal patency was accepted if bilateral in 145/232 (62.5%) and if unilateral in 24/232 (10.3%). Ovulation was assessed using mid-luteal serum progesterone in 115/205 (56.1%) and by a history of regular cycles in 87/205 (42.4%). Other criteria, including uterine cavity assessment and hormone profile, were applied in less than 50% of included studies.

CONCLUSIONS

This review highlights the heterogeneity among studied populations with unexplained infertility. Development and application of internationally accepted criteria will improve the quality of research and future clinical care.

Should IUI replace IVF as first-line treatment for unexplained infertility? A literature review

BACKGROUND

Unexplained infertility accounts for 25% of infertility causes in the UK. Active intervention methods, such as intrauterine insemination (IUI) or in vitro fertilisation (IVF), are often sought. Despite the National Institute for Health and Care Excellence (NICE) recommending IVF for unexplained infertility, this recommendation has generated an ongoing debate, with few fertility clinics discontinuing the use of IUI as the first-line management of choice. In contrast to NICE, recent guidance released from the European Society for Human Reproduction and Embryology (ESHRE) in August 2023 supports the use of IUI as first-line. High-quality evidence behind such interventions is lacking, with current literature providing conflicting results.

AIMS

This review aims to provide a literature overview exploring whether IUI or IVF should be used as first-line treatment for couples with unexplained infertility, in the context of current guidelines.

METHODS

The primary outcome used to assess efficacy of both treatment methods is live birth (LB) rates. Secondary outcomes used are clinical pregnancy (CP) and ongoing pregnancy (OP) rates. A comprehensive literature search of 4 databases: Ovid MEDLINE, EMBASE, Maternity & Infant Care and the Cochrane Library were searched in January 2022. Upon removal of duplications, abstract screening, and full-text screening, a total of 34 papers were selected.

DISCUSSION/CONCLUSION

This review highlights a large discrepancy in the literature when examining pregnancy outcomes of IUI and IVF treatments. Evidence shows IUI increases LB and CP rates 3-fold compared to expectant management. Literature comparing IUI to IVF is less certain. The review finds the literature implies IVF should be used for first-line management but the paucity of high-quality randomised controlled trials (RCTs), coupled with heterogeneity of the identified studies and a lack of research amongst women > 40 years warrants the need for further large RCTs. The decision to offer IUI with ovarian stimulation (IUI-OS) or IVF should be based upon patient prognostic factors. We suggest that IUI-OS could be offered as first-line treatment for unexplained infertility for women < 38 years, with good prognosis, and IVF could be offered first to those > 38 years. Patients should be appropriately counselled to enable informed decision making.

The management of infertility for primary care physicians

To conduct this review of primary care, we looked for related papers in PubMed from the last 15 years. WHO's initial concept of Health defined a condition of physical, mental, and social well-being, nowadays extended to quality of life. Infertility or not being able to form a family fits perfectly into the definition. Primary care is responsible for mandatory discussions about fertility as part of a broader aspect regarding reproductive health issues. Having children is a decision taken by heterosexual couples, same sex couples, or single individuals. Understanding factors associated with infertility help guiding propedeutic. Although woman's age is one of the main factors to influence treatment success rates, multifactorial male factor may contribute to 50 percent. Infertility consultations should include partners, alleviating the accompanying stress and anxiety. Anamnesis must focus on duration of infertility, primary or secondary, sexual activity, and lifestyle habits such as smoking, alcohol consumption, diet, physical activity, use of licit and/or illicit drugs, and occupational risks. Previous treatments should be accessed. Management of infertility by primary care is mandatory, and patients requiring specialized treatments must not have their journey protracted. Strategies and couple-based interventions are essential to continuity of care and close follow-up should follow these patients.

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Background

This 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.

Methods

The 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.

Results

In 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.

Conclusion

The 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01974-8.

Effects of hysterosalpingo-contrast sonography examination on endometrial receptivity among women with unexplained infertility

PURPOSE

Hysterosalpingo-contrast sonography (HyCoSy) is the preferred method for evaluating fallopian tubal patency, and it is associated with improved rates of natural pregnancy among infertile patients. However, the mechanism underlying the improvement in pregnancy rates following HyCoSy remains unclear. This study aimed to investigate the effect of HyCoSy examination on endometrial receptivity as well as pregnancy rates among infertile women.

METHODS

This prospective study included 120 women with unexplained infertility who visited our department between June 2018 and February 2021. These patients were classified into the study group (n = 60) and the control group (n = 60) depending on their willingness to undergo three-dimensional HyCoSy in the present cycle (study group) or 6 months later (control group). Endometrial characteristics, including endometrial thickness and pattern as well as the endometrial blood flow distribution pattern, were measured twice by transvaginal Doppler ultrasonography in the preovulatory phase before and after HyCoSy examination. Participants were followed for 6 months to observe the outcome of spontaneous conception.

RESULTS

Compared with the control group, the study group had a significantly higher cumulative pregnancy rate at 6 months after HyCoSy (21.6% [13/60] vs 5.0% [3/60], P = 0.007). More patients in the study group showed improved endometrial blood flow distribution (P = 0.021, χ² = 7.699), but no differences in endometrial thickness and pattern were observed between the groups (P > 0.05).

CONCLUSION

HyCoSy examination may improve endometrial perfusion and has a therapeutic effect on improving spontaneous pregnancy among women with unexplained infertility.