Clarifying the relationship between total motile sperm counts and intrauterine insemination pregnancy rates

Muthigi, Akhil; Jahandideh, Samad; Bishop, L.A.; Naeemi, Firoozeh K.; Shipley, S.K.; O’Brien, Jeanne E.; Shin, Paul; Devine, Kate; Tanrikut, Cigdem

doi:10.1016/j.fertnstert.2021.01.014

Cited by 31 publications

(14 citation statements)

References 17 publications

(20 reference statements)

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“…This seemed to reflect the low number of patients continuing past 3 cycles rather than a genuine decrease in per cycle success rate. This is in line with the results of a recent study by Muthigi et al, reporting consistent pregnancy rates up to 6 total IUI cycles [ 26 ]. In contrast, other previous studies described a plateau of IUI success rates after 3 or 4 cycles.…”

Section: Discussionsupporting

confidence: 92%

“…It has previously been reported that the minimum TPMSC for the performance of an IUI should be > 5 mil [ 21 , 48 ]. Recently, Muthigi et al found maximal pregnancy rates at IUI with a TPMSC ≥ 9 mil [ 26 ]. These data referred to TPMSC after semen preparation, whereas the correlation between pre-wash TPMSC and live birth rate after IUI was previously found to be low [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

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Predicting success of intrauterine insemination using a clinically based scoring system

Zippl

Wachter

Rockenschaub

et al. 2022

Arch Gynecol Obstet

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Purpose To develop a predictive score for the success of intrauterine insemination (IUI) based on clinical parameters. Methods We performed a retrospective cohort study evaluating the homologous IUI cycles performed at a single university-based reproductive medical center between 2009 and 2017. The primary outcome measure was pregnancy, defined as positive serum human chorionic gonadotropin (hCG) 12–14 days after IUI. Predictive factors for pregnancy after IUI were identified, and a predictive score was developed using a multivariable continuation ratio model. Results Overall, 1437 IUI cycles in 758 couples were evaluated. We found a per cycle pregnancy rate of 10.9% and a cumulative pregnancy rate of 19.4%. In a multivariable analysis, the probability of pregnancy was negatively associated with female age ≥ 35 years (OR 0.63, 95% CI 0.41–0.97, p = 0.034), endometriosis, unilateral tubal factor, or anatomical alteration (OR 0.54, 95% CI 0.33–0.89, p = 0.016), anti-Mullerian hormone (AMH) < 1 ng/ml (OR 0.50, 95% CI 0.29–0.87, p = 0.014), and total progressive motile sperm count (TPMSC) < 5 mil (OR 0.47, 95% CI 0.19–0.72, p = 0.004). We developed a predictive clinical score ranging from 0 to 5. Following 3 cycles, couples in our cohort with a score of 5 had a cumulative probability of achieving pregnancy of nearly 45%. In contrast, couples with a score of 0 had a cumulative probability of only 5%. Conclusion IUI success rates vary widely depending on couples’ characteristics. A simple to use score could be used to estimate a couple’s chance of achieving pregnancy via IUI, facilitating individualized counseling and decision-making.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Predicting success of intrauterine insemination using a clinically based scoring system

Zippl

Wachter

Rockenschaub

et al. 2022

Arch Gynecol Obstet

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“…On the other hand, the increase in the number of assisted reproductive institutions approved by the state can alleviate the negative impact of 'cannot birth', but the solution to the problem of infertility still needs the advancement of assisted reproductive technology. The optimized sperm was sent into the woman's uterine cavity through IUI to achieve the process of natural fertilization, pregnancy and childbirth, which is one of the commonly used assisted reproductive technologies, and the largest comprehensive analysis integrating success, risks and costs shows that IUI is safer and more cost-effective than other ART treatments (19,20). IUI pregnancy rates have been reported to be mixed and varied widely, ranging from 8% to 22% (29).…”

Section: Discussionmentioning

confidence: 99%

“…The successful application of sperm freezing technology provides conditions for the preservation of male fertility and the storage and transportation of donated semen, and artificial insemination technology for sperm has been applied on a large scale. Since artificial insemination technology is closer to natural conception, it has the advantages of noninvasiveness, simplicity and convenience, making most patients more acceptable and more compliant, and it is also the preferred adjuvant treatment in clinical practice (18,19). Intrauterine insemination (IUI) is safer and more costeffective in clinical practice than other ARTs (20).…”

Section: Introductionmentioning

confidence: 99%

Correlations between elevated basal sperm DNA fragmentation and the clinical outcomes in women undergoing IUI

et al. 2022

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ObjectiveThis study aimed to explore the impact of the sperm DNA fragmentation index (DFI) on the clinical outcomes in women undergoing artificial insemination by husband intrauterine insemination (AIH-IUI).MethodsIn this retrospective study, the value of sperm DFI was detected by sperm chromatin structure assay (SCSA) in a semen analysis collected before fertility treatment (basal DFI) in 1,500 IUI cycles at the infertility clinic of Northern Jiangsu People’s Hospital Reproductive Medicine Center from Jan 2016 to April 2021. Receiver operating characteristic (ROC) curves were used to calculate the cut-off value for the clinical outcomes of IUI, including the biochemical pregnancy rate, clinical pregnancy rate, delivery rate, and live birth rate, and multivariate logistic regression was conducted to analyse the risk factors for clinical outcomes after IUI.ResultIn 1,500 IUI cycles, the results showed that there were no statistically significant differences between the normal DFI group and the abnormal DFI group in biochemical pregnancy rate (14.41% vs. 11.3%, P = 0.386), clinical pregnancy rate (12.9% vs. 10.5%, P = 0.433), delivery rate (11.0% vs. 8.9%, P = 0.456), live birth rate (10.9% vs. 8.9%, P = 0.484) or pregnancy loss rate (14.6% vs. 15.4%, P = 1.000).ConclusionSperm DFI alone may have limited predictive power for IUI clinical outcomes.

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“…Every year more than seven million couples seek treatment for infertility, a disease that affects around 15% of couples worldwide [ 1 ]. Unsuccessful clinical pregnancy after 12 months of regular sexual intercourse is considered infertility [ 2 , 3 ]. Nowadays, infertile couples rely on sophisticated laboratory technology to conceive, and various methods are used, from less aggressive to more aggressive, to help infertile couples.…”

Section: Introductionmentioning

confidence: 99%

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

Khodabandelu

Basirat

Khaleghi

et al. 2022

BMC Med Inform Decis Mak

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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.

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Clarifying the relationship between total motile sperm counts and intrauterine insemination pregnancy rates

Cited by 31 publications

References 17 publications

Predicting success of intrauterine insemination using a clinically based scoring system

Predicting success of intrauterine insemination using a clinically based scoring system

Correlations between elevated basal sperm DNA fragmentation and the clinical outcomes in women undergoing IUI

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

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