The evaluation of pre and post processing semen analysis parameters at the time of intrauterine insemination in couples diagnosed with male factor infertility and pregnancy rates based on stimulation agent. A retrospective cohort study

Luco, Stephanie M.; Agbo, C.; Behr, Barry; Dahan, Michael H.

doi:10.1016/j.ejogrb.2014.05.003

Cited by 36 publications

(41 citation statements)

References 13 publications

(12 reference statements)

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“…Some investigators have suggested a beneficial effect, whereas others observed no benefit . In some cases, the protocols of ovarian stimulation were clearly defined, but in others, different protocols were grouped together . In our study when all ovarian stimulation protocols were lumped together, we could not find any difference in pregnancy or live birth rate.…”

Section: Discussioncontrasting

confidence: 69%

“…11 In some cases, the protocols of ovarian stimulation were clearly defined, 12,15 but in others, different protocols were grouped together. 13,14 In our study when all ovarian stimulation protocols were lumped together, we could not find any difference in pregnancy or live birth rate. The same applied to our case series if we grouped all cases of ovarian stimulation together, in which case the live birth and pregnancy rates were 7 and 9.5% compared with no stimulation, for which the corresponding rates were 7.6 and 9.3% (P = 0.39 and 0.77, respectively).…”

Section: Discussioncontrasting

confidence: 57%

“…Some investigators have advocated using the natural cycle 10,11 whereas others have claimed that ovarian stimulation may lead to better results. 12,13 Furthermore, there is still no consensus on the optimal protocol for ovarian stimulation. Various stimulation protocols have been applied, including clomifene citrate (CC), letrozole (LE), human menopausal gonadotrophin (HMG), urinary follicle-stimulating hormone (uFSH), recombinant FSH (rFSH) and combinations of these products.…”

Section: Introductionmentioning

confidence: 99%

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The impact of ovarian stimulation on the outcome of intrauterine insemination treatment: an analysis of 8893 cycles

Liu

Wang

et al. 2016

BJOG

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Objective To evaluate the impact of ovarian stimulation on the outcome of intrauterine insemination (IUI). Design Retrospective analysis. Setting A single university‐based centre. Population A total of 5109 couples with 8893 cycles. Methods The outcome of IUI with different protocols for ovarian stimulation was examined. Main outcome measures The live birth rate (LBR), twin pregnancy rate and ovarian hyperstimulation syndrome (OHSS). Results In ovulatory women without ovarian stimulation, the LBR was 7.6%. Stimulation with clomifene citrate (CC), letrozole (LE), human menopausal gonadotrophin (HMG), CC or LE combined with HMG achieved LBRs of 6.1, 5, 7.9, 8 and 12.2%, respectively. LE combined with HMG achieved a significantly improved LBR compared with no stimulation. HMG stimulation was associated with a higher rate of twins (7.4%) than no stimulation (0%, P < 0.01). In ovulatory women, the LBR appeared lower in CC and LE compared with no stimulation (P > 0.05). In anovulatory women, ovarian stimulation with CC, LE, HMG, CC or LE combined with HMG achieved LBRs of 11.3, 5.1, 11.8, 12.6 and 13.6%, respectively. No significant difference was observed. There were no triplet pregnancies or OHSS in stimulated cycles. Conclusions In ovulatory women, ovarian stimulation with LE combined with HMG achieved a significantly improved live birth rate. HMG stimulation resulted in a high risk for twins. Tweetable abstract In ovulatory women, ovarian stimulation with letrozole and HMG resulted in a significantly improved LBR.

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Section: Discussioncontrasting

confidence: 69%

Section: Discussioncontrasting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

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The impact of ovarian stimulation on the outcome of intrauterine insemination treatment: an analysis of 8893 cycles

Liu

Wang

et al. 2016

BJOG

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“…There is only one accepted male semen analysis parameter, either from the pre or post processing analysis, which has been shown to be predictive of IUI outcome. That parameter total motile sperm count [2]. Data from the literature are contradictory and do not allow to identify clearly if other sperm parameters before or after semen treatment are associated with IUI success and which minimum threshold values should be accepted to use in daily practice in order to be able to establish a real guide of good practices for insemination [3].…”

Section: Introductionmentioning

confidence: 99%

Threshold Levels of Sperm Parameters Impacting on Pregnancy Rate in an Intrauterine Insemination Programme

Kdous¹

2015

JGO

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Abstract:Objectives. Intrauterine insemination (IUI) is frequently used as a first line strategy in the treatment of male and unexplained infertility. Threshold levels of sperm parameters associated with IUI success are controversial. The aim of this study was to evaluate the influence of sperm parameters on the outcome of IUI. Study Design. A prospective observational study. Patients and methods. This study included 295 IUI cycles. All IUI cycles were preceded by ovarian superovulation with clomiphene citrate 50 mg tablets orally twice daily for 5 days starting on the second day of menses and recombinant FSH 150 units IM on the 6th, 8th, and 10th day. Cycles were monitored by transvaginal ultrasound. The IUI was performed with a catheter 36 ± 4 hours after hCG injection. Sperm parameters before and after semen treatment for IUI were evaluated and correlated with pregnancy outcome. Results. A total of 29 clinical pregnancies were obtained, for a pregnancy rate per cycle of 9.83%. No pregnancy was obtained when less than one million spermatozoa were inseminated (p = 0.022). A statically significant increase in pregnancy rate was observed when normal sperm morphology was > 20% before semen treatment (p = 0.01) and> 25% after semen treatment (p = 0.034). NTSI (number of typical spermatozoa inseminated) and NTMSI (number of typical and mobile spermatozoa inseminated) significantly influence the clinical pregnancy rate. A 65% decrease in pregnancy rate was observed when the NTSI <2 106 compared to the NTSI ≥ 2 106 (5.16% vs 15%, p =0.004). Similarly, a 54% decrease in pregnancy rate was observed when the NTMS <106" compared to the NTMS ≥ 106 (6.12% vs 13.5%, p =0.026). Conclusion. IUI used for treating male factor infertility seems to have little chance of success when NMSI <1 × 106, NTSI < 2 million, NTMSI < 1 million. If theses thresholds cannot be obtained, IVF should be recommended.

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“…This study identifies that the total dose of gonadotropin is positively correlated with the pregnancy rate. Moreover, other factors contributing to failure or success of IUI outcome according to this study's findings include semen volume, male age, sperm normal and amorphous morphology, duration of the marriage, and endometrial thickness which some of them have been considered as the influential attributes in some previous similar studies [42][43][44].…”

Section: Discussionmentioning

confidence: 85%

CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

Ranjbari

Khatibi

Dizaj

et al. 2020

Preprint

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Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue with which the assisted reproductive technology (ART) practitioners are dealing. 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. The large number of studies that have been focused on predicting the IVF and ICSI outcome by 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 develop an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking of the most significant features, based on patients' cycle’s characteristics under IUI treatment.Methods: For this purpose, a novel hybrid 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 for 8,360 couples who underwent 11,255 IUI cycles were included. Results: Experimental results show that the proposed method outperforms the compared methods with 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 BMI.

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The evaluation of pre and post processing semen analysis parameters at the time of intrauterine insemination in couples diagnosed with male factor infertility and pregnancy rates based on stimulation agent. A retrospective cohort study

Cited by 36 publications

References 13 publications

The impact of ovarian stimulation on the outcome of intrauterine insemination treatment: an analysis of 8893 cycles

The impact of ovarian stimulation on the outcome of intrauterine insemination treatment: an analysis of 8893 cycles

Threshold Levels of Sperm Parameters Impacting on Pregnancy Rate in an Intrauterine Insemination Programme

CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

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