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ORIGINAL ARTICLE
Year : 2014  |  Volume : 1  |  Issue : 3  |  Page : 153-157

What are the best semen parameters to predict pregnancy in intrauterine insemination cycles?


Department of Obstetrics and Gynecology, McGill University, Montreal, QC, Canada

Date of Web Publication7-Oct-2014

Correspondence Address:
Dr. Einat Shalom-Paz
687 Pine Avenue West, Montreal, Quebec H3A 1A1
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2348-2907.142332

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  Abstract 

Background/Aim: Intrauterine insemination (IUI) is widely used for sub-fertile couples. The optimal method of evaluating semen analysis as a predictor of pregnancy is still not concluded. The aim of this study was to determine the best semen analysis parameter to predict pregnancy in IUI treatments and to evaluate when the sperm is insufficient to IUI, and the couple has to proceed with IVF. Materials and Methods: We evaluated all patients who underwent ovulation induction with IUI. The data were analyzed in different combinations to build the best model. Results: The semen analysis parameters that were found as the best predictors for pregnancy include volume, concentration, motility and morphology from the sperm analysis during the infertility evaluation. A cut-off of total motile normal morphology sperm count (TMNC) <4.8 × 10 6 from these parameters yielded very low probability of pregnancy and in those cases it is recommended to refer patients to IVF. Conclusion: The model includes the normal morphology in the formula of total motile sperm count more accurately and significantly predicts pregnancy rate than the data of sperm in the day of IUI. Semen analysis results with TMNC <4.8 × 10 6 should be considered as a threshold for referral to IVF.

Keywords: Post wash sperm, pregnancy rate, pre wash sperm, sperm analysis, sperm morphology, total motile sperm count


How to cite this article:
Wiser A, Herrero B, Hyman J, Reinblatt S, Shalom-Paz E. What are the best semen parameters to predict pregnancy in intrauterine insemination cycles?. IVF Lite 2014;1:153-7

How to cite this URL:
Wiser A, Herrero B, Hyman J, Reinblatt S, Shalom-Paz E. What are the best semen parameters to predict pregnancy in intrauterine insemination cycles?. IVF Lite [serial online] 2014 [cited 2022 Jan 18];1:153-7. Available from: http://www.ivflite.org/text.asp?2014/1/3/153/142332


  Introduction Top


Intrauterine insemination (IUI) is widely used for sub-fertile couples and is often proposed as initial treatment for couples with unexplained infertility, as well as those with mild or moderate male factor. [1],[2] IUI is combined with ovulation induction (OI) treatment in order to improve insemination timing accuracy, and increase the availability of sperm to the oocyte. It is a simple, inexpensive procedure, and offers a considerable improvement in pregnancy rate. [3] Several different factors have been reported as influencing pregnancy rates with IUI. Female factors and male factors contribute concomitantly.

Male infertility evaluation includes mainly the semen parameters; number (concentration) of spermatozoa per ml, sperm motility and the morphology. There are conflicting studies regarding the optimal method of evaluating semen analysis as a predictor of pregnancy. Some studies suggest that parameters of raw semen samples, prewash, do not seem to correlate well with cycle fecundity and that the post wash specimen may provide a superior predictor of pregnancy. [4],[5] Others claim that total motile sperm count (TMC) in the prewash sample on the day of insemination may serve as a good predictor of sperm quality. [5],[6] Nevertheless, analysis of postpreparation semen parameters can provide useful prognostic information for women undergoing IUI. The total motile progressive count after sperm preparation (postwash TMC) was proposed as a reliable criterion to determine when to conduct ICSI in IVF cycles. [7],[8],[9]

The main aim of this study was to identify the best semen analysis parameters to predict pregnancy during OI and IUI treatment. A secondary aim was to define a cut-off value to help with the decision when to proceed with IVF.


  Materials and Methods Top


We retrospectively reviewed the medical records of infertile couples that underwent OI/IUI at our institution between 2009 and 2010. We included patients who were diagnosed with Polycystic Ovarian Syndrome (PCOS), unexplained infertility and very mild male factor. PCOS was defined according to the Rotterdam criteria of ESHRE/ASRM. [10] Unexplained infertility was defined as the absence of an established cause of a couple's failure to achieve pregnancy after 12 months of attempting conception, after a thorough evaluation including documentation of ovulation, tubal patency, normal uterine cavity and normal semen analysis. Mild male infertility was defined as <50% reduction in any parameter of semen analysis.

During infertility evaluation, all women underwent baseline serum testing of Follicle-stimulating hormone (FSH), luteinizing hormone (LH) Estradiol (E 2 ) as well as antral follicle count (AFC) assessment on the day 3-5 of their cycle. All males had at least one semen analysis, which was performed according to the guidelines of the World Health Organization. [11]

The patients were evaluated by their physician after completing the investigation workup, and an infertility diagnosis was applied when appropriate.

Ovulation induction and intrauterine insemination treatments

Ovulation induction treatment included either oral clomiphene citrate at a dose of 50-100 mg daily from day 3 to 7 of the cycle or gonadotropin (FSH or hMG) subcutaneously at a dose of 75-300 IU daily starting on day 3 of the cycle. The dose was based on AFC, age or knowledge of prior response. Human chorionic gonadotropin (hCG 10,000 IU) was administered when the leading follicle reached ≥18 mm in diameter. A single IUI was performed 36 h later.

Serum β-human chorionic gonadotropin (β-hCG) was measured 14 days after IUI. In women with positive β-hCG, transvaginal ultrasound was performed 3 weeks later to confirm pregnancy viability. Clinical pregnancy was defined as the presence of an intrauterine gestational sac with fetal heart activity.

Semen preparation for Intrauterine insemination

The semen sample was obtained on the day of insemination. After liquification, the volume, concentration, and motility were determined, and the TMSC was calculated. Sperm preparation included treatment with the sperm wash medium (Ferticult Flushing, FertiPro N.V., Belgium), followed by separation on a discontinuous density gradient (80:40% PureSperm 100, Nidacon International A B, Sweden). The policy of our unit is to re-evaluate each semen sample after the preparation process, and to calculate the postwash sperm count after gradient. After an additional wash, the sperm pellet was suspended in 500 μl insemination medium, and the postwash and gradient TMSC were re-evaluated in the final sperm suspension.

Demographic data

Demographic data were recorded including age, diagnosis of infertility, basal hormonal levels and AFC. We also evaluated the number of follicles and endometrial thickness on the day of ovulation triggering, and the pregnancy outcome in those who conceived. The primary semen analysis from the initial infertility evaluation was recorded as well as the concentration and motility of the sperm on the day of IUI pre- and post-wash.

Statistics

All data was analyzed in different combinations to build the best model. For the model calculation, we considered sperm analysis data that was performed during the infertility evaluation and sperm sample parameters on the day of insemination pre and post wash. We incorporated female factors such as FSH, LH, E 2 , AFC, age, endometrial thickness and number of a mature follicle in the logistic regression analysis. The entire diverse factors were used in different combinations that might influence pregnancy outcome.

Logistic regression was performed using R statistical software. The background factors (covariates) considered were age and AFC, E 2 , and FSH. Missing data were imputed by sequential imputation using linear regression. Preliminary model selection was performed by comparing P values obtained from the t-test and from ANOVA and by comparing the AIC (Aikake Information Criterion, which is essentially on a penalized likelihood function) for the logistic regression models. The best models from the above analysis were subsequently tested using more rigorous methods of leave-one-out cross-validation with Monte Carlo simulation (M = 1000), with the statistics of comparison being positive predictive value (PPV), misclassification rates, and log-likelihood. PPV equal the number of correct positive predictions divided by the total number of positive predictions. We sought the cut-off value of TMC, which provided the most significant determinant of pregnancy rate in that group, the threshold values between "high" and "low" for the categorical inputs were found by grid search.


  Results Top


326 couples, who underwent 601 treatment cycles, were evaluated. 73.1% (238) of our patients were diagnosed with unexplained infertility; 22.3% (73) had PCOS, and the remainder 4.6% (15) had very mild male factor infertility. The pregnancy rate was significantly higher for the PCOS subgroup compared with the overall pregnancy rate (15.7% vs. 10.5%, P = 0.02).

We built three models [Table 1] based on different alternatives for sperm analysis: M1 includes parameters of the initial sperm sample on the day of the primary semen analysis during the infertility workup. M2 was based on the crude sperm parameters on the day of insemination (before processing), prewash concentration and motility. Model M3 included the post wash sperm parameters, concentration, and motility. The results are presented in [Table 1]. We found that the best model to predict pregnancy rate by male semen characteristics is model M1; this model included all basic sperm characteristics: Sperm concentration (million/ml) × volume (ml) × motility (% as a decimal fraction) × morphology (% as a decimal fraction).
Table 1: Fit values for models to predict pregnancy

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Comparing this model to the total motile count without morphology, we found that by including the morphology in the model, we increased the level of significance from 2.3% to 14%. We defined model M1 as total motile normal morphology sperm count (TMNC). This model was found to be applicable to all different etiologies of patients in our study (unexplained, mild male factor, as well as PCOS,).

According to our final model, we detected the best cut-off values for TMNC. TMNC of 4.8 × 10 6 was found to be the cut-off to predict pregnancy [Table 2]. The pregnancy rate below and above this cut-off was 1.8% and 14.8%, respectively; P = 0.007. We found that there is a significant improvement of 1.14; P = 0.00013 in the probability of success (pregnancy) below and above the cut-off.
Table 2: Semen characteristics according to cut-off value

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The female baseline characteristics were comparable in the two subgroups of TMNC below and above the cut-off. [Table 3] demonstrates female's characteristics in the two subgroups.
Table 3: Female's characteristics according to semen cut-off

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  Discussion Top


In this study, we report the influence of different semen analysis parameters on pregnancy rate. We compared parameters from the initial semen analysis during primary evaluation, prewash on the day of insemination and postwash on the day of insemination. We demonstrated that the most accurate model to predict pregnancy is the M1 model from the day of primary evaluation which includes the volume, concentration, motility and morphology. The above calculation which includes all four parameters improves the model accuracy and should be used as TMNC.

This study utilizes the widely used WHO classification system for semen evaluation. The impact of morphology alone on the success of IUI varies in the literature. [12],[13] We included the morphology as part of TMNC formula.

The importance of our study is the definition of a predictive threshold based on TMNC. Low TMNC below 4.8 × 10 6 yielded low probability of pregnancy-1.8% pregnancy rates versus high TMNC above 4.8 × 10 6 with 14.8% pregnancy rate. Our results imply that a minimum of 4. 8 × 10 6 TMNC is needed to achieve a higher probability of pregnancy in OI/IUI cycles. This is a simple, inexpensive tool, which can help guide the clinician's decision regarding the best infertility treatment for the patient. According to our data, we recommend that TMNC <4.8 × 10 6 /ml is an indication for IVF, which should be considered a superior treatment option for these couples. Importantly, this model can be extrapolated to all diagnoses of infertility including the anovulation, unexplained infertility and mild male factor.

To evaluate the effect of the female partner on the accuracy of the model we considered other factors such as FSH, LH, E2, AFC, women's age, endometrial thickness and number of a mature follicle into the logistic regression. None of the parameters mentioned improved the model fit.

Our results concur with several studies that discuss a minimum recommended concentration of motile sperm of 0.8-10 × 10 6 /ml as a threshold for IUI after sperm processing. [14],[15] Other studies reported progressive motility and TMC after sperm preparation to be important predictors of pregnancy. [16],[17] Some authors recommended directly referring couples to IVF treatment in case of <5×10 6 /ml motile sperm since in those couples the probability of achieving pregnancy with IUI is significantly lower. [18],[19] However, they did not include sperm morphology in their calculation.

Wiser et al. [20] demonstrated a better correlation between post gradient TMC and prediction of pregnancy compared to morphology alone. Their theory was that the morphology affects the post gradient TMC. However, fertilization process in IVF lab is a different process from IUI and in this study the best predictor for pregnancy after IUI was the model of the basal sperm with the morphology.

The importance of sperm morphology has been considered in other studies. Badawy et al. [19] discussed the importance of normal morphology sperm on the day of insemination. However, they did not combine the morphology results in the post wash TMC. Other studies have also reported the influence of normal morphology on pregnancy rates. In these studies found that the semen with <5% normal morphology diagnosed by tricked criteria yielded low postwash TMC and the fertilization rate in conventional IVF was low. [21],[22]

We believe that the morphology influences the motility of sperm and thus the TMC. Inclusion of sperm morphology in the calculation of TMC and preferring instead to TMNC better represents the availability of good quality sperm for insemination. This tool is more accurate and reflects the post wash TMC on the day of insemination.

A weakness of our study is that it is retrospective and that we did not evaluate the post wash sperm morphology in the IUI day. Another weakness is the use of old WHO classification, however further studies are needed to evaluate our date with the newer WHO classification from 2010.

The decision when to start or continue with IUI treatment or when to proceed with IVF is still not clear. Our study postulates a simple, comprehensive method which includes all parameters of sperm analysis in the prediction of pregnancy rate. This study presents a 1.14 higher pregnancy success rate when TMNC is above 4.8 × 10 6 . When the cut-off of semen analysis is less than this value IVF should be discussed with the couple.

 
  References Top

1.Cantineau AE, Cohlen BJ, Heineman MJ. Intra-uterine insemination versus fallopian tube sperm perfusion for non-tubal infertility. Cochrane Database Syst Rev 2009; 15:CD001502.  Back to cited text no. 1
    
2.Brandes M, Hamilton CJ, Bergevoet KA, de Bruin JP, Nelen WL, Kremer JA. Origin of multiple pregnancies in a subfertile population. Acta Obstet Gynecol Scand 2010;89:1149-54.  Back to cited text no. 2
    
3.Zhao Y, Vlahos N, Wyncott D, Petrella C, Garcia J, Zacur H, et al. Impact of semen characteristics on the success of intrauterine insemination. J Assist Reprod Genet 2004;21:143-8.  Back to cited text no. 3
    
4.Horvath PM, Bohrer M, Shelden RM, Kemmann E. The relationship of sperm parameters to cycle fecundity in superovulated women undergoing intrauterine insemination. Fertil Steril 1989;52:288-94.  Back to cited text no. 4
    
5.Schulte RT, Keller LM, Hiner MR, Ohl DA, Smith GD. Temporal decreases in sperm motility: Which patients should have motility checked at both 1 and 2 hours after collection? J Androl 2008;29:558-63.  Back to cited text no. 5
    
6.Kastrop PM, Weima SM, Van Kooij RJ, Te Velde ER. Comparison between intracytoplasmic sperm injection and in-vitro fertilization (IVF) with high insemination concentration after total fertilization failure in a previous IVF attempt. Hum Reprod 1999;14:65-9.  Back to cited text no. 6
    
7.Verheyen G, Tournaye H, Staessen C, De Vos A, Vandervorst M, Van Steirteghem A. Controlled comparison of conventional in-vitro fertilization and intracytoplasmic sperm injection in patients with asthenozoospermia. Hum Reprod 1999;14:2313-9.  Back to cited text no. 7
    
8.Rhemrev JP, Lens JW, McDonnell J, Schoemaker J, Vermeiden JP. The postwash total progressively motile sperm cell count is a reliable predictor of total fertilization failure during in vitro fertilization treatment. Fertil Steril 2001;76:884-91.  Back to cited text no. 8
    
9.van Weert JM, Repping S, van der Steeg JW, Steures P, van der Veen F, Mol BW. A prediction model for ongoing pregnancy after in vitro fertilization in couples with male subfertility. J Reprod Med 2008;53:250-6.  Back to cited text no. 9
    
10.Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil Steril 2004;81:19-25.  Back to cited text no. 10
    
11.World Health Organization. WHO Laboratory Manuals for the Examination of Human Semen and Sperm-cervical Mucus Interaction. Cambridge, MA: Cambridge University Press; 1992.  Back to cited text no. 11
    
12.Dickey RP, Pyrzak R, Lu PY, Taylor SN, Rye PH. Comparison of the sperm quality necessary for successful intrauterine insemination with World Health Organization threshold values for normal sperm. Fertil Steril 1999;71:684-9.  Back to cited text no. 12
    
13.Matorras R, Corcóstegui B, Perez C, Mandiola M, Mendoza R, Rodríguez-Escudero FJ. Sperm morphology analysis (strict criteria) in male infertility is not a prognostic factor in intrauterine insemination with husband's sperm. Fertil Steril 1995;63:608-11.  Back to cited text no. 13
    
14.Van Voorhis BJ, Barnett M, Sparks AE, Syrop CH, Rosenthal G, Dawson J. Effect of the total motile sperm count on the efficacy and cost-effectiveness of intrauterine insemination and in vitro fertilization. Fertil Steril 2001;75:661-8.  Back to cited text no. 14
    
15.Wainer R, Albert M, Dorion A, Bailly M, Bergère M, Lombroso R, et al. Influence of the number of motile spermatozoa inseminated and of their morphology on the success of intrauterine insemination. Hum Reprod 2004;19:2060-5.  Back to cited text no. 15
    
16.Campana A, Sakkas D, Stalberg A, Bianchi PG, Comte I, Pache T, et al. Intrauterine insemination: Evaluation of the results according to the woman's age, sperm quality, total sperm count per insemination and life table analysis. Hum Reprod 1996;11:732-6.  Back to cited text no. 16
    
17.Arny M, Quagliarello J. Semen quality before and after processing by a swim-up method: Relationship to outcome of intrauterine insemination. Fertil Steril 1987;48:643-8.  Back to cited text no. 17
    
18.Miller DC, Hollenbeck BK, Smith GD, Randolph JF, Christman GM, Smith YR, et al. Processed total motile sperm count correlates with pregnancy outcome after intrauterine insemination. Urology 2002;60:497-501.  Back to cited text no. 18
    
19.Badawy A, Elnashar A, Eltotongy M. Effect of sperm morphology and number on success of intrauterine insemination. Fertil Steril 2009;91:777-81.  Back to cited text no. 19
    
20.Wiser A, Ghetler Y, Gonen O, Piura E, Berkovits A, Itskovich A, et al. Re-evaluation of post-wash sperm is a helpful tool in the decision to perform in vitro fertilisation or intracytoplasmic sperm injection. Andrologia 2012;44:73-7.  Back to cited text no. 20
    
21.Grow DR, Oehninger S, Seltman HJ, Toner JP, Swanson RJ, Kruger TF, et al. Sperm morphology as diagnosed by strict criteria: Probing the impact of teratozoospermia on fertilization rate and pregnancy outcome in a large in vitro fertilization population. Fertil Steril 1994;62:559-67.  Back to cited text no. 21
    
22.Hall JA, Fishel SB, Timson JA, Dowell K, Klentzeris LD. Human sperm morphology evaluation pre-and post-Percoll gradient centrifugation. Hum Reprod 1995;10:342-6.  Back to cited text no. 22
    

 
  Authors Top


Dr. Shalom-Paz Einat obtained her MD at the Technion Faculty of Medicine in Haifa and her specialty in obstetrics and gynaecology in "Sapir Medical center", C'far-Saba in 2004. Since 2004 to 2008 , she has been working in IVF the unit of Hillel Yafe medical center, Hadera, Israel as a senior physician. From 2008 to 2012 she completed a research and a clinical fellowship at the McGill Reproductive Center, Montreal, Canada and worked as a senior physician and researcher. Her main scientific interests include ovarian reserve , IVF implantation failure and fertility preservation. At the present she is an attending physician at the IVF unit in Hillel Yafe medical center, Hadera, Israel.



 
 
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