JRI 
Vol. 17, Issue 2, / April-June 2016
(Systematic Review, pages 68-81)
PMID: 27141461 (PubMed) - PMCID: PMC4842237

Alireza Zarinara
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
Hojjat Zeraati
- Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, TUMS, Tehran, Iran
Koorosh Kamali
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
Kazem Mohammad
- Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, TUMS, Tehran, Iran
Parisa Shahnazari
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
Mohammad Mehdi Akhondi Corresponding Author
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran

Received: 7/6/2015 Accepted: 11/11/2015 - Publisher : Avicenna Research Institute

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Abstract

Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models.
Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated.
Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems.
Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable.



Keywords: ART, Infertility treatment, Prediction model, Treatment success


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Full Text

Introduction
Usually deficiency in reproductive system is known as a disease (1) and infertility has a negative concept in ancient myths and civilizations (2). Based on reports, average of 10-15% of couples are infertile (3-5) but this is reported up to 22% in Iran that is thinkable (6). In the past, infertility treatment has been done by treatment of reproductive system in male or female by management of chronic diseases such as diabetes or thyroid diseases (7, 8). In the past three decades, infertility treatment has been improved with advances in medical science. Sexually transmitted disease has decreased and level of public health has increased. Since 1970, ART treatments have opened a new view in infertility treatment and specialists` approach has changed from research to practice and fertility probability increased (1, 9, 10).
Take home baby is the aim of the couples (patients) and physicians choose the most simple and natural way to achieve the goal. Infertility has many causes in both or one of infertile partners and sometimes no specific reason can be found for the patient infertility (7). In these cases, treatment is difficult and ART should be used (11). Infertility treatment by many ART methods is invasive and sometimes has no successful result. ART procedures have the purpose to overcome unknown problems and they are expensive yet (12). In fifty percent of infertile couples, the problem relates to both partners and (13) thus cost effectiveness of treatment is very important for them (14-16). Infertility treatment should be repeated several times; however a few infertile couples achieve successful pregnancy with the first cycle. For these subjects, the cost is more important. These costs usually are not covered by insurance and may not be efficient for couples. Infertility in most societies has a bad label for couples and the problems would be extended to family (17, 18).
Through the lens of genetic, immunologic, infection, endocrine and other factors that affect infertility, identifying the cause of infertility is very important. In these circumstances, having a prognosis for probability of pregnancy is helpful, thus physicians and patients anxiously look for a measure of treatment success (19, 20). They search for an estimation that guides them for decision making about treatment. Type of disease and the characteristics of couples and approach to treatment determine the costs (13, 21, 22).
Infertility treatment has been carried out in Iran during recent decades and physicians and patients are interested in predicting the success in infertility treatment. A good prediction of treatment success must be done by regional (aborigine) model. Two favorable events may occur as a result of providing an acceptable prediction of treatment success rate in an infertile couple. First, the couple will have a cost-benefit estimate and can decide for their future accordingly. Second, the physician can choose the best and the most cost-effective option, depending on circumstances of the patient. It is hugely important for physician to make the right decision to shorten time and avoid complications that may physically harm the patient (23).
In the past three decades, new approaches have been used in infertility treatment. Laboratory techniques for saving and freezing sperm, oocyte and embryo have created a new arena (13, 24). Physicians usually have challenges for estimating the success of infertility treatment and prediction models can efficiently help them (25). Recently, prediction models for infertility treatment success have been proposed in Europe and America. Therefore, prediction of treatment success is a new field in infertility treatment (26-30).
Different therapeutic methods have been compared in different studies such as clinical trials, and prediction models have been designed for them in various studies; mostly based on linear and logistic regression analysis, Cox regression, and other statistical methods (7, 25, 31-34). This paper was a systematic review of effective factors and models in this respect. Therefore, the purpose was to recognize the models and their effective factors in this field which empower us to be careful in applying them.
Prediction models: There are various methods for prediction based on statistics or neural networks (35). It has not been a long time since prediction models for assisted reproductive techniques were produced and applied. Infertility treatment success has been predicted since 1987, when Varmain predicted pregnancy outcome of infertility treatment (36). This was followed by publication of articles by Collins and Hull, that investigating the issue from their own particular angle (37, 38). For clinical application of these models, first, their reliability and validity have to be assessed and necessary modifications have to be made. Using an incorrect prediction may have adverse consequences for both physician and couples (patients) (9). Thus of the first, the most appropriate model should be chosen and used after theoretical assessments. Second, the model should undergo rigorous evaluation to enable its use with confidence (39). Importantly, results from using these models should be regularly recorded and assessed to provide feedback for ongoing modification and completion (9, 24). With the right information, it is possible to provide an acceptable prediction, using reproductive prediction models. These models use statistics and results from past procedures to provide prediction through probability analysis (24).

Methods
In this review study, articles on prediction models and predicting factors of successful infertility treatments were examined. Search was based on Pubmed, Pubmed central, SCOPUS, EMBASE, Cochrane library and Ovid database.
A particular timeframe was not specified for the search, but only English articles and articles with English abstracts were included. Keywords were selected from the terminology approved by the International Committee of Monitoring ART and World Health Organization Revised Glossary of ART.
Similarity of the article’s title and its abstract with our study aims was the main criteria for selection of article.
To complete the search and avoid missing out articles by scholars (distinguished researchers), their articles were identified in the references, and their names were searched independently to find and review their articles. Articles meeting study inclusion criteria were selected and added to those previously chosen.
Next, abstracts from selected articles were carefully read and a table was drawn containing data extracted from these abstracts, which was assessed by the group according to "eligibility evaluation". After refining titles, doubts about their inclusion in the first stage of study were overruled by reviewing abstracts. Rejection of articles was approved by the senior researcher, after reviewing them.
Final table of data containing features from these articles was prepared. Disagreements about final selection of articles were resolved through group discussions, and final decision was made by the senior researcher. Articles that contained information on prediction models for IUI-ICSI-IVF techniques, treatment success rates, and factors affecting prediction were included in the study. Figure 1 displays article selection and refinement sequence. Article selection, elimination, and quality criteria are presented in table 1 (9, 40).

Results
Of the 121 articles identified, 4 were excluded according to inclusion criteria (systematic review and assessment of other articles). Ultimately, 81 articles were selected. Selected articles had been produced in 20 reference countries and covered information after 1970. Statistical population mostly studied consisted of patients attending fertility treatment centers or university teaching hospitals. In some cases, study population comprised patients from several centers in a region, or a country. Table 2 shows number of studies based on time of development of prediction model or factors affecting prediction.
Sample size in these studies is shown in table 3. In some articles, both number of couples and number of cycles have been studied.
In nearly all studies, samples were selected from eligible patients that attended hospitals and entered treatment cycle (random and convenient sampling). Design and structure of studies are summarized in table 4. The approach of articles toward factors affecting infertility treatment and prediction models of successful treatment is presented in table 5 (41).
Sixteen articles were concerned with influential factors or development of prediction model without medical intervention, including general treatment and assisted reproductive treatment, and 59 articles discussed influential factors or prediction models together with treatment. Twenty-two articles studied IVF treatment by development of a model, and 24 articles investigated factors affecting success of IVF treatment. Three articles addressed development of a model for IUI treatment technique, and 7 articles investigated factors affecting this technique. Eight articles were developed for external validation of various models. Table 6 presents decisive factors (predictors) in predicting treatment success, based on review of articles. Statistical analyses performed in these articles are listed in table 7. Outcomes in reviewed articles are shown in table 8.

Discussion
Designing of prediction models has a distinct methodology and various articles have been presented on prediction models for successful infertility treatment (81). Research presented in these articles leads to designing a model for predicting the outcome of a diagnostic or medical procedure. Review of articles indicates that this is an important debate in Europe and the U.S., but not so serious in Asian countries, which provides an opportunity for work in this area.
The results indicate that designing models to predict success in infertility treatment is a new area, and thus, there are widely different views on production of these models. For instance, most studies discuss the effect of one factor as the predictor of treatment outcome or diagnosis. Meanwhile, some articles are concerned with designing a model. Some articles are concerned with examining previous models, and others explain modifications of their own model (9, 82, 83).
In this study, comparison of articles shows that there are generally two approaches: predicting success of infertility treatment without medical intervention, which is usually about patients with no treatment history, but with over two years of infertility history, and predicting success of infertility treatment with medical intervention, and also measurement of the effect of one or more factors on fertility. Articles that discuss predicting treatment success before using assisted reproductive techniques, base their work on a few recommended models. These articles rather focus on factors affecting fertility, and are not much concerned with the development of a new model. A glance at table 2 reveals that there is greater interest in this group of studies, given multitude of models and studies conducted on them and influential factors in assisted reproductive treatments.
Articles that address influential factors in the stage before assisted reproductive intervention, review these factors in 4 groups of medication effect, hormonal effect, possibility of natural pregnancy during the time, and the effect of test or diagnostic intervention.
Articles that study success of assisted reproductive treatments examine influential factors in 6 groups of medication effect, hormonal effect, further treatment attempts (IUI or IVF/ICSI), the effect of performing a diagnostic or medical test, the effect of a disease or a particular cause (53, 56, 75).
Given the number of factors affecting female fertility, more effective factors have been found in studies on women, which seems to be logical, because infertility problems in women have greater number of causes than in men, which explains such results. Higher prevalence of infertility among women has been previously reported. However, this inequality has been questioned in recent studies on prevalence of infertility among men (84-104). Table 6 shows that the prevalence of infertility factors has drawn interest of various studies in both sexual partners (7, 105).
The important point is that whether the proposed model has been properly developed, and whether it possesses required adequacy in terms of standards (106, 107). Some articles rate prediction performance of the studied model good and some poor (44, 47, 108-110). This shows how aware the designer had been and if he had tested his model with respect to objectives of the model.
Fertility treatment centers usually attend to patients' requests. Although diversity of services provided may vary across these centers, services with greater demands are normally provided. Larger centers provide more specialized and complex services (14, 111, 112). Table 5 shows a variety of sample sizes in different studies. This table also reveals that, given the number of subjects, there is usually sufficient sample size for research, and that researchers have no limitations in this regard (113).
Prospective studies are highly valuable in terms of planning to obtain desired and reliable results and appropriate controls (114). Prospective studies are better performed because infertile couples voluntarily and enthusiastically follow up their treatment results. Table 4 shows that 43 studies were designed and conducted prospectively. In articles attempting to develop models, compliance with requirements for development of a prediction model was not found. However, no case was found to violate these conditions either. Thus, it is expected that authors would have observed relevant conditions with the knowledge of requirements of development of a model.
Given the standards of developing a prediction model, clarity in definition of output, treatment or intervention, and outcome is among requirements when designing a model, which was observed in reviewed articles. Moreover, during research, practical physicians should have no knowledge of predictor or diagnostic factors, and diagnostic criteria should not be part of factors affecting prediction (24). In studies performed, these assumptions seem to have been observed. Furthermore, these assumptions can be implemented in a variety of studies (Table 4).
Excluding unexplained infertility cases in men and women, the list of factors affecting treatment success (Table 6) shows that many factors have been studied, but some have greater impact on infertility treatment and modeling to predict success of treatment, including woman's age, duration of infertility of couples, history of pelvic surgery, and tubal factors or male factors associated with sperm quality (73, 74, 115). A variety of predictors of successful infertility treatment was found, with diverse factors, depending on treatment method and researcher's choice, and even unusual factors such as regular drinking coffee. For example, it has recently been proposed that men's age also plays an effective role in treatment success. However, this has not been taken seriously, and has not been entered as a factor in models yet (116).
With respect to fertility incidence rate, it should be noted that spontaneous pregnancy is different from pregnancy after treatment. There are at least two stages in infertility treatment. The first stage involves ensuring ongoing infertility and diagnosing its cause, followed by initial treatments to resolve the problem or couples' failure. Failing that leads to the second stage involves use of assisted reproductive treatments, including IVF, IUI, and ICSI. Fertility prediction models before treatment are treatment-independent. Couples take part in these models before starting any treatment. These models usually predict ongoing pregnancies. Successful treatment before ART is predicted by treatment-independent models. Fertility success after assisted treatment, which includes ART, is evaluated by success rate or probability of success (9). Of the 81 articles reviewed, 56 prediction articles were ART-dependent (Table 2).
Researchers sought answer to the question "Has modeling been the main aim in these studies?". Twenty-five articles were concerned about development or review of a prediction model, and 32 aimed to find factors affecting prediction of successful treatment. Since designing a prediction model requires certain subtleties, it seems a study that has not been designed for modeling, and aims to find effective factors, cannot provide an appropriate model. Furthermore, focus on finding influential factors diverts attention from designing a predictor model. This applied to 23 articles.
In most cases, in prediction models for successful treatment, factors affecting infertility treatment are found to be statistically significant, and they are entered in the model and thus become influential. Meanwhile, their influence may not be clinically important or beneficial, and may divert prediction path of the model. Conversely, effective factors in a study may not be statistically significant, yet they may be clinically important and beneficial. Hence, they should be entered in prediction model, and their effect should be implemented. Clinical and functional perspective plays a definite role in its application and in exploitation of results in designing a prediction model and developing its effective factors.
The important issue is that assumptions or principles should be considered in designing a prediction model. This relates to application of a model and a study population. Although these are clearly defined at the beginning, they are very important and influential at this stage (117). This study shows that in many studies, the researcher designed and conducted his study with the aim to find variables affecting success of treatment, and proceeded to present a model based on the same data and significant results with specific P-values. This process contains two basic problems: first, it is possible that the researcher may not have chosen the right variables to assess the effect on success of treatment, and second, given the attention paid to significance of influential factors, the final proposed model may not have sufficient rigor or efficiency. It is important to note that significance of variables alone is not sufficient reason for fitness of a model.
Critical appraisal of predictive models shows the necessity for analysis of the statistical part of these models (9). To enter predictor factor, predictive models should have P-values between 5% and 10%. It may be defensible to use lesser values, which may cause greater discrimination power in the model. But, before a model can be used, external validation should be performed in several centers (117).
The important question is "Has dependent variable (treatment success) been defined clearly and accurately?". Different definitions of treatment success have been presented in various studies. Not only are these definitions substantially different from one another (12 week fetus, live birth, or ...), in parallel with one another, they can cause fundamental differences in estimates. Furthermore, the number of attempts for treatment success should be defined, and whether or not a limit has been considered for attempts. Exploring this issue shows that one article has defined a limit for attempts.
Some models are concerned with assisted reproductive treatments, and are designed according to live birth (78, 118). Nearly all articles reviewed have identified and defined their expected results, which is a requirement in model design, and accordingly, it can be decided if the researcher reached his goal or not (81, 119). In most studies, success has been defined on the basis of live birth or ongoing pregnancy, and in others, according to producing clinical pregnancy or biochemical pregnancy by βHCG. Each has its own particular point. Table 8 presents definitions associated with these results. The important issue is that definition of outcome has a significant impact on determining objective; it is also a clear criterion for internal and external evaluations of the model (24, 39).
A model works well when its variables control dependent variable (treatment success) at a high level of variance. Choice of influential variables on treatment success is an important point in designing a model (119). Hence, it should be clear which variables are used, why they are used, and whether main variables are all present in the final model. In reviewed studies, no percentage is proposed as prediction power of factors affecting treatment success.
Linearity of independent variables (predictors) should be controlled at the outset, and their interactive effect should be considered. Success and factors affecting success should be assessed for study population with repeated treatment attempts. Importantly, if repeated treatment is considered in this analysis, then appropriate analysis should also be considered, and data should be collected in such a way to show frequency of treatment attempts from the beginning to the end of study. Unless the right model, the right effective factors, and coefficients are obtained through appropriate statistical analysis, problem with the model and its coefficients will remain. None of the reviewed articles provided an explanation for this. Thus, a proper judgment cannot be made.
In articles reviewed, 8 articles assessed prediction model for treatment success in other centers (8 articles explained external validation results), and 5 focused on fertility predicting models before assisted reproductive treatments (Table 5) and showed that these models had been tested systematically and according to validation principles. However, results of articles show that a model cannot be as easily applied in other centers as in previous one. Such results are to be expected because designing and validation principles in prediction models depend on a variety of functions. Regarding validation tests for models, articles merely cited compliance with standards, indicating whether or not model has necessary validation, discrimination, or calibration in a new setting. Although these criteria are important, it should be noted that structure of target population should be the same in the center or place that designed the model and validation center. It is hugely important that in terms of target population, these two centers should be as similar as possible. Otherwise, a proper model validation result (especially, external validation) will not be obtained (24, 39). In reviewed studies, populations in new settings and the results obtained are cited, and researchers discussed weaknesses in some of these models (82). In a study, the model that was adopted from the Netherlands, was validated and used in New Zealand, and somewhat met expectations. It was also cited that necessary modifications and completion should be implemented at validation stage. It seems researchers have realized the necessity to implement calculated changes for testing a model in a new setting to obtain results, and that review of a model should be performed carefully and accurately (120).
There is greater diversity of factors affecting IVF results compared to IUI. In IVF, laboratory and female factors play an important role, and influential factors are known. Twenty-four articles addressed factors affecting IVF success, and the effect on outcome and prediction of success was investigated in 5 groups that appeared more important than the rest, including medication effect, hormones effect, second IVF attempt, the effect of diagnostic (or medical) test, the effect of diseases and factors affecting it. Among factors studied, opportunity for reducing stress and relationship training increases chances of fertility (66). Positive role of medication and laparoscopy is recognized in some studies, but not implemented in models (121).
29 reviewed articles rather focused on the role of laboratory factors (34). With respect to predicting IVF success, attention has been drawn to various aspects of embryo quality and avoiding frequent embryo transfer and replacement in the uterus (60-62, 122, 123). In this method, some influential factors have greater importance, and their role has been proven, but others remain controversial. For example, HFEA center in England does not allow transfer of more than 3 embryos in each cycle for those older than 35 years, and donor must be younger than 35 years. Given advances and new care methods, multiple births are reduced. This should be debated by specialists in scientific groups (8). Technological advances in laboratory fields are certainly influential.

Conclusion
Infertility treatment has been done based on chronic diseases or ovulation or status of spermatogenesis and prediction models for treatment success have been designed according to their effective factors (124, 125). Designing the prediction models have led to effective factors of new ART treatments or laboratory and surgery factors (45). Now, there are more effective factors that may be related together or they may be separate from each other (23, 118). Hence, designed models for infertility treatment success are specialized and have focused on ART treatment effective factors. As a result, there are some prediction models for each treatment (40). It seems that according to the definition of pregnancy (treatment successful), woman age, infertility duration, kind of infertility, sperm quality and pelvic surgery are important effective factors before ART treatment. In addition to the above, treatment method, basal FSH of serum, number of retrieved oocytes, number of transferred embryos and quality of embryos are important factors for prediction of ART treatment success (40, 126). It should also be noted that the study population and the environmental factors for their big impact on the factors affecting the success of treatment should be considered as well (7, 45, 46).
Prediction models for treatment success are clinical models, and their applicability and proper performance in different conditions and in new settings are very important. The main feature of these models is compliance and accuracy of prediction estimates. This feature leads to clinicians’ greater use of the model with confidence. This gradually leads to further recognition of prediction factors, and updating and applicability of the model. Ongoing updates of the model gradually expose defects. This process leads to implementation and trust to the use of the model (39, 107).
In this study, the main objective was to provide initial information needed for designing a model to predict infertility treatment success (in Avicenna Research Institute). The following were obtained in this study; list of influential and predicting factors in infertility treatment success and extent of their influence, examples of models developed before ART, and models for predicting success of IVF, IUI, ICSI.

Acknowledgement
This article is a section of Ph.D. thesis done by A. Zarinara at Avicenna Research Institute. Our appreciation goes to the staff and managers of this institute.

Conflict of Interest
No conflict of interest has been reported by the authors.




Figures, Charts, Tables


Figure 1. Selection and refinement of articles
Figure 1. Selection and refinement of articles



Table 1. Criteria for selection and quality of articles
Table 1. Criteria for selection and quality of articles



Table 2. Number of studies according to previous review and after assisted reproductive treatment




*Some cases are in common with IVF (6 in total)
Table 2. Number of studies according to previous review and after assisted reproductive treatment

*Some cases are in common with IVF (6 in total)



Table 3. Sample size in different studies
Table 3. Sample size in different studies



Table 4. Structure and design of studies
Table 4. Structure and design of studies



Table 5. Approach of articles toward prediction models for infertility treatment outcome, and factors affecting it (11, 34, 42-55)
Table 5. Approach of articles toward prediction models for infertility treatment outcome, and factors affecting it (11, 34, 42-55)



Table 6. Factors affecting the outcome of prediction models in different studies (53, 55-80)
Table 6. Factors affecting the outcome of prediction models in different studies (53, 55-80)



Table 7. Quantity and variety of statistical analysis in reviewed articles
Table 7. Quantity and variety of statistical analysis in reviewed articles



Table 8. Described outcomes in reviewed articles
Table 8. Described outcomes in reviewed articles


References

  1. Gurunath S, Pandian Z, Anderson RA, Bhattacharya S. Defining infertility--a systematic review of prevalence studies. Hum Reprod Update. 2011;17(5):575-88.   [PubMed]
  2. Behjati Ardakani Z, Akhondi MM, Mahmoodzadeh H, Hosseini SH. An evaluation of the historical importance of fertility and its reflection in ancient mythology. J Reprod Infertil. 2016;17(1):2-9.   [Abstract]
  3. Thoma ME, McLain AC, Louis JF, King RB, Trumble AC, Sundaram R, et al. Prevalence of infertility in the United States as estimated by the current duration approach and a traditional constructed approach. Fertil Steril. 2013;99(5):1324-31.e1.   [PubMed]
  4. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 2012;9(12):e1001356.   [PubMed]
  5. Thonneau P, Spira A. Prevalence of infertility: international data and problems of measurement. Eur J Obstet Gynecol Reprod Biol. 1991;38(1):43-52.   [PubMed]
  6. Akhondi MM, Kamali K, Ranjbar F, Shirzad M, Shafeghati S, Behjati Ardakani Z, et al. Prevalence of Primary Infertility in Iran in 2010. Iran J Public Health. 2013;42(12):1398-404.   [PubMed]
  7. Templeton A, Morris JK, Parslow W. Factors that affect outcome of in-vitro fertilisation treatment. Lancet. 1996;348(9039):1402-6.   [PubMed]
  8. Andrews M, Gibbons W, Oehninger S, Morshedi M, Mayer J, Jones H Jr, et al. Optimizing use of assisted reproduction. Am J Obstet Gynecol. 2003;189(2):327-32.   [PubMed]
  9. Leushuis E, van der Steeg JW, Steures P, Bossuyt PM, Eijkemans MJ, van der Veen F, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update. 2009;15(5):537-52.   [PubMed]
  10. Nardelli AA, Stafinski T, Motan T, Klein K, Menon D. Assisted reproductive technologies (ARTs): evaluation of evidence to support public policy development. Reprod Health. 2014;11(1):76.   [PubMed]
  11. Tan SL, Royston P, Campbell S, Jacobs HS, Betts J, Mason B, et al. Cumulative conception and livebirth rates after in-vitro fertilisation. Lancet. 1992;339(8806):1390-4.   [PubMed]
  12. Moolenaar LM, Vijgen SM, Hompes P, van der Veen F, Mol BW, Opmeer BC. Economic evaluation studies in reproductive medicine: a systematic review of methodologic quality. Fertil Steril. 2013;99(6):1689-94.   [PubMed]
  13. te Velde ER, Cohlen BJ. The management of infertility. N Engl J Med. 1999;340(3):224-6.   [PubMed]
  14. Gameiro S, Boivin J, Peronace L, Verhaak CM. Why do patients discontinue fertility treatment? A systematic review of reasons and predictors of discontinuation in fertility treatment. Hum Reprod Update. 2012;18(6):652-69.   [PubMed]
  15. Sadeghi MR. Unexplained infertility, the controversial matter in management of infertile couples. J Reprod Infertil. 2015;16(1):1-2.   [PubMed]
  16. Sharma V, Allgar V, Rajkhowa M. Factors influencing the cumulative conception rate and discontinuation of in vitro fertilization treatment for infertility. Fertil Steril. 2002;78(1):40-6.   [PubMed]
  17. Benyamini Y, Gozlan M, Kokia E. Variability in the difficulties experienced by women undergoing infertility treatments. Fertil Steril. 2005;83(2):275-83.   [PubMed]
  18. Ranjbar F, Behboodi-Moghadam Z, Borimnejad L, Ghaffari SR, Akhondi MM. Experiences of infertile women seeking assisted pregnancy in Iran: a qualitative study. J Reprod Infertil. 2015;16(4):221-8.   [Abstract]
  19. Chambers GM, Sullivan EA, Ishihara O, Chapman MG, Adamson GD. The economic impact of assisted reproductive technology: a review of selected developed countries. Fertil Steril. 2009;91(6):2281-94.   [Abstract]
  20. Akhondi MM, Binaafar S, Ardakani ZB, Kamali K, Kosari H, Ghorbani B. Aspects of psychosocial development in infertile versus fertile men. J Reprod Infertil. 2013;14(2):90-3.   [PubMed]
  21. Cooper GS. An analysis of the costs of infertility treatment. Am J Public Health. 1986;76(8):1018-9.   [PubMed]
  22. Uyar A, Ciray HN, Bener A, Bahceci M. 3P: Personalized pregnancy prediction in IVF treatment process. 8th ed. Berlin: Springer; 2009. p. 58-65.
  23. Hunault CC, Eijkemans MJ, Pieters MH, te Velde ER, Habbema JD, Fauser BC, et al. A prediction model for selecting patients undergoing in vitro fertilization for elective single embryo transfer. Fertil Steril. 2002;77(4):725-32.   [PubMed]
  24. Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules. Applications and methodological standards. N Engl J Med. 1985;313(13):793-9.   [PubMed]
  25. van der Steeg JW, Steures P, Eijkemans MJ, Habbema JD, Bossuyt PM, Hompes PG, et al. Do clinical prediction models improve concordance of treatment decisions in reproductive medicine? BJOG. 2006;113(7):825-31.   [PubMed]
  26. Boivin J, Bunting L, Collins JA, Nygren KG. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care. Hum Reprod. 2007;22(6):1506-12.   [PubMed]
  27. 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(4):250-6.   [PubMed]
  28. Verberg MF, Eijkemans MJ, Macklon NS, Heijnen EM, Fauser BC, Broekmans FJ. Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF. Hum Reprod. 2007;22(7):1919-24.   [PubMed]
  29. Carrera-Rotllan J, Estrada-Garcia L, Sarquella-Ventura J. Prediction of pregnancy in IVF cycles on the fourth day of ovarian stimulation. J Assist Reprod Genet. 2007;24(9):387-94.   [PubMed]
  30. Bouckaert A, Psalti I, Loumaye E, De Cooman S, Thomas K. The probability of a successful treatment of infertility by in-vitro fertilization. Hum Reprod. 1994;9(3):448-55.   [PubMed]
  31. Minaretzis D, Harris D, Alper MM, Mortola JF, Berger MJ, Power D. Multivariate analysis of factors predictive of successful live births in in vitro fertilization (IVF) suggests strategies to improve IVF outcome. J Assist Reprod Genet. 1998;15(6):365-71.   [PubMed]
  32. Commenges-Ducos M, Tricaud S, Papaxanthos-Roche A, Dallay D, Horovitz J, Commenges D. Modelling of the probability of success of the stages of in-vitro fertilization and embryo transfer: stimulation, fertilization and implantation. Hum Reprod. 1998;13(1):78-83.   [PubMed]
  33. Stolwijk AM, Zielhuis GA, Hamilton CJ, Straatman H, Hollanders JM, Goverde HJ, et al. Prognostic models for the probability of achieving an ongoing pregnancy after in-vitro fertilization and the importance of testing their predictive value. Hum Reprod. 1996;11(10):2298-303.   [PubMed]
  34. Cai QF, Wan F, Huang R, Zhang HW. Factors predicting the cumulative outcome of IVF/ICSI treatment: a multivariable analysis of 2450 patients. Hum Reprod. 2011;26(9):2532-40.   [PubMed]
  35. Steyerberg EW. Clinical Prediction Models: a practical approach to development validation and updating. 2nd ed. New Yourk: Springer; 2009. p. 300-10.
  36. Varma TR, Patel RH. Outcome of pregnancy following investigation and treatment of infertility. Int J Gynaecol Obstet. 1987;25(2):113-20.   [PubMed]
  37. Collins JA, Crosignani PG. Unexplained infertility: a review of diagnosis, prognosis, treatment efficacy and management. Int J Gynaecol Obstet. 1992;39(4):267-75.   [PubMed]
  38. Hull MG. Effectiveness of infertility treatments: choice and comparative analysis. Int J Gynaecol Obstet. 1994;47(2):99-108.   [PubMed]
  39. Toll DB, Janssen KJ, Vergouwe Y, Moons KG. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61(11):1085-94.   [PubMed]
  40. van Loendersloot LL, van Wely M, Limpens J, Bossuyt PM, Repping S, van der Veen F. Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Hum Reprod Update. 2010;16(6):577-89.   [PubMed]
  41. Stolwijk AM, Straatman H, Zielhuis GA, Jansen CA, Braat DD, van Dop PA, et al. External validation of prognostic models for ongoing pregnancy after in-vitro fertilization. Hum Reprod. 1998;13(12):3542-9.   [PubMed]
  42. Custers IM, Steures P, van der Steeg JW, van Dessel TJ, Bernardus RE, Bourdrez P, et al. External validation of a prediction model for an ongoing pregnancy after intrauterine insemination. Fertil Steril. 2007;88(2):425-31.   [PubMed]
  43. Hunault CC, te Velde ER, Weima SM, Macklon NS, Eijkemans MJ, Klinkert ER, et al. A case study of the applicability of a prediction model for the selection of patients undergoing in vitro fertilization for single embryo transfer in another center. Fertil Steril. 2007;87(6):1314-21.   [PubMed]
  44. Smeenk JM, Stolwijk AM, Kremer JA, Braat DD. External validation of the templeton model for predicting success after IVF. Hum Reprod. 2000;15(5):1065-8.   [PubMed]
  45. Erdem A, Erdem M, Atmaca S, Korucuoglu U, Karabacak O. Factors affecting live birth rate in intrauterine insemination cycles with recombinant gonadotrophin stimulation. Reprod Biomed Online. 2008;17(2):199-206.   [PubMed]
  46. Tomlinson MJ, Amissah-Arthur JB, Thompson KA, Kasraie JL, Bentick B. Prognostic indicators for intrauterine insemination (IUI): statistical model for IUI success. Hum Reprod. 1996;11(9):1892-6.   [PubMed]
  47. Steures P, van der Steeg JW, Mol BW, Eijkemans MJ, van der Veen F, Habbema JD, et al. Prediction of an ongoing pregnancy after intrauterine insemination. Fertil Steril. 2004;82(1):45-51.   [PubMed]
  48. Steures P, van der Steeg JW, Hompes PG, Habbema JD, Eijkemans MJ, Broekmans FJ, et al. Intrauterine insemination with controlled ovarian hyperstimulation versus expectant management for couples with unexplained subfertility and an intermediate prognosis: a randomised clinical trial. Lancet. 2006;368(9531):216-21.   [PubMed]
  49. Stolwijk AM, Wetzels AM, Braat DD. Cumulative probability of achieving an ongoing pregnancy after in-vitro fertilization and intracytoplasmic sperm injection according to a woman's age, subfertility diagnosis and primary or secondary subfertility. Hum Reprod. 2000;15(1):203-9.   [PubMed]
  50. Lintsen AM, Eijkemans MJ, Hunault CC, Bouwmans CA, Hakkaart L, Habbema JD, et al. Predicting ongoing pregnancy chances after IVF and ICSI: a national prospective study. Hum Reprod. 2007;22(9):2455-62.   [PubMed]
  51. Soria M, Pradillo G, Garcia J, Ramon P, Castillo A, Jordana C, et al. Pregnancy predictors after intrauterine insemination: analysis of 3012 cycles in 1201 couples. J Reprod Infertil. 2012;13(3):158-66.   [PubMed]
  52. Isa AM, Abu-Rafea B, Alasiri SA, Al-Mutawa J, Binsaleh S, Al-Saif S, et al. Accurate diagnosis as a prognostic factor in intrauterine insemination treatment of infertile saudi patients. J Reprod Infertil. 2014;15(4):184-9.   [PubMed]
  53. La Marca A, Nelson SM, Sighinolfi G, Manno M, Baraldi E, Roli L, et al. Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction. Reprod Biomed Online. 2011;22(4):341-9.   [PubMed]
  54. van Loendersloot LL, van Wely M, Repping S, Bossuyt PM, van der Veen F. Individualized decision-making in IVF: calculating the chances of pregnancy. Hum Reprod. 2013;28(11):2972-80.   [PubMed]
  55. Trimarchi JR, Hackett RJ, Pierce DJ, Keefe DL. Mathematical model predicts day 6 transfer outcomes. Fertil Steril. 2002;78(Suppl 1):S140.   [Abstract]
  56. Nayudu PL, Gook DA, Hepworth G, Lopata A, Johnston WI. Prediction of outcome in human in vitro fertilization based on follicular and stimulation response variables. Fertil Steril. 1989;51(1):117-25.   [PubMed]
  57. Hughes EG, King C, Wood EC. A prospective study of prognostic factors in in vitro fertilization and embryo transfer. Fertil Steril. 1989;51(5):838-44.   [PubMed]
  58. Sabatini L, Zosmer A, Hennessy EM, Tozer A, Al-Shawaf T. Relevance of basal serum FSH to IVF outcome varies with patient age. Reprod Biomed Online. 2008;17(1):10-9.   [PubMed]
  59. Stolwijk AM, Zielhuis GA, Sauer MV, Hamilton CJ, Paulson RJ. The impact of the woman's age on the success of standard and donor in vitro fertilization. Fertil Steril. 1997;67(4):702-10.   [PubMed]
  60. Terriou P, Sapin C, Giorgetti C, Hans E, Spach JL, Roulier R. Embryo score is a better predictor of pregnancy than the number of transferred embryos or female age. Fertil Steril. 2001;75(3):525-31.   [PubMed]
  61. Sohrabvand F, Shariat M, Fotoohi Ghiam N, Hashemi M. Comparison of two embryo scoring systems for prediction of outcome in assisted reproductive techniques cycles. Acta Med Iran. 2011;49(12):784-8.   [PubMed]
  62. Sohrabvand F, Shariat M, Fotoohi Ghiam N, Hashemi M. The relationship between number of transferred embryos and pregnancy rate in ART cycles. Tehran Univ Med J. 2009;67(2):132-6.   [Abstract]
  63. Strandell A, Bergh C, Lundin K. Selection of patients suitable for one-embryo transfer may reduce the rate of multiple births by half without impairment of overall birth rates. Hum Reprod. 2000;15(12):2520-5.   [PubMed]
  64. Wang YA, Healy D, Black D, Sullivan EA. Age-specific success rate for women undertaking their first assisted reproduction technology treatment using their own oocytes in Australia, 2002-2005. Hum Reprod. 2008;23(7):1633-8.   [PubMed]
  65. Syrop CH, Dawson JD, Husman KJ, Sparks AE, Van Voorhis BJ. Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3. Hum Reprod. 1999;14(7):1752-6.   [PubMed]
  66. Ebbesen SM, Zachariae R, Mehlsen MY, Thomsen D, Hojgaard A, Ottosen L, et al. Stressful life events are associated with a poor in-vitro fertilization (IVF) outcome: a prospective study. Hum Reprod. 2009;24(9):2173-82.   [PubMed]
  67. Hart R, Khalaf Y, Yeong CT, Seed P, Taylor A, Braude P. A prospective controlled study of the effect of intramural uterine fibroids on the outcome of assisted conception. Hum Reprod. 2001;16(11):2411-7.   [PubMed]
  68. Hauzman E, Fedorcsak P, Klinga K, Papp Z, Rabe T, Strowitzki T, et al. Use of serum inhibin A and human chorionic gonadotropin measurements to predict the outcome of in vitro fertilization pregnancies. Fertil Steril. 2004;81(1):66-72.   [PubMed]
  69. Maugey-Laulom B, Commenges-Ducos M, Jullien V, Papaxanthos-Roche A, Scotet V, Commenges D. Endometrial vascularity and ongoing pregnancy after IVF. Eur J Obstet Gynecol Reprod Biol. 2002;104(2):137-43.   [PubMed]
  70. Yenkie KM, Diwekar UM, Bhalerao V. Modeling the superovulation stage in in vitro fertilization. IEEE Trans Biomed Eng. 2013;60(11):3003-8.   [PubMed]
  71. Rolf C, Behre HM, Cooper TG, Koppers B, Nieschlag E. Creatine kinase activity in human spermatozoa and seminal plasma lacks predictive value for male fertility in in vitro fertilization. Fertil Steril. 1998;69(4):727-34.   [PubMed]
  72. Repping S, van Weert JM, Mol BW, de Vries JW, van der Veen F. Use of the total motile sperm count to predict total fertilization failure in in vitro fertilization. Fertil Steril. 2002;78(1):22-8.   [PubMed]
  73. Crosignani PG, Walters DE. Clinical pregnancy and male subfertility; the ESHRE multicentre trial on the treatment of male subfertility. European Society of Human Reproduction and Embryology. Hum Reprod. 1994;9(6):1112-8.   [PubMed]
  74. Sabbaghian M, Modarresi T, Hosseinifar H, Daliri Hampa A, Karimian L, Ghaffari F, et al. Predictive value of semen parameters and age of the couple in pregnancy outcome after Intrauterine insemination. Tehran Univ Med J. 2013;71(8):530-5.   [Abstract]
  75. van Weert JM, Repping S, van der Steeg JW, Steures P, van der Veen F, Mol BW. IUI in male subfertility: are we able to select the proper patients? Reprod Biomed Online. 2005;11(5):624-31.   [PubMed]
  76. Ombelet W, Vandeput H, Van de Putte G, Cox A, Janssen M, Jacobs P, et al. Intrauterine insemination after ovarian stimulation with clomiphene citrate: predictive potential of inseminating motile count and sperm morphology. Hum Reprod. 1997;12(7):1458-63.   [PubMed]
  77. Abramsson L, Duchek M. A prognostic score for subfertile men based on anamnestic data and semen variables. Int J Androl. 1989;12(1):1-9.   [PubMed]
  78. Porcu G, Lehert P, Colella C, Giorgetti C. Predicting live birth chances for women with multiple consecutive failing IVF cycles: a simple and accurate prediction for routine medical practice. Reprod Biol Endocrinol. 2013;11:1.   [PubMed]
  79. Ghasemi A, Jahanlou A, Hamdi K, Rezaei A. The effects of cervical mucus removal before Intrauterine Insemination (IUI) in improving pregnancy rates infertile women. Tehran Univ Med J. 2011;69(4):225-30.   [Abstract]
  80. Akhondi MM, Dadkhah A, Bagherpour A, Ardakani ZB, Kamali K, Binaafar S, et al. Study of body image in fertile and infertile men. J Reprod Infertil. 2011;12(4):295-8.   [PubMed]
  81. Hier DB, Edelstein G. Deriving clinical prediction rules from stroke outcome research. Stroke. 1991;22(11):1431-6.   [PubMed]
  82. Hunault CC, Habbema JD, Eijkemans MJ, Collins JA, Evers JL, te Velde ER. Two new prediction rules for spontaneous pregnancy leading to live birth among subfertile couples, based on the synthesis of three previous models. Hum Reprod. 2004;19(9):2019-26.   [PubMed]
  83. van der Steeg JW, Steures P, Eijkemans MJ, Habbema JD, Hompes PG, Broekmans FJ, et al. Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples. Hum Reprod. 2007;22(2):536-42.   [PubMed]
  84. Tournaye H. Male factor infertility and ART. Asian J Androl. 2012;14(1):103-8.   [PubMed]
  85. Brezina PR, Yunus FN, Zhao Y. Effects of pharmaceutical medications on male fertility. J Reprod Infertil. 2012;13(1):3-11.   [PubMed]
  86. Jedrzejczak P, Taszarek-Hauke G, Hauke J, Pawelczyk L, Duleba AJ. Prediction of spontaneous conception based on semen parameters. Int J Androl. 2008;31(5):499-507.   [PubMed]
  87. Hunault CC, Laven JS, van Rooij IA, Eijkemans MJ, te Velde ER, Habbema JD. Prospective validation of two models predicting pregnancy leading to live birth among untreated subfertile couples. Hum Reprod. 2005;20(6):1636-41.   [PubMed]
  88. Hunault CC, Eijkemans MJ, te Velde ER, Collins JA, Habbema JD. Validation of a model predicting spontaneous pregnancy among subfertile untreated couples. Fertil Steril. 2002;78(3):500-6.   [PubMed]
  89. Collins JA, Burrows EA, Wilan AR. The prognosis for live birth among untreated infertile couples. Fertil Steril. 1995;64(1):22-8.   [PubMed]
  90. Bahamondes L, Alma FA, Faundes A, Vera S. Score prognosis for the infertile couple based on historical factors and sperm analysis. Int J Gynaecol Obstet. 1994;46(3):311-5.   [PubMed]
  91. Comhaire FH. Simple model and empirical method for the estimation of spontaneous pregnancies in couples consulting for infertility. Int J Androl. 1987;10(5):671-80.   [PubMed]
  92. Veltman-Verhulst SM, Fauser BC, Eijkemans MJ. High singleton live birth rate confirmed after ovulation induction in women with anovulatory polycystic ovary syndrome: validation of a prediction model for clinical practice. Fertil Steril. 2012;98(3):761-8.   [PubMed]
  93. Leushuis E, van der Steeg JW, Steures P, Repping S, Bossuyt PM, Mol BW, et al. Semen analysis and prediction of natural conception. Hum Reprod. 2014;29(7):1360-7.   [PubMed]
  94. Murto T, Bjuresten K, Landgren BM, Stavreus-Evers A. Predictive value of hormonal parameters for live birth in women with unexplained infertility and male infertility. Reprod Biol Endocrinol. 2013;11:61.   [PubMed]
  95. Tomassetti C, Geysenbergh B, Meuleman C, Timmerman D, Fieuws S, D'Hooghe T. External validation of the endometriosis fertility index (EFI) staging system for predicting non-ART pregnancy after endometriosis surgery. Hum Reprod. 2013;28(5):1280-8.   [PubMed]
  96. Van Geloven N, Van der Veen F, Bossuyt PM, Hompes PG, Zwinderman AH, Mol BW. Can we distinguish between infertility and subfertility when predicting natural conception in couples with an unfulfilled child wish? Hum Reprod. 2013;28(3):658-65.   [PubMed]
  97. van Wely M, Bayram N, van der Veen F, Bossuyt PM. Predicting ongoing pregnancy following ovulation induction with recombinant FSH in women with polycystic ovary syndrome. Hum Reprod. 2005;20(7):1827-32.   [PubMed]
  98. van der Steeg JW, Steures P, Eijkemans MJ, Habbema JD, Hompes PG, Michgelsen HW, et al. Predictive value of pregnancy history in subfertile couples: results from a nationwide cohort study in the Netherlands. Fertil Steril. 2008;90(3):521-7.   [PubMed]
  99. Leushuis E, van der Steeg JW, Steures P, Repping S, Schols W, van der Veen F, et al. Immunoglobulin G antisperm antibodies and prediction of spontaneous pregnancy. Fertil Steril. 2009;92(5):1659-65.   [PubMed]
  100. van der Steeg JW, Steures P, Eijkemans MJ, Habbema JD, Hompes PG, Broekmans FJ, et al. Predictive value and clinical impact of Basal follicle-stimulating hormone in subfertile, ovulatory women. J Clin Endocrinol Metab. 2007;92(6):2163-8.   [PubMed]
  101. Imani B, Eijkemans MJ, te Velde ER, Habbema JD, Fauser BC. A nomogram to predict the probability of live birth after clomiphene citrate induction of ovulation in normogonadotropic oligoamenorrheic infertility. Fertil Steril. 2002;77(1):91-7.   [PubMed]
  102. Giwercman A, Lindstedt L, Larsson M, Bungum M, Spano M, Levine RJ, et al. Sperm chromatin structure assay as an independent predictor of fertility in vivo: a case-control study. Int J Androl. 2010;33(1):e221-7.   [PubMed]
  103. Ferlitsch K, Sator MO, Gruber DM, Rucklinger E, Gruber CJ, Huber JC. Body mass index, follicle-stimulating hormone and their predictive value in in vitro fertilization. J Assist Reprod Genet. 2004;21(12):431-6.   [PubMed]
  104. Bancsi LF, Huijs AM, den Ouden CT, Broekmans FJ, Looman CW, Blankenstein MA, et al. Basal follicle-stimulating hormone levels are of limited value in predicting ongoing pregnancy rates after in vitro fertilization. Fertil Steril. 2000;73(3):552-7.   [PubMed]
  105. Ombelet W, Cooke I, Dyer S, Serour G, Devroey P. Infertility and the provision of infertility medical services in developing countries. Hum Reprod Update. 2008;14(6):605-21.   [PubMed]
  106. McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS. Users' guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA. 2000;284(1):79-84.   [PubMed]
  107. Randolph AG, Guyatt GH, Calvin JE, Doig G, Richardson WS. Understanding articles describing clinical prediction tools. Evidence Based Medicine in Critical Care Group. Crit Care Med. 1998;26(9):1603-12.   [PubMed]
  108. Bostofte E. Prognostic parameters in predicting pregnancy. A twenty-year follow-up study comprising semen analysis in 765 men of infertile couples evaluated by the Cox regression model. Acta Obstet Gynecol Scand. 1987;66(7):617-24.   [PubMed]
  109. Snick HK, Snick TS, Evers JL, Collins JA. The spontaneous pregnancy prognosis in untreated subfertile couples: the Walcheren primary care study. Hum Reprod. 1997;12(7):1582-8.   [PubMed]
  110. Ottosen LD, Kesmodel U, Hindkjaer J, Ingerslev HJ. Pregnancy prediction models and eSET criteria for IVF patients--do we need more information? J Assist Reprod Genet. 2007;24(1):29-36.   [PubMed]
  111. Tjon-Kon-Fat RI, Lar DN, Steyerberg EW, Broekmans FJ, Hompes P, Mol BW, et al. Inter-clinic variation in the chances of natural conception of subfertile couples. Hum Reprod. 2013;28(5):1391-7.   [PubMed]
  112. Haan G, Bernardus RE, Hollanders JM, Leerentveld RA, Prak FM, Naaktgeboren N. Results of IVF from a prospective multicentre study. Hum Reprod. 1991;6(6):805-10.   [PubMed]
  113. Nachtigall RD. International disparities in access to infertility services. Fertil Steril. 2006;85(4):871-5.   [PubMed]
  114. Bonita R, Beaglehole R. Basic Epidemiology. 2nd ed. Geneva: World Health Organization; 2006. 207 p.
  115. Shibahara H, Obara H, Ayustawati, Hirano Y, Suzuki T, Ohno A, et al. Prediction of pregnancy by intrauterine insemination using CASA estimates and strict criteria in patients with male factor infertility. Int J Androl. 2004;27(2):63-8.   [PubMed]
  116. Wichmann L, Isola J, Tuohimaa P. Prognostic variables in predicting pregnancy. A prospective follow up study of 907 couples with an infertility problem. Hum Reprod. 1994;9(6):1102-8.   [PubMed]
  117. Mol BW, van Wely M, Steyerberg EW. Using prognostic models in clinical infertility. Hum Fertil (Camb). 2000;3(3):199-202.   [PubMed]
  118. Luke B, Brown MB, Wantman E, Stern JE, Baker VL, Widra E, et al. A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology. Fertil Steril. 2014;102(3):744-52.   [PubMed]
  119. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277(6):488-94.   [PubMed]
  120. Farquhar CM, van den Boogaard NM, Riddell C, Macdonald A, Chan E, Mol BW. Accessing fertility treatment in New Zealand: a comparison of the clinical priority access criteria with a prediction model for couples with unexplained subfertility. Hum Reprod. 2011;26(11):3037-44.   [PubMed]
  121. Mol BW, Collins JA, Burrows EA, van der Veen F, Bossuyt PM. Comparison of hysterosalpingography and laparoscopy in predicting fertility outcome. Hum Reprod. 1999;14(5):1237-42.   [PubMed]
  122. Roberts SA, McGowan L, Mark Hirst W, Vail A, Rutherford A, Lieberman BA, et al. Reducing the incidence of twins from IVF treatments: predictive modelling from a retrospective cohort. Hum Reprod. 2011;26(3):569-75.   [PubMed]
  123. Williams Z, Banks E, Bkassiny M, Jayaweera SK, Elias R, Veeck L, et al. Reducing multiples: a mathematical formula that accurately predicts rates of singletons, twins, and higher-order multiples in women undergoing in vitro fertilization. Fertil Steril. 2012;98(6):1474-80.   [PubMed]
  124. Eimers JM, te Velde ER, Gerritse R, Vogelzang ET, Looman CW, Habbema JD. The prediction of the chance to conceive in subfertile couples. Fertil Steril. 1994;61(1):44-52.   [PubMed]
  125. Bostofte E, Bagger P, Michael A, Stakemann G. Fertility prognosis for infertile couples. Fertil Steril. 1993;59(1):102-7.   [PubMed]
  126. van Loendersloot L, Repping S, Bossuyt PM, van der Veen F, van Wely M. Prediction models in in vitro fertilization; where are we? A mini review. J Adv Res. 2014;5(3):295-301.   [PubMed]

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