Human Reproduction Update Advance Access originally published online on August 4, 2006
Human Reproduction Update 2006 12(6):685-718; doi:10.1093/humupd/dml034
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
A systematic review of tests predicting ovarian reserve and IVF outcome
1 Department of Reproductive Medicine, University Medical Centre Utrecht, Utrecht, 2 Division of Reproductive Endocrinology and Fertility and the IVF Centre, Department of Obstetrics and Gynaecology, Vrije Universiteit Medical Centre and 3 Centre for Reproductive Medicine, Department of Obstetrics and Gynecology, Academic Medical Centre, Amsterdam, The Netherlands
4 To whom correspondence should be addressed at: Department of Reproductive Medicine, Vrije Universiteit Medical Center (VUmc), PO Box 70057, 1007 MB, Amsterdam, The Netherlands. E-mail: cb.lambalk{at}vumc.nl
 |
Abstract |
|---|
 TOP Abstract IntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
The age-related decline of the success in IVF is largely attributable to a progressive decline of ovarian oocyte quality and quantity. Over the past two decades, a number of so-called ovarian reserve tests (ORTs) have been designed to determine oocyte reserve and quality and have been evaluated for their ability to predict the outcome of IVF in terms of oocyte yield and occurrence of pregnancy. Many of these tests have become part of the routine diagnostic procedure for infertility patients who undergo assisted reproductive techniques. The unifying goals are traditionally to find out how a patient will respond to stimulation and what are their chances of pregnancy. Evidence-based medicine has progressively developed as the standard approach for many diagnostic procedures and treatment options in the field of reproductive medicine. We here provide the first comprehensive systematic literature review, including an a priori protocolized information retrieval on all currently available and applied tests, namely early-follicular-phase blood values of FSH, estradiol, inhibin B and anti-Müllerian hormone (AMH), the antral follicle count (AFC), the ovarian volume (OVVOL) and the ovarian blood flow, and furthermore the Clomiphene Citrate Challenge Test (CCCT), the exogenous FSH ORT (EFORT) and the gonadotrophin agonist stimulation test (GAST), all as measures to predict ovarian response and chance of pregnancy. We provide, where possible, an integrated receiver operating characteristic (ROC) analysis and curve of all individual evaluated published papers of each test, as well as a formal judgement upon the clinical value. Our analysis shows that the ORTs known to date have only modest-to-poor predictive properties and are therefore far from suitable for relevant clinical use. Accuracy of testing for the occurrence of poor ovarian response to hyperstimulation appears to be modest. Whether the a priori identification of actual poor responders in the first IVF cycle has any prognostic value for their chances of conception in the course of a series of IVF cycles remains to be established. The accuracy of predicting the occurrence of pregnancy is very limited. If a high threshold is used, to prevent couples from wrongly being refused IVF, a very small minority of IVF-indicated cases (
3%) are identified as having unfavourable prospects in an IVF treatment cycle. Although mostly inexpensive and not very demanding, the use of any ORT for outcome prediction cannot be supported. As poor ovarian response will provide some information on OR status, especially if the stimulation is maximal, entering the first cycle of IVF without any prior testing seems to be the preferable strategy.
Key words: IVF/ICSI outcome / ovarian reserve / ovarian stimulation
|
Introduction |
|---|
|
TOPAbstractIntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
In Western societies the introduction in the 1960s of reliable methods of contraception has led to the birth of fewer children per family. Driven by increasing levels of female education, a growing participation in labour force and career demands, postponement of childbearing has been a secondary consequence of the so-called sexual revolution (Leridon, 1998
). These societal changes in family planning have caused a significant increase in the incidence of unwanted infertility due to female reproductive ageing (Weinstein et al., 1993; Abma et al., 1997; Ventura et al., 2001).
From studies on natural populations in which no consistent methods of birth control are applied, it has been shown that natural fertility starts to decline after the age of 30, accelerates in the mid-30s and will lead to sterility at a mean age of 41 (Spira, 1988; Wood, 1989; te Velde and Pearson, 2002) (Figure 1). The reduction in female fertility can also be shown from contemporary population studies. The chance of not conceiving a first child within one year increases from under 5% in women in their early 20s to approximately 30% or over in the age group of 35 years and older (Abma et al., 1997). So, although the majority of women of older age will obtain the desired pregnancy within a one-year period, the chance of becoming subfertile increases 6 fold in comparison with very young women.
|
The age-related effect on female fertility has also been shown in numerous reports on the results of IVF treatment in infertile couples. The probability of live birth obtained through IVF treatment clearly decreases after the age of 35 (Anonymous, 1995
; Templeton et al., 1996) and the same has been shown to be true for the implantation rate per embryo (van Kooij et al., 1996). In fact, female age has consistently been shown to be an important predictor of success in IVF treatment.
Over the past two decades, a number of so-called ovarian tests have been studied for their ability to predict outcome of IVF in terms of oocyte yield and occurrence of pregnancy. Some of these tests have become part of the routine diagnostic procedure for infertility patients that will undergo assisted reproductive techniques. With the current work we aim to provide an answer to the question of what the true value is of these tests to patient management. Evidence-based medicine has progressively developed as the standard approach for many diagnostic procedures and treatment options in the field of reproductive medicine (National Collaborating Center for Women’s and Children’s Health, 2004). Therefore, we provide a comprehensive systematic literature review, including an a priori protocolized information retrieval on all currently available and applied tests to determine ovarian reserve (OR).
What follows is first a general section in which we briefly outline the aims and the valuation of OR testing and the set-up of the systematic review. After this, we describe individually all currently available tests and their effectiveness with regard to prediction of ovarian response and pregnancy after IVF in generally accepted terms for diagnostic procedures. A unique feature of this systematic review is that we will furthermore provide where possible an integrated receiver operating characteristic (ROC) analysis and curve of all individual evaluated published papers of each test, as well as a formal judgement upon the clinical value.
|
The assessment of OR |
|---|
|
TOPAbstractIntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
OR can be considered normal in conditions where stimulation with the use of exogenous gonadotrophins will result in the development of at least 8–10 follicles and the retrieval of a corresponding number of healthy oocytes at follicle puncture (Fasouliotis et al., 2000
). With such a yield, the chances of producing a live birth through IVF are considered optimal. In general, as outlined earlier, age of the woman is a simple way of obtaining information on the extent of her OR, in terms of both quantity and quality (Templeton et al., 1996). However, in the view of the substantial variation in the decline of reproductive capacity with age (te Velde and Pearson, 2002) (Figure 2), there is a need to identify women of relatively young age with clearly diminished reserve, as well as women around the mean age at which natural fertility on average is lost (41 years) but still with adequate OR. In clinical terms, we aim to identify women with a high risk of producing a poor response to ovarian stimulation and/or a very low probability of becoming pregnant through IVF, as well as those who still produce enough oocytes to have a good chance of becoming pregnant even if female age is advanced. If it appears possible to identify such categories of women, then management could be individualized, for instance by stimulation dose or treatment scheme adjustments (Tarlatzis et al., 2003), by counselling against initiation of IVF treatment or pertinent refusal to accept initiation, or by indicating the necessity of early initiation of treatment before reserve has diminished too far.
|
OR is currently defined as the number and quality of the follicles left in the ovary at any given time. An accurate measure of the quantitative OR would involve the counting of all follicles present in both ovaries, as is done in post-mortem studies (Block, 1952
). For obvious reasons, in OR testing, the true size of the follicle pool has not been used as the benchmark for evaluation (Lass et al., 1997a; Lambalk et al., 2004; Lass, 2004; Sharara and Scott, 2004), apart from one distinct study (Gulekli et al., 1999), where whole ovary counts served as reference for several OR tests (ORTs). Instead, several proxy variables of the pool size are used in studies on diagnostic accuracy, like ovarian response to hyperstimulation with exogenous FSH in IVF and the occurrence of menopause or menopausal transition, as these events are quantitatively determined. Although related, the quality of the oocyte released from the dominant follicle at ovulation represents the other aspect of ovarian reserve. Proxy variables for oocyte quality currently used are the pregnancy probability in infertility treatment like IUI and IVF or in the follow-up of couples during and after the initial infertility work-up. We should therefore realize that in the vast majority of studies on ORTs that will be discussed below, either ovarian response or occurrence of pregnancy in IVF serves as the benchmark to judge upon the accuracy and clinical value of the test under study. Ovarian response to adequate stimulation may be considered the most accurate, though still indirect, representation of the status of the primordial follicle pool, as it is a condition that is continuously present in the individual that undergoes the test. In contrast, the occurrence of pregnancy in such an individual may be influenced by many more factors than oocyte, and hence embryo quality, alone. Only if the occurrence of pregnancy is studied in a series of treatment cycles it may represent a solid proxy variable of the benchmark for ovarian reserve. Most ORTs are quite adequate in predicting ovarian response, but often fail to correctly predict the occurrence of pregnancy, especially if only one IVF cycle was studied.
ORT evaluation using response and/or pregnancy as reference or outcome variables should imply the assessment of predictive accuracy and clinical value of the test. Accuracy refers to the degree by which the outcome condition is predicted correctly. Summary statistics of accuracy include sensitivity (rate of correct identification of cases with poor response), specificity (rate of correct identification of cases without poor response), likelihood ratio (LR, how many times more likely particular test results are in patients with poor response than in those without poor response) and diagnostic odds ratios (DOR, the odds of positive test results in cases with poor response over the odds of positive test results in those without poor response) (Deeks, 2001; Grimes and Schulz, 2005). To identify all cases that will respond poorly to stimulation without judging many normal responders badly, the test must have high sensitivity and high specificity.
Positive LRs above 10 and negative LRs below 0.1 are considered as indicators of an adequate diagnostic test, while values between 5 and 10 and below 0.2 are considered to indicate a moderate test. As such, the LR can be considered a clinically useful tool to help judge the performance of the test, as the value will change when the threshold for an abnormal test is shifted.
The diagnostic odds ratio is an adequate measure when combining studies in a systematic review, as a single diagnostic odds ratio corresponds to a set of sensitivities and specificities depicted by an ROC curve and is considered threshold independent (Figure 3). It therefore can be considered a good parameter to compare the overall accuracy of a test evaluated in different studies. Although the DOR values will be higher for tests with better combinations for sensitivity and specificity, this value has not been advocated as a single measure of clinical value, as changes in the threshold used will not be expressed by a change in DOR value. For the meta-analytic approach, the range of DOR values across studies gives some indication as to the homogeneity of such studies.
|
Finally, the area under the ROC curve provides information on the overall discriminatory capacity of the test. Values of 1.0 imply perfect and that of 0.5 indicate completely absent discrimination.
Clinical value incorporates the question whether application of the test at a certain threshold will really change management or costs or safety or success rates on a population basis. It deals with the valuation of false positive and false negative test results in relation to the consequences of these test results for clinical decisions. Also it implies the rate of abnormal test results leading to altered decisions within the population of interest.
Studies on the predictive accuracy and clinical value of ORTs should preferably be prospective in design, should examine cohorts of patients in IVF settings without exclusion of cases with signs of diminished ovarian reserve and patient management should not have been influenced by the test under study (verification bias). Also, evaluation should be equally weighted for every case, thus every case should contribute the same amount of cycles to the analysis. In most studies, only one IVF cycle is studied. A case–control design for the purpose of OR testing bears the disadvantage of retrospection and the absence of a reliable estimate of disease prevalence. The tests under study should in principle be reproducible, both at the laboratory (hormone assays) and at the operator level (ultrasound examination). Also, the outcome of treatment (response and pregnancy), serving as the reference for ovarian reserve, should be clearly defined.
The accuracy in predicting a certain outcome by the test under study should be evaluated by constructing contingency tables at several threshold levels for an abnormal test. Using the calculated sensitivity and specificity from each threshold level, a ROC curve (Figure 3) can be drawn and the calculated area under this curve represents the overall predictive accuracy of the test. Assessment of the clinical value is a complex process in which the applicability in daily practice should become clear. The overall accuracy represented by the ROC curve, the choice of a threshold for abnormality, the rate of abnormal tests at that threshold, the post-test probability of disease (i.e. poor response or non-pregnancy), the valuation of false positive and false negative test results and the consequence for patient management of an abnormal test will all contribute to the process of deciding whether a test is useful or not. Finally, the cost of carrying out the test as a routine measure and the burden to the patient balanced against the reduction in costs by excluding cases with low pregnancy prospects should contribute to the decision whether or not to apply a test.
ORTs in relation to other predictors of success
It is important for patients who are considering treatment with IVF to know the probability of success in the course of a series of IVF treatment cycles. The possibility of a live birth for any couple undergoing treatment will depend on the success rate at the individual clinic. However, equally important in the prediction of outcome are the characteristics of the couple seeking treatment (Stolwijk et al., 1996; Templeton et al., 1996; Sharma et al., 2002). Serious effort has been put into the build-up of prediction models that estimate the probabilities for success prior and during subsequent IVF cycles. In general, these models appeared inaccurate when external validation studies were carried out (Stolwijk et al., 1998; Smeenk et al., 2000). Intuitively, many IVF centres will use factors like female age, parity, duration of infertility, ovarian response in the first IVF attempt and embryo quality for individual counselling, albeit not through a formal prediction model. Within this practice, ORTs also may play a certain role and female age will be the one ORT applied almost without exception. The pressing question would be to what extent other, endocrine- or ultrasound-based, ORTs contribute and add to the prognostic information already obtained from the infertility work-up or the first IVF cycle. To date, studies specifically addressing this question are scarce or do not include the full range of prognostic factors available.
There are a number of studies (Eimers et al., 1994; Collins et al., 1995; Snick et al., 1997; Hunault et al., 2004; Hunault et al., 2005) that offer a model, based on factors like duration of subfertility, female age, parity, sperm quality and post-coital test, for the prediction of live birth among untreated subfertile couples. However, none of these models included ORTs, apart from female age. Only one study showed that on top of predictions based on the Eimers model, ORTs failed to add relevant information to the couple’s chances for a spontaneous pregnancy (van Rooij et al., 2005).
General remarks on physiological background of ORTs
Tests that are used to predict some defined outcome related to ovarian reserve almost without exception give assessment of the number of follicles remaining at some time point in both ovaries. Any marker giving an estimate of the remaining pool will at the same time be capable of providing, to some extent, information on oocyte quality. But on average, from prediction studies it seems that some markers give a better indication of quality than others. Female age, for instance, is the basic factor that is related to both quantity and quality. Basal FSH, through the feedback of inhibin B and estradiol, will represent cohort size but mostly at the extremes and therefore give a more thorough indication of quality aspects. This is in contrast to the more direct quantitative tests using antral follicle count (AFC), anti-Müllerian hormone (AMH) and ovarian volume (OVVOL) that are capable of describing a more complete range of ovarian reserve states. By choosing the right thresholds these tests may eventually correctly predict oocyte quality. The true relation between quantity and quality, however, remains a source of debate. Quantity is an aspect of ovarian reserve that is present in a continuous state and therefore offers a more or less continuous measurability. Quality, however, comes to expression every now and then, even in the setting of IVF. The relationship between the two aspects of ovarian reserve has become more evident when the predictive value of a poor response in a first IVF cycle was examined towards the probability of pregnancy in the actual or subsequent cycles (Klinkert et al., 2004). While cases with a normal response in additional cycles yielded acceptable rates of pregnancy, it was shown that in repeated poor responders this probability never surpassed 10% (de Boer et al., 2002; Lawson et al., 2003; Klinkert et al., 2004). It is also important to remember that there are several factors that contribute to the occurrence of pregnancy other than ovarian reserve, such as embryo transfer technique and number of embryos replaced. Even in young women with normal reserve the chance of non-pregnancy remains at least at the 50% level. So, a non-pregnancy state after IVF may even be attributed to unknown, yet non-ovarian reserve related, factors.
Approach of the systematic review
The aim of the systematic review on the value of diagnostic tests is to obtain an overall estimate of the test accuracy and clinical value based on all present evidence, after assessing the quality of the included studies and evaluating the variation in findings among the studies (Irwig et al., 1995; Deeks, 2001; Deville et al., 2002; Honest and Khan, 2002; Glas et al., 2003). Systematic review and meta-analysis on diagnostic accuracy and value implies consecutive steps as summarized in Table I (Irwig et al., 1994; Mol et al., 1997) please see addendum.
|
For each study finally included in the meta-analysis, sensitivity and specificity are calculated from the contingency tables. Homogeneity of the sensitivity–specificity points is tested by means of the
2-test statistic. A summary point estimate of sensitivity and specificity and the 95% confidence interval is calculated if homogeneity cannot be rejected. In case of heterogeneity, logistic regression is used to evaluate whether Quality/Methodology characteristics of a study are associated with the discriminative capacity of the test under study. If one of the study characteristics is found to have a statistically significant impact on the performance of the test, further analysis is performed in subgroups of patients. If not, it is explored whether the differences in sensitivity–specificity combinations are because of the use of different threshold levels of the test under study. For this purpose, a Spearman correlation coefficient is calculated to assess the association between sensitivity and specificity. If there is a negative correlation as defined by a correlation coefficient of –0.5 or stronger, the individual pairs of sensitivity and specificity are considered to originate from a single ROC curve. All sensitivity–specificity points are then plotted and a summary ROC curve is estimated using a random-effects regression model (Littenberg and Moses, 1993; Midgette et al., 1993; Moses et al., 1993). An important issue is the fact that individual studies may produce highly variable sensitivity–specificity points in the ROC space. This is generally explained by variation in the applied threshold level for an abnormal test across the studies or the presence of considerable study heterogeneity. As in the formal analysis, the presence of heterogeneity in design will be dealt with, and the variation in sens/spec points is generally attributed to the variation in threshold levels and thus allows us to construct a summary ROC curve. At the same time, the threshold variation will prevent the possibility of assessing a single threshold for a specific test that has a generalizable value. This will only become possible if from every study the original database would be available and to date this seems to be an extreme effort.
To assess the clinical value of the test under study for the assessment of disease state (i.e. poor response or non-pregnancy), the positive and negative predictive values are calculated using the estimated summary ROC curve and assuming arbitrary prevalences of the disease in the population. An LR for a positive (or abnormal) test result is then calculated for each point on the estimated ROC curve. Subsequently, the post-test probabilities of disease at various LR values are then calculated for the arbitrary pre-test probabilities of disease, assuming independence between the pre-test probability and the performance of the test (Bancsi et al., 2003). Final judgement depends on the overall accuracy, the choice of the test threshold, the post-test prediction at that threshold level and the valuation of a false positive test result. In case no estimated curve from the selected studies can be constructed, the judgement upon the clinical value is based on a comparison of a preset level of sensitivity and specificity with the observed levels in the various studies.
The aim of the present series of systematic reviews is to assess the true diagnostic accuracy and clinical value of the ORTs known to date, when applied in an IVF/ICSI population. Reference standards used to valuate the test properties are response to ovarian stimulation and occurrence of pregnancy. No preset definition was used for these standards. For every ORT under study, a computerized MEDLINE search was performed to identify articles on the subject outlined in the previous chapters published until December 2004. Checking of reference lists of articles already obtained was done, all in an iterative fashion. Keywords used for the various searches were ‘in vitro fertilization’ or ‘in vitro fertilisation’ or ‘assisted’ or ‘intracytoplasmatic’ or ‘intracytoplasmic’, in combination with ‘test-specific’ keywords, as mentioned in the tables.
One investigator (DH or JK) read all abstracts of the articles that were identified by the search. Any article reporting on the association of the test with poor ovarian response and/or non-pregnancy after IVF or possibly containing information that was to be transformed into a predictive tabulation was pre-selected. Subsequently, all pre-selected articles were fully read and judged independently by two investigators (DH and JK), and separate 2 x 2 tables were constructed for cross classification of the test result and the occurrence of poor response and/or non-pregnancy, whenever possible. In the event of disagreement on the inclusion or exclusion of pre-selected studies for the meta-analysis or on the calculation of the 2 x 2 table data or the scoring of quality characteristics, the judgement of a third author (FB or CL) was decisive. Studies in which it was not possible to construct 2 x 2 tables were excluded. Cross-references in all selected articles were checked, and, if applicable, studies were added to the analysis.
Each study was scored by the investigators on the following Quality/Methodology characteristics: (i) sampling (consecutive versus other), (ii) data collection (prospective versus retrospective), (iii) study design (cohort study versus case–control study), (iv) blinding (present or absent), (v) selection bias, (vi) verification bias, (vii) analysis on one or multiple cycles per couple and (viii) definition of outcome, poor response and pregnancy.
In the following sections, the results of search, data extraction, quality and methodology assessment and meta-analysis of extracted data as outlined above are discussed for every ORT comprised in this review.
Systematic review
Through the search and selection strategy, a total of 37 studies reporting on the capacity of basal FSH to predict poor ovarian response and/or non-pregnancy after IVF and which were suitable for data extraction and meta-analysis were identified (Scott et al., 1989; Padilla et al., 1990; Toner et al., 1991; Khalifa et al., 1992; Chan et al., 1993; Ebrahim et al., 1993; Fanchin et al., 1994; Huyser et al., 1995; Licciardi et al., 1995; Smotrich et al., 1995; Balasch et al., 1996; Csemiczky et al., 1996; Martin et al., 1996; Pruksananonda et al., 1996; Gurgan et al., 1997; Chang et al., 1998a; Evers et al., 1998; Ranieri et al., 1998; Sharif et al., 1998; Bassil et al., 1999; Hall et al., 1999; Bancsi et al., 2000; Chae et al., 2000; Creus et al., 2000; Fabregues et al., 2000; Jinno et al., 2000; Penarrubia et al., 2000; Mikkelsen et al., 2001; Nahum et al., 2001; van der Stege and van der Linden, 2001; Esposito e al., 2002; Chuang et al., 2003; Fiçicio
lu et al., 2003; Kwee et al., 2003; Yanushpolsky et al., 2003; Akande et al., 2004; Erdem et al., 2004). Characteristics of the included studies are listed in Table II. As shown, there was a large diversity with regard to the various aspects of methodology and quality, and the definition of poor ovarian response. Logistic regression analysis indicated no significant association between any of these study characteristics and the predictive performance of basal FSH. For example, whether the design of the study was retrospective or prospective did not influence the prognostic capacity of basal FSH.
|
Accuracy of poor response prediction
The sensitivities and specificities, as well as the positive LRs of an abnormal test and the DORs for the prediction of poor ovarian response, as calculated from each study, are summarized in Table III, please see addendum. Sensitivity and specificity points, as plotted in Figure 4, were heterogeneous between studies (
2-test statistic: P-value for sensitivity 0.001 and P-value for specificity 0.001). Therefore, calculation of one summary point estimate for sensitivity and specificity was not meaningful for overall judgement of accuracy. The Spearman correlation coefficient for sensitivity and specificity was –0.87, which was judged to be sufficient to estimate a summary ROC curve (Figure 4).
|
|
Accuracy of non-pregnancy prediction
Sensitivities and specificities for the prediction of non-pregnancy, as calculated from each study, are summarized in Table IV, please see addendum. Again, sensitivity and specificity points plotted in Figure 5 were heterogeneous between studies (
2-test statistic: P-value for sensitivity 0.001 and P-value for specificity 0.001). The Spearman correlation coefficient for sensitivity and specificity was –0.82 and as such was sufficient to estimate a summary ROC curve (Figure 5).
|
|
Clinical value
Based on the summary ROC curves depicted in Figure 4, a range of positive LRs was calculated and for each ratio the pre-FSH test probability of poor response and non-pregnancy was converted into a post-FSH-test probability. Table V, (please see addendum) depicts the probability of obtaining a certain FSH test result and the corresponding LR within different LR ranges for the prediction of poor response and non-pregnancy. At a maximum positive LR of 8, the post-FSH-test probability of poor response will approximate 70% if the pre-FSH-test probability is assumed to be as high as 20%. As is apparent from this table, the probability of obtaining a test result (FSH level) with an LR of
8 is quite small. Table III shows that in women with an increased FSH level the probability of poor response only increases substantially (3-fold or more) in studies applying a high threshold level for FSH, resulting in a very limited number of patients with an abnormal test result.
|
Even more so, for prediction of non-pregnancy, the extremely high FSH levels that are necessary to obtain the moderate positive LR of
5, leading to a post-test pregnancy rate of less than 5% based on a pre-test rate of 20%, again occur only in a very limited number of patients (Table V). Beyond the coordinate defined by specificity 0.90 and sensitivity 0.20, the summary ROC curve almost runs parallel to the line of equality. This indicates that this segment of the curve is 100% uninformative (LR 1).
All this leads to the conclusion that with the use of basal FSH in regularly cycling women, accuracy in the prediction of poor response and non-pregnancy is adequate only at very high threshold levels, but because of the very low numbers of abnormal tests has hardly any clinical value. Considering this along with a false positive rate of 5%, the test will not be suitable as a diagnostic test to exclude patients, but only as screening test for counselling purposes and further diagnostic steps, in which a first IVF attempt may be the step of choice (Roberts et al., 2005).
Systematic review
Through the search and selection strategy, two studies reporting on the predictive capacity of AMH and which were suitable for data extraction and meta-analysis were identified (van Rooij et al., 2002; Muttukrishna et al., 2004). Characteristics of the included studies are listed in addendum, Table VI.
|
Accuracy of poor response prediction
The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response, as calculated from each study, are summarized in Table VII, (see addendum) and in Figure 6. Homogeneity could not be rejected for sensitivity and specificity (
2-test statistic: P-value for sensitivity 0.12 and P-value for specificity 0.64), but this is merely because of the fact that only two studies were included. As can be seen from Figure 6, the points of the two studies can be thought of as originating from a single ROC curve (Spearman correlation coefficient between sensitivity and specificity is –0.81). The summary ROC curve that can be estimated from these points is also shown in Figure 6.
|
|
Accuracy of non-pregnancy prediction
Sensitivities and specificities for the prediction of non-pregnancy by AMH, as calculated from each study, are summarized in Table VIII. As the study of Van Rooij was the only one detected, further meta-analysis is not useful. The ROC-curve derived from the data of Van Rooij et al. representing the accuracy of AMH in the prediction of non-pregnancy is shown in Figure 7.
|
|
Clinical value
As data from only two studies are available, it is not feasible to extract data on the interrelation between positive LRs, post-test probabilities and the rate of abnormal tests. However, looking at the performance of AMH in the prediction of poor response, a desired level for sensitivity of 75% and for specificity of 85% would imply that the test performs only moderately, especially at the sensitivity level. For non-pregnancy prediction, a desired level of sensitivity of 40% and specificity of 95% would imply that the test has hardly any value, unless very low threshold levels would be used, which will certainly lead to only very small percentages of abnormal tests. Additional studies are to be awaited to learn whether test capacity may prove to be more superior than current tests like basal FSH and the AFC (Hazout et al., 2004
; Muttukrishna et al., 2005; Penarrubia et al., 2005).
Systematic review
We detected a total of nine studies reporting on the predictive capacity of inhibin-B and which were suitable for data extraction and meta-analysis (Balasch et al., 1996; Seifer et al., 1997; Hall et al., 1999; Creus et al., 2000; Fabregues et al., 2000; Penarrubia et al., 2000; Bancsi et al., 2002a; Fiçiciolu et al., 2003; Erdem et al., 2004). Characteristics of the included studies are listed in addendum Table IX. Variation among the definitions of poor response and study quality and design characteristics was clearly present but logistic regression analysis revealed that none of the items significantly impacted upon the predictive performance of the test. Subgroup analysis therefore was not indicated.
|
Accuracy of poor response prediction
The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response, as calculated from each study, are summarized in Table X, see addendum. Calculation of one summary point estimate for sensitivity and specificity was not meaningful, as both test characteristics, as plotted in Figure 8, were heterogeneous among studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity 0.002). The Spearman correlation coefficient for sensitivity and specificity was sufficient to estimate a summary ROC curve (R = –0.93, Figure 8). In the figure, it is clearly seen that all but one study were close to the estimated ROC curve, and that one study reported a clearly better accuracy (Fiçiciolu et al., 2003). This study was of good quality, but reported on only a small number of patients.
|
|
Accuracy of non-pregnancy prediction
There were three studies that reported on the capacity of inhibin B to predict non-pregnancy. Sensitivities and specificities for the prediction of non-pregnancy, as calculated from each study, are summarized in Table XI. Sensitivity and specificity as plotted in Figure 9 were heterogeneous between studies (
2-test statistic: P-value for sensitivity 0.004 and P-value for specificity <0.001). The Spearman correlation between sensitivity and specificity showed a coefficient of –0.94, sufficient to estimate a summary ROC curve.
|
|
Clinical value
Based on the summary ROC curves depicted in Figure 8, a range of positive LRs was calculated and for each ratio pre-inhibin B-test probabilities of poor response or non-pregnancy (20 and 80%, respectively) were converted into post-inhibin B-test probabilities. Table XII depicts the probability of obtaining a certain inhibin B test result and the corresponding LR, within different LR ranges for the prediction of poor response and non-pregnancy. At a very modest LR of 4, the post-inhibin B-test probability of poor response will not be higher than 55%, while the chance of obtaining such a test result is very small.
|
For prediction of non-pregnancy, extreme threshold levels are necessary to obtain a modest positive likelihood ratio of
4–5, leading to a post-test pregnancy rate of approximately 5%. Such abnormal test results occur only in a very limited number of patients, while the false positive rate will lead to unnecessary exclusions from IVF programs if the test is used in a diagnostic fashion. With the use of basal inhibin B in regularly cycling women, the accuracy in the prediction of poor response and non-pregnancy is only modest at a very low threshold level. At best the test may be used as screening test for counselling purposes or to direct further diagnostic steps, like a first IVF attempt to observe the response to ovarian stimulation. Used in this way, the test may well be inferior to other tests discussed in this review.
Systematic review
We detected a total of 10 studies reporting on the predictive capacity of basal estradiol and which were suitable for data extraction and meta-analysis (Licciardi et al., 1995; Smotrich et al., 1995; Evers et al., 1998; Vazquez et al., 1998; Hall et al., 1999; Frattarelli et al., 2000; Penarrubia et al., 2000; Phophong et al., 2000; Mikkelsen et al., 2001; Ranieri et al., 2001; Bancsi et al., 2002a). Characteristics of the included studies are listed in addendum Table XIII. Again, variation among the definitions of poor response and study quality and design characteristics was clearly present, but logistic regression analysis revealed that none of the items significantly impacted upon the predictive performance of the test. Subgroup analysis therefore was not indicated.
|
Accuracy of poor response prediction
There were eight studies that reported on the prediction of poor response. The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response, as calculated from each study, are summarized in Table XIV. Calculation of one summary point estimate for sensitivity and specificity was not meaningful, as both test characteristics as plotted in Figure 10 were heterogeneous among studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity 0.002). The Spearman correlation coefficient for sensitivity and specificity was –0.50. As can be seen from Figure 10, this can be because of three outliers, which were extracted from the studies of Smotrich et al. and Ranieri et al. From neither the clinical nor the methodological point of view could a clear explanation be provided for the outliers. When correlation between sensitivity and specificity was assessed after exclusion of the three outliers, we found a very strong correlation (–0.94). Figure 10 shows two estimates of a summary ROC curve, one constructed with all data and one constructed after exclusion of the two studies with outlying data (Figure 10).
|
|
Accuracy of non-pregnancy prediction
There were nine studies that reported on the capacity of basal estradiol to predict non-pregnancy after IVF. Sensitivities and specificities for the prediction of non-pregnancy, as calculated from each study, are summarized in Table XV. Again, sensitivity and specificity as plotted in Figure 11 were heterogeneous between studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity <0.001). The Spearman correlation between sensitivity and specificity showed a coefficient of –0.89, sufficient to estimate a summary ROC curve (Figure 11). This summary ROC curve is almost parallel to the line x = y, indicating virtually no discriminative capacity.
|
|
Clinical value
Based on the two summary ROC curves for all studies depicted in Figure 10, a range of positive LRs was calculated and for each ratio, pre-estradiol-test probabilities of poor response or non-pregnancy (20 and 80%, respectively) were converted into post-estradiol-test probabilities. Table XVI (please see addendum) depicts the probability of obtaining a certain estradiol-test result and the corresponding LR, within different LR ranges for the prediction of poor response and non-pregnancy. At a moderate LR of 4–5, the post-estradiol-test probability of poor response will not be higher than
50%, while the chance of obtaining such a test result is very small.
|
For prediction of non-pregnancy no clear threshold levels can be identified for basal estradiol that will lead to an adequate combination of LR, post-test probability and abnormal test rate. This could be anticipated from the shape of the ROC curve in Figure 11
All this leads to the conclusion that the clinical applicability for basal estradiol as a test before starting IVF is prevented by the very low predictive accuracy, both for poor response and non-pregnancy.
Systematic review
Through the search and selection strategy, a total of 15 studies reporting on the predictive capacity of basal AFC and suitable for data extraction and meta-analysis were identified (Chang et al., 1998b; Frattarelli et al., 2000; Ng et al., 2000; Sharara and McClamrock, 2000; Hsieh et al., 2001; Nahum et al., 2001; Bancsi et al., 2002a; Erdem et al., 2002; Fisch and Sher, 2002; Fiçiciolu et al., 2003; Frattarelli et al., 2003; Jarvela et al., 2003; Kupesic et al., 2003; Yong et al., 2003; Durmusoglu et al., 2004). Characteristics of the included studies are listed in addendum Table XVII. Variation among the definitions of poor response and study quality and design characteristics is clearly present but logistic regression analysis revealed that none of the items significantly impacted upon the predictive performance of the test. Subgroup analysis therefore was not indicated.
|
Accuracy of poor response prediction
The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response, as calculated from each study, are summarized in Table XVIII. Calculation of one summary point estimate for sensitivity and specificity was not meaningful, as both test characteristics as plotted in Figure 12 were heterogeneous among studies (
2-test statistic: P-value for sensitivity 0.001 and P-value for specificity 0.001). The Spearman correlation coefficient for sensitivity and specificity was –0.57 and was judged to be sufficient to estimate a summary ROC curve (Figure 12).
|
|
Accuracy of non-pregnancy prediction
Sensitivities and specificities for the prediction of non-pregnancy, as calculated from each study, are summarized in Table XIX. Again, sensitivity and specificity as plotted in Figure 13 were heterogeneous between studies (
2-test statistic: P-value for sensitivity 0.001 and P-value for specificity 0.001). The Spearman correlation between sensitivity and specificity showed a coefficient of –0.66, sufficient to estimate a summary ROC curve (Figure 13).
|
|
Clinical value
Based on the summary ROC curves depicted in Figure 12, a range of positive LRs was calculated and for each ratio pre-AFC test probabilities of poor response or non-pregnancy were converted into a post-AFC-test probability. Table XX depicts the probability of obtaining a certain AFC test result and the corresponding LR within different LR ranges for the prediction of poor response and non-pregnancy. At a maximum positive LR of
8, the post-AFC test probability of poor response will approximate 70%, if the pre-AFC-test probability is assumed to be as high as 20%. The probability of obtaining a test result (AFC) with a likelihood ratio 8 is high enough to consider the AFC as a clinically valuable test for poor response prediction.
|
For prediction of non-pregnancy, the extremely low AFC that is necessary to obtain a moderate positive likelihood ratio of
5, leading to a post-test pregnancy rate of less than 5% based on a pre-test rate of 20%, occurs only in an extremely limited number of patients (Table XX). Beyond the coordinate defined by specificity 0.80 and sensitivity 0.30, the summary ROC curve almost runs parallel to the line of equality. This indicates that this segment of the curve is 100% uninformative (LR 1).
Based on these data, it can be concluded that the accuracy of the AFC for predicting poor response in regularly cycling women is adequate at a low threshold level, but because of the very limited numbers of abnormal tests has hardly any clinical value for pregnancy prediction. Added to the false positive rate of 5% the test will not be suitable as diagnostic test to exclude patients on the basis of the presumed diagnosis of advanced ovarian ageing. It may well be used as a screening test for possible poor responders and for directing further diagnostic steps like a first IVF attempt, where the ovarian response to hyperstimulation will provide additional information (Hendriks et al., 2005d).
Systematic review
For assessing the predictive value of OVVOL, the search detected a total of 10 studies available for data extraction and meta-analysis. Of these, two studies reported solely on the prediction of poor response (Sharara and McClamrock, 1999; Fiçiciolu et al., 2003) and eight studies reported on the prediction of both poor response and pregnancy (Syrop et al., 1995; Lass et al., 1997b; Frattarelli et al., 2000; Schild et al., 2001; Bancsi et al., 2002a; Jarvela et al., 2003; Kupesic et al., 2003; Erdem et al., 2004). Study characteristics of the included studies are listed in addendum Table XXI. Selection bias was present in almost half of all studies (Lass et al., 1997b; Frattarelli et al., 2000; Kupesic et al., 2003; Erdem et al., 2004). In three studies, patients were selected by basal FSH level (Frattarelli et al., 2000; Kupesic et al., 2003; Erdem et al., 2004) and in the study by Lass et al. (Lass et al., 1997b) only patients aged >36 years with an FSH level <15 IU/L were included. Three studies showed evidence of verification bias (Jarvela et al., 2003; Kupesic et al., 2003; Erdem et al., 2004), implying that smaller OVVOL altered the management of the patient by applying higher FSH dosages.
|
Accuracy of poor response prediction
Sensitivities and specificities, positive LR and the DOR for the prediction of poor ovarian response are summarized in Table XXII. Homogeneity for both sensitivity and specificity had to be rejected (
2-test: both P-values <0.001). Hence, the calculation of a summary point estimate for sensitivity and specificity was not meaningful. None of the study characteristics recorded had a statistically significant impact on the reported predictive performance of OVVOL. The Spearman correlation coefficient for the relation between sensitivity and specificity was –0.55, sufficient to estimate a summary ROC curve. This curve showed a modest overall predictive accuracy as can be seen in the ROC space in Figure 14.
|
|
Accuracy of non-pregnancy prediction
For the prediction of non-pregnancy, test characteristics for each study are summarized in Table XXIII. As with the data for ovarian response, homogeneity for sensitivity had to be rejected. However, specificity appeared to be homogeneous (
2-test: P-value 0.11). Because for the estimation of one summary point for sensitivity and specificity statistical homogeneity, both test parameters are required, this solution was abandoned. Logistic regression analysis showed that three studies which suffered from verification bias reported a significantly different accuracy compared to the seven remaining studies (p-value: 0.01). None of the other study characteristics had a significant impact on the estimates of test accuracy. In the subgroup analysis of the seven studies without verification bias, homogeneity was again rejected for sensitivity, while specificity again showed homogeneity. The Spearman correlation coefficient for sensitivity and specificity was –0.94, which was judged to be sufficient to estimate a summary ROC curve. The curve in Figure 15 indicates that OVVOL volume has no clear accuracy in the prediction of non-pregnancy in IVF patients, even if a very low threshold for abnormality of the test would be chosen.
|
|
Clinical value
Based on the estimated ROC curves in Figure 14 the probability of obtaining a certain test result for the OVVOL measurement is shown in Table XXIV within a corresponding range of LRs for the prediction of poor ovarian response. Only at modest LRs, the post-test probability of poor response may approach 50%, while abnormal test results will be obtained in some 30% of tested cases. However, applying a more adequate positive likelihood level will result in virtually no cases being identified by the test. For non-pregnancy prediction, Table XXIV shows that for higher LRs (>4), the post-test probability of non-pregnancy may increase to
93–97%, assuming a pre-test probability of 80%. However, the probability that a test result will be in that range is close to zero. As false positive test results for both ovarian response and non-pregnancy prediction are not acceptable if patients are refused treatment, all this implies that the OVVOL is hardly suitable as a routine test for ovarian reserve assessment.
|
Ovarian vascular flow
Systematic review
Through the search we detected seven studies reporting on the predictive capacity of ovarian vascular flow parameters for ovarian response and/or the occurrence of pregnancy (Zaidi et al., 1996; Engmann et al., 1999a,b; Kim et al., 2002; Kupesic and Kurjak, 2002; Kupesic et al., 2003; Popovic-Todorovic et al., 2003b). In these studies ovarian flow was assessed either on cycle day 3 or after achievement of pituitary suppression with a GnRh agonist and before the onset of ovarian stimulation. As only the 2003 study by Kupesic (Kupesic et al., 2003) could be included on a 2 x 2 for cross classification of the test result and the occurrence of poor response or non-pregnancy, it was not possible to carry out a formal meta-analysis (see addendum Table XXV and XXVI). Also, the studies used very different flow-derived predictors. Peak systolic velocity was used as the main predictor (Kupesic et al., 2003). Others used ovarian stromal blood flow obtained by 3D power Doppler (Engmann et al., 1999a).
|
|
Ovarian biopsy
Ovarian reserve depends on the number of primordial follicles in the ovarian cortex, which suggests that the obvious way to obtain an estimate would be to measure follicular density in an ovarian biopsy (Lass, 2001; Lass, 2004). Attempts were made to quantify the number of small antral follicles in small shallow biopsies taken during diagnostic laparoscopy from infertility patients (Lass et al., 1997a) and there was a clear age-dependent decline in follicular density. Women over 35 years of age had only 30% of the quantities present in younger women. The number of follicles per unit of volume found in the biopsies was used to estimate the total and it was suggested that it could as such be potentially applied at the individual level. It was recognized though that the biopsy follicle density would not accurately represent the density in the whole ovary (Lass, 2001) and this seems indeed the case. Recently, several investigators have shown that follicle density varied greatly in small pieces of cortex, rendering information from biopsies as completely unreliable for an individual ovarian follicle content irrespective of how many were taken, their size and the location (Qu et al., 2000; Schmidt et al., 2003; Lambalk et al., 2004; Sharara and Scott, 2004). This indicates that the technique which is invasive and potentially harmful in terms of risks of adhesions and other complications of the surgical procedure is intrinsically unreliable and should therefore not be used to evaluate individual ovarian reserve. It is probably useful for research purposes to determine follicle density statistics in patient groups provided that group sizes are such that they compensate for the inherent extreme inter-biopsy and inter-individual spread of information (Qu et al., 2000; Schmidt et al., 2003; Webber et al., 2003; Lambalk et al., 2004). Finally, in the context of the current systematic review, there are no studies published that have evaluated ovarian biopsy follicle density for prediction of IVF outcome in terms of ovarian response and pregnancy rates.
Clomiphene Citrate Challenge Test
Systematic review
The computerized MEDLINE search detected 12 studies on the capacity of the Clomiphene Citrate Challenge Test (CCCT) to predict poor ovarian response and/or pregnancy after IVF (Tanbo et al., 1989; Loumaye et al., 1990; Tanbo et al., 1990; Tanbo et al., 1992; Csemiczky et al., 1996; Kahraman et al., 1997; van der Stege and van der Linden, 2001; Csemiczky et al., 2002; Kwee et al., 2003; Yanushpolsky et al., 2003; Erdem et al., 2004; Hendriks et al., 2005a). Study characteristics of the included studies are listed in addendum Table XXVII. This table shows that many studies suffered from various sources of potential bias, especially selection bias. Also, definitions applied for poor ovarian response and for an abnormal CCCT result (based on either day-10 FSH alone or on both basal FSH and day-10 FSH results) varied considerably. Logistic regression analysis indicated that none of the study characteristics had a statistically significant impact on the reported predictive performance of the CCCT, neither for the outcome response nor for the outcome non-pregnancy. As a consequence, all studies were taken together for further analysis.
|
Accuracy of poor response prediction
For the prediction of ovarian response, sensitivities and specificities of each study are summarized in Table XXVIII. Homogeneity could not be rejected for sensitivity (
2-test statistic: P-value 0.09), but had to be rejected for specificity (2-test statistic: P-value <0.001). Therefore, calculation of one summary point estimate for sensitivity and specificity was not feasible. Moreover, values of the DOR (range 2.4–38.8) from the various studies appeared heterogeneous, indicating that the individual ROC curves were quite heterogeneous. Also, the Spearman correlation coefficient for sensitivity and specificity values was –0.46, which was judged not to be sufficient to estimate a summary ROC-curve. A plot of the sensitivity–specificity points in an ROC space is shown in Figure 16, showing the considerable heterogeneity which appeared not be attributable to differences in threshold level used.
|
|
Accuracy of non-pregnancy prediction
For the prediction on non-pregnancy, the sensitivities and specificities of each study are summarized in Table XXIX. Homogeneity was rejected for both sensitivity and specificity (
2-test statistic: P-value <0.001 and 0.04, respectively) and calculation of one summary point estimate for sensitivity and specificity was not meaningful. Also, the values of the DOR in the various studies (range 1.0–35.4) appeared non-homogeneous. A plot of sensitivity–specificity points in an ROC space is shown in Figure 17. The Spearman correlation between sensitivity and specificity was –0.20, which again was judged not to be sufficient to estimate a summary ROC curve.
|
|
Clinical value
Because of the absence of estimated ROC curves for response and non-pregnancy prediction, the interrelation between positive LR, post-test probability and percentage of abnormal tests could not be calculated. It is considered that a challenge test used as a diagnostic tool to identify poor responders should have sensitivity and specificity at a certain desired level. If these levels are set at 75 and 85%, respectively, it can be concluded from Figure 16 that hardly any study will fulfil these criteria. Moreover, in comparative studies the clinical performance of the CCCT in response prediction appeared not better than that of the AFC or FSH (Jain et al., 2004
; Hendriks et al., 2005c). Regarding prediction of non-pregnancy, desired levels for a test that excludes cases from entering an IVF program should arbitrarily be set at 40% for sensitivity and 95% for specificity. The vast majority of studies fail to reach both criteria as shown in Figure 17 As such the CCCT performs no better than other tests like the AFC or basal FSH, especially because of a loss in specificity.
Systematic review
We detected three studies from the literature reporting on the predictive capacity of the exogenous FSH ORT (EFORT) that were suitable for data extraction (Fanchin et al., 1994; Kwee et al., 2003; Yong et al., 2003). The characteristics of these studies are listed in addendum Table XXX.
|
Accuracy of poor response prediction
The individual values for sensitivity and specificity pairs are summarized in Table XXXI and plotted in Figure 18. As can be seen from this ROC space, the three detected studies report sensitivities around 80%, whereas specificities vary around 60% in the study of Kwee et al. and Yong et al. and above 90% in the study of Fanchin et al. In view of these different results between the studies, further assessment of heterogeneity appeared not useful and therefore a summary point or curve in the ROC space could not be constructed.
|
|
Accuracy of non-pregnancy prediction
No single study reported on the predictive accuracy using the outcome pregnancy as test reference.
Clinical value
Because of the absence of an estimated ROC curve for poor response prediction, the interrelation between positive LR, post-test probability and percentage of abnormal tests could not be calculated. It is considered that a challenge test used as a diagnostic tool to identify poor responders should have sensitivity and specificity at a certain desired level. If these levels are set at a minimum level of 75 and 85%, respectively, it can be concluded from Figure 18 that only one study fulfils these criteria (Fanchin et al., 1994). Especially, the false positive prediction may hamper the use of this test if a high level of detection is needed and patients are refused IVF on the basis of the test result. Finally, in comparison to basal tests, challenge tests should clearly improve prediction if they are to be preferred.
Gonadotrophin: releasing hormone agonist stimulation test
Systematic review
Through the search and selection strategy, a total of four studies reporting on the predictive capacity of the Gonadotrophin releasing hormone agonist stimulation test (GAST) were identified and considered suitable for data extraction and meta-analysis (Ranieri et al., 1998; Padilla et al., 1990; Winslow et al., 1991; Hendriks et al., 2005b). Characteristics of the included studies are listed in addendum Table XXXII. Considerable variation among the definitions of poor response and the study quality and design characteristics was observed, but as only three studies reported on each of the two endpoints, a systematic analysis of these study characteristics was not indicated.
|
Accuracy of poor response prediction
There were three studies that reported on the prediction of poor response. The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response, as calculated from each study, are summarized in Table XXXIII. Calculation of one summary point estimate for sensitivity and specificity was not meaningful as both test characteristics shown in Figure 19 were heterogeneous among studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity 0.014). As the Spearman correlation coefficient for sensitivity and specificity was –0.57, it appeared justified to estimate a summary ROC curve as shown in Figure 19.
|
|
Accuracy of non-pregnancy prediction
There were also three studies that reported on the capacity of the GAST to predict non-pregnancy after IVF. Sensitivities and specificities for the prediction of non-pregnancy, as calculated from each study, are summarized in Table XXXIV. Again, sensitivity and specificity, as shown in Figure 20, were heterogeneous between studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity 0.005). The Spearman correlation between sensitivity and specificity showed a coefficient of –0.98, sufficient to estimate a summary ROC curve (Figure 20).
|
|
Clinical value
Based on the summary ROC curves depicted in Figure 19, a range of positive LRs was calculated and for each ratio, pre-GAST-test probabilities of poor response or non-pregnancy (set at 20 and 80%, respectively) were converted into post-GAST-test probabilities. Table XXXV depicts the probability of obtaining a certain GAST test result and the corresponding LR within different LR ranges for the prediction of poor response and non-pregnancy. At a modest LR of 4–5, the post-GAST-test probability of poor response will not be higher than
50%, while the chance of obtaining such a test result is quite high, 49%. However, only with an extreme threshold a post-test probability of poor response that approaches 70% can be retained in a considerable number of cases (30%).
|
For prediction of non-pregnancy, extreme threshold levels are necessary to obtain a modest positive LR of 4–5, leading to a post-test pregnancy rate of approximately 5%. Such abnormal test results occur only in a very limited number of patients, while the false positive rate will lead to unnecessary exclusions from IVF programs if the test is used in a diagnostic fashion.
It can be concluded that with the use of the GAST in regularly cycling women, the accuracy in the prediction of poor response is quite high and could match with those obtained by the use of the AFC. For non-pregnancy prediction the test may only be adequate at a very low threshold level, where hardly any abnormal tests can be found. The results show that the GAST is a candidate for more extensive confirmation research.
Systematic review
Through the search and selection strategy, a total of nine studies reporting on the predictive capacity of several multivariate models were identified and considered suitable for data extraction and meta-analysis (Balasch et al., 1996; Ranieri et al., 1998; Creus et al., 2000; Fabregues et al., 2000; Bancsi et al., 2002a; van Rooij et al., 2002; Durmusoglu et al., 2004; Erdem et al., 2004; Muttukrishna et al., 2004). Characteristics of the included studies are listed in addendum Table XXXVI. As with most studies on ORTs, definitions for poor response varied considerably. It should be noted that none of the multifactor studies revealed usable data on pregnancy prediction. Moreover, the total number of cases included in these aggregated studies is modest (n=991).
|
Accuracy of poor response prediction
All ten studies only reported on the prediction of poor response. The sensitivities and specificities, the positive LR and the DOR for the prediction of poor ovarian response are summarized in Table XXXVII. Calculation of one summary point estimate for sensitivity and specificity did not appear to be possible, as both test characteristics (shown in Figure 21) were heterogeneous among studies (
2-test statistic: P-value for sensitivity <0.001 and P-value for specificity 0.014). As the Spearman correlation coefficient for sensitivity and specificity was –0.45, it appeared unjustified to estimate a summary ROC curve. Regression analysis showed that the performance of one particular test model was not superior to the other, as can also be seen in addendum Table XXXVI from the listing of sensitivities and specificities.
|
|
Clinical value
The impossibility of creating summary characteristics makes it difficult to assess the interrelation between positive LR, post-test probability and percentage of abnormal tests. Obviously, clinical value can only be discussed regarding prediction of poor response. It is considered that a challenge test used as a diagnostic tool to identify poor responders should have sensitivity and specificity at a certain desired level. If these levels are set at 75 and 85%, respectively, it can be concluded from Figure 21 that only one study will fulfil these criteria (Bancsi et al., 2002a
). Especially, the false positive prediction may hamper the use of this test if a high level of detection is needed and patients are refused IVF on the basis of this test. From these data it seems that compared to other ORTs, multifactor models do not create a definite improvement in predictive capacity. |
Implications for daily practice |
|---|
|
TOPAbstractIntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
With the postponement of childbearing, the age-related fertility decline has been shown to play an important role in the increase in infertility among couples who are trying to conceive. In IVF treatment, this age effect has been shown in much accumulated data. Because of the variation of female fertility within a certain age category, the need was felt for tests which better identified cases with a state of ovarian reserve that is clearly too low for their age. Because a benchmark for ovarian reserve status in the sense of quantity and quality is lacking, the occurrence of poor ovarian response to maximal stimulation and the occurrence of pregnancy in IVF are used as parameters to assess the accuracy of the test. The ideal ORT should identify a substantial percentage of IVF-indicated cases which have a practically zero chance of becoming pregnant because of the adverse effects of diminished ovarian reserve in a series of treatment cycles. Those cases can be refrained from entering the programme, as the very high costs involved will have only minimal results. If not too expensive and not too demanding for the patient, such a test would be readily embraced by physicians, patients, health politicians and insurance companies.
From the systematic and meta-analytic reviews presented here, it can be concluded that the ORTs known to date have only very modest predictive properties and are therefore far from suitable for relevant clinical use. Although mostly cheap and not very demanding, their accuracy, especially in the prediction of the occurrence of pregnancy, is very limited. If a high threshold is used, to prevent couples from wrongly being refused IVF, a very small minority of IVF-indicated cases (3%) were identified as having unfavourable prospects in an IVF treatment cycle (pregnancy rate for that cycle = 5%). It should be noted that the use of pregnancy as outcome parameter for the assessment of ovarian reserve status may be insufficient if only one exposure cycle is taken into account. As such, the possibility of misjudgement on the basis of currently known ORTs is hard to rule out. This implies that the use of the test as a method to deny treatment to assumed ovarian aged women should be declined and, as a consequence the test should not be applied on a regular basis and should only be used for counselling or screening purposes.
Accuracy of testing for the occurrence of poor ovarian response to stimulation appeared to be clearly better than for the occurrence of pregnancy. This may be understood in the light of the following factors: (i) that the chance of pregnancy after IVF depends on many more factors than ovarian reserve alone, (ii) that the occurrence of pregnancy after an ORT was usually evaluated in only one IVF cycle and as such may not accurately represent a female’s true reproductive capacity and (iii) that the response to stimulation is likely to represent the size of the cohort of FSH-sensitive follicles continuously present in the ovaries and is directly related to the magnitude of ovarian reserve (i.e. the remaining primordial follicle pool (Gougeon, 1984). Poor ovarian response has been associated with a reduced chance of pregnancy in the actual treatment cycle as well as in subsequent cycles and as such may well be indicative of ovarian reserve status in both the quantitative and qualitative sense (Ulug et al., 2003; Klinkert et al., 2004; Klinkert et al., 2005a). Accurate prediction of poor response could therefore have clinical value if the pregnancy prospects are so unfavourable that a predicted poor responder would be denied treatment. Accuracy in response prediction, however, will only be high if the false positives are prevented by using extreme threshold levels, implicating that only minor percentages of abnormal tests will be found and many future poor responders will pass unrecognized. At the same time it is necessary to know whether the predicted poor responder indeed has very low prospects for success in subsequent cycles. As much of this is unknown at the present time, the use of any ORT for poor response prediction cannot be supported, not even if it would be used for adapting the treatment schedule in anticipated poor responders, as an altered treatment schedule has consistently been shown to be effective in women with a severely reduced size of follicle cohort (Tarlatzis et al., 2003; Klinkert, 2005; Klinkert et al., 2005a).
One aspect of clinical value deserves some special attention. ORTs are mostly used as a diagnostic test, indicating that in case of an abnormal test result, the diagnosis that there is diminished ovarian reserve is made (Scott and Hofmann, 1995; Levi et al., 2001). From the fact that for evaluation of the test, proxy variables of true ovarian reserve (poor ovarian response and non-pregnancy) are used and that false positive test results may eliminate couples from the IVF trail even if they do have adequate prospects, it becomes clear that ORTs may better be considered as screening tests. All this implies that an abnormal test necessitates confirmation by another test. This other test may, for instance, be a first IVF attempt where ovarian response is the additional test. Alternatively, combinations of independent predictive tests or repeating of the initial test could improve the diagnostic performance of the single test (Ng et al., 2000; Bancsi et al., 2002b; van Rooij et al., 2002; Popovic-Todorovic et al., 2003a,b; Bancsi et al., 2004a,b).
As poor ovarian response will provide some information on ovarian reserve status, especially if the stimulation is maximal, entering the first cycle of IVF without any prior testing seems to be the preferable strategy. Once a poor response is obtained, the question arises whether this finding is based on depleted ovaries or other causes, like underdosing for instance, based on the presence of certain FSH receptor polymorphisms (Perez et al., 2000; Behre et al., 2005; Greb et al., 2005; de Koning et al., 2005). A repeat cycle with adequate, maximal stimulation or a post hoc-performed ORT [basal FSH or AFC (Hendriks et al., 2005c)] may correctly classify the poor responder patient as having an aged ovary and may correctly suggest that they refrain from further treatment (Klinkert et al., 2004).
It should be remembered that the purpose of any ORT is the identification of women with poor ovarian reserve for their age. This implies that chronological age always is the first step in ovarian reserve assessment. In young women, ORTs may help to classify poor responders and in direct management in these cases by estimating the size of the FSH-sensitive cohort. In older women, ORTs may help to identify those cases that, in spite of their age, still may have acceptable chances of becoming pregnant through IVF as the quantity of response to stimulation is anticipated to be normal or even high (Klinkert et al., 2005b).
Future perspectives in this research field may be found in studies where success rates in cumulative treatment cycles or in units of time (1-year treatment periods) are analysed to answer the question of whether any test will correctly identify those couples who will not become pregnant in such series of exposures. Novel tests that most accurately estimate the age at which menopause is expected to take place in an individual woman may facilitate the estimation of the remaining reproductive potential at a certain age. Such tests will probably be based on family history (age at menopause of mother) or will comprise testing for genetic markers, which may be discovered from large-scale population genetic screening.
|
Addendum |
|---|
|
TOPAbstractIntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
|
References |
|---|
|
TOPAbstractIntroductionThe assessment of ORImplications for daily practiceAddendumReferences |
|---|
Abma JC, Chandra A, Mosher WD, Peterson LS and Piccinino LJ (1997) Fertility, family planning, and women’s health: new data from the 1995 National Survey of Family Growth. Vital Health Stat 23,1–114.
Akande VA, Keay SD, Hunt LP, Mathur RS, Jenkins JM and Cahill DJ (2004) The practical implications of a raised serum FSH and age on the risk of IVF treatment cancellation because of a poor ovarian response. J Assist Reprod Genet 21,257–262.[CrossRef][Web of Science][Medline]
Balasch J, Creus M, Fabregues F, Carmona F, Casamitjana R, Ascaso C and Vanrell JA (1996) Inhibin, follicle-stimulating hormone, and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment. Am J Obstet Gynecol 175,1226–1230.[CrossRef][Web of Science][Medline]
Bancsi LF, Huijs AM, Den Ouden CT, Broekmans FJ, Looman CW, Blankenstein MA and te Velde ER (2000) Basal follicle-stimulating hormone levels are of limited value in predicting ongoing pregnancy rates after in vitro fertilization. Fertil Steril 73,552–557.[CrossRef][Web of Science][Medline]
Bancsi LF, Broekmans FJ, Eijkemans MJ, de Jong FH, Habbema JD, te Velde ER (2002a) Predictors of poor ovarian response in in vitro fertilization: a prospective study comparing basal markers of ovarian reserve. Fertil Steril 77,328–336.[CrossRef][Web of Science][Medline]
Bancsi LFJMM, Broekmans FJM, Eijkemans MJC, de Jong FH, Habbema JDF and te Velde ER (2002b) Predictors of poor ovarian response in in vitro fertilization: a prospective study comparing basal markers of ovarian reserve. Fertil Steril 77,328–336.[CrossRef][Web of Science][Medline]
Bancsi LF, Broekmans FJ, Mol BW, Habbema JD and te Velde ER (2003) Performance of basal follicle-stimulating hormone in the prediction of poor ovarian response and failure to become pregnant after in vitro fertilization: a meta-analysis. Fertil Steril 79,1091–1100.[CrossRef][Web of Science][Medline]
Bancsi LF, Broekmans FJ, Looman CW, Habbema JD and te Velde ER (2004a) Impact of repeated antral follicle counts on the prediction of poor ovarian response in women undergoing in vitro fertilization. Fertil Steril 81,35–41.[Web of Science][Medline]
Bancsi LF, Broekmans FJ, Looman CW, Habbema JD and te Velde ER (2004b) Predicting poor ovarian response in IVF: use of repeat basal FSH measurement. J Reprod Med 49,187–194.[Web of Science][Medline]
Bassil S, Godin PA, Gillerot S, Verougstraete JC and Donnez J (1999) In vitro fertilization outcome according to age and follicle-stimulating hormone levels on cycle day 3. J Assist Reprod Genet 16,236–241.[CrossRef][Web of Science][Medline]
Behre HM, Greb RR, Mempel A, Sonntag B, Kiesel L, Kaltwasser P, Seliger E, Ropke F, Gromoll J, Nieschlag E and Simoni M (2005) Significance of a common single nucleotide polymorphism in exon 10 of the follicle-stimulating hormone (FSH) receptor gene for the ovarian response to FSH: a pharmacogenetic approach to controlled ovarian hyperstimulation. Pharmacogenet Genomics 15,451–456.[Web of Science][Medline]
Block E (1952) Quantitative morphological investigations of the follicular system in women. Variations at different ages. Acta Anat (Basel) 14 (Suppl 16),108–123.[Web of Science][Medline]
de Boer EJ, den TI, te Velde ER, Burger CW, Klip H and van Leeuwen FE (2002) A low number of retrieved oocytes at in vitro fertilization treatment is predictive of early menopause. Fertil Steril 77,978–985.[CrossRef][Web of Science][Medline]
de Bruin JP and te Velde ER (2004) Female reproductive ageing: concepts and consequences. In Tulandi T and Gosden RG (eds) Preservation of Fertility. London, UK: Taylor & Francis, p. 3.
Chae HD, Kim CH, Kang BM and Chang YS (2000) Clinical usefulness of basal FSH as a prognostic factor in patients undergoing intracytoplasmic sperm injection. J Obstet Gynaecol Res 26,55–60.[Medline]
Chan YF, Ho PC, So WW and Yeung WS (1993) Basal serum pituitary hormone levels and outcome of in vitro fertilization utilizing a flare nasal gonadotropin releasing hormone agonist and fixed low-dose follicle-stimulating hormone/human menopausal gonadotropin regimen. J Assist Reprod Genet 10,251–254.[CrossRef][Web of Science][Medline]
Chang MY, Chiang CH, Chiu TH, Hsieh TT, Soong YK (1998a). The antral follicle count predicts the outcome of pregnancy in a controlled ovarian hyperstimulation/intrauterine insemination program. J Assist Reprod Genet 15,12–17.[CrossRef][Web of Science][Medline]
Chang MY, Chiang CH, Hsieh TT, Soong YK and Hsu KH (1998b) Use of the antral follicle count to predict the outcome of assisted reproductive technologies. Fertil Steril 69,505–510.[CrossRef][Web of Science][Medline]
Chuang CC, Chen CD, Chao KH, Chen SU, Ho HN and Yang YS (2003) Age is a better predictor of pregnancy potential than basal follicle-stimulating hormone levels in women undergoing in vitro fertilization. Fertil Steril 79,63–68.[CrossRef][Web of Science][Medline]
Collins JA, Burrows EA and Wilan AR (1995) The prognosis for live birth among untreated infertile couples. Fertil Steril 64,22–28.[Web of Science][Medline]
Creus M, Penarrubia J, Fabregues F, Vidal E, Carmona F, Casamitjana R, Vanrell JA and Balasch J (2000) Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome. Hum Reprod 15,2341–2346.
Csemiczky G, Wramsby H and Landgren BM (1996) Luteal phase oestradiol and progesterone levels are stronger predictors than follicular phase follicle stimulating hormone for the outcome of in-vitro fertilization treatment in women with tubal infertility. Hum Reprod 11,2396–2399.
Csemiczky G, Harlin J and Fried G (2002) Predictive power of clomiphene citrate challenge test for failure of in vitro fertilization treatment. Acta Obstet Gynecol Scand 81,954–961.[CrossRef][Web of Science][Medline]
Deeks JJ (2001) Systematic reviews in health care: systematic reviews of evaluations of diagnostic and screening tests. BMJ 323,157–162.
Deville WL, Buntinx F, Bouter LM, Montori VM, de Vet HC, van der Windt DA and Bezemer PD (2002) Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2,9.[CrossRef][Medline]
Durmusoglu F, Elter K, Yoruk P and Erenus M (2004) Combining cycle day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response. Fertil Steril 81,1073–1078.[CrossRef][Web of Science][Medline]
Ebrahim A, Rienhardt G, Morris S, Kruger TF, Lombard CJ and Van der Merwe JP (1993) Follicle stimulating hormone levels on cycle day 3 predict ovulation stimulation response. J Assist Reprod Genet 10,130–136.[CrossRef][Web of Science][Medline]
Eimers JM, te Velde ER, Gerritse R, Vogelzang ET, Looman CW and Habbema JD (1994). The prediction of the chance to conceive in subfertile couples. Fertil Steril 61,44–52.[Web of Science][Medline]
Engmann L, Sladkevicius P, Agrawal R, Bekir J, Campbell S and Tan SL (1999a) The pattern of changes in ovarian stromal and uterine artery blood flow velocities during in vitro fertilization treatment and its relationship with outcome of the cycle. Ultrasound Obstet Gynecol 13,26–33.[CrossRef][Web of Science][Medline]
Engmann L, Sladkevicius P, Agrawal R, Bekir JS, Campbell S and Tan SL (1999b) Value of ovarian stromal blood flow velocity measurement after pituitary suppression in the prediction of ovarian responsiveness and outcome of in vitro fertilization treatment. Fertil Steril 71,22–29.[CrossRef][Web of Science][Medline]
Erdem A, Erdem M, Biberoglu K, Hayit O, Arslan M and Gursoy R (2002) Age-related changes in ovarian volume, antral follicle counts and basal FSH in women with normal reproductive health. J Reprod Med 47,835–839.[Web of Science][Medline]
Erdem M, Erdem A, Gursoy R and Biberoglu K (2004) Comparison of basal and clomiphene citrate induced FSH and inhibin B, ovarian volume and antral follicle counts as ovarian reserve tests and predictors of poor ovarian response in IVF. J Assist Reprod Genet 21,37–45.[CrossRef][Web of Science][Medline]
Esposito MA, Coutifaris C and Barnhart KT (2002) A moderately elevated day 3 FSH concentration has limited predictive value, especially in younger women. Hum Reprod 17,118–123.
Evers JL, Slaats P, Land JA, Dumoulin JC and Dunselman GA (1998) Elevated levels of basal estradiol-17beta predict poor response in patients with normal basal levels of follicle-stimulating hormone undergoing in vitro fertilization. Fertil Steril 69,1010–1014.[CrossRef][Web of Science][Medline]
Fabregues F, Balasch J, Creus M, Carmona F, Puerto B, Quinto L, Casamitjana R and Vanrell JA (2000) Ovarian reserve test with human menopausal gonadotropin as a predictor of in vitro fertilization outcome. J Assist Reprod Genet 17,13–19.[CrossRef][Web of Science][Medline]
Fanchin R, de Ziegler D, Olivennes F, Taieb J, Dzik A and Frydman R (1994) Exogenous follicle stimulating hormone ovarian reserve test (EFORT): a simple and reliable screening test for detecting ‘poor responders’ in in-vitro fertilization. Hum Reprod 9,1607–1611.
Fasouliotis SJ, Simon A and Laufer N (2000) Evaluation and treatment of low responders in assisted reproductive technology: a challenge to meet. J Assist Reprod Genet 17,357–373.[CrossRef][Web of Science][Medline]
Fiçiciolu C, Kutlu T, Demirbasoglu S and Mulayim B (2003) The role of inhibin B as a basal determinant of ovarian reserve. Gynecol Endocrinol 17,287–293.[Web of Science][Medline]
Fisch JD and Sher G (2002) The antral follicle count (AFC) correlates with the metaphase II oocytes and ART cycle outcome: an update. Fertil Steril 78,S90.
Frattarelli JL, Lauria-Costab DF, Miller BT, Bergh PA and Scott RT (2000) Basal antral follicle number and mean ovarian diameter predict cycle cancellation and ovarian responsiveness in assisted reproductive technology cycles. Fertil Steril 74,512–517.[CrossRef][Web of Science][Medline]
Frattarelli JL, Levi AJ, Miller BT and Segars JH (2003) A prospective assessment of the predictive value of basal antral follicles in in vitro fertilization cycles. Fertil Steril 80,350–355.[CrossRef][Web of Science][Medline]
Glas AS, Lijmer JG, Prins MH, Bonsel GJ and Bossuyt PM (2003) The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 56,1129–1135.[CrossRef][Web of Science][Medline]
Gougeon A (1984) Caracteres qualitatifs et quantitatifs de la population folliculaire dans l’ovaire humaine adulte. Contracept Fertil Sex 12(3),527–535.
Greb RR, Grieshaber K, Gromoll J, Sonntag B, Nieschlag E, Kiesel L and Simoni M (2005) A common single nucleotide polymorphism in exon 10 of the human follicle stimulating hormone receptor is a major determinant of length and hormonal dynamics of the menstrual cycle. J Clin Endocrinol Metab 90,4866–4872.
Grimes DA and Schulz KF (2005) Refining clinical diagnosis with likelihood ratios. Lancet 365,1500–1505.[CrossRef][Web of Science][Medline]
Gulekli B, Bulbul Y, Onvural A, Yorukoglu K, Posaci C, Demir N and Erten O (1999) Accuracy of ovarian reserve tests. Hum Reprod 14,2822–2826.
Gurgan T, Urman B, Yarali H and Duran HE (1997) Follicle-stimulating hormone levels on cycle day 3 to predict ovarian response in women undergoing controlled ovarian hyperstimulation for in vitro fertilization using a flare-up protocol. Fertil Steril 68,483–487.[CrossRef][Web of Science][Medline]
Hall JE, Welt CK and Cramer DW (1999) Inhibin A and inhibin B reflect ovarian function in assisted reproduction but are less useful at predicting outcome. Hum Reprod 14,409–415.
Hazout A, Bouchard P, Seifer DB, Aussage P, Junca AM and Cohen-Bacrie P (2004) Serum antimullerian hormone/mullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone, inhibin B, or estradiol. Fertil Steril 82,1323–1329.[CrossRef][Web of Science][Medline]
Hendriks DJ, Broekmans FJ, Bancsi LF, de Jong FH, Looman CW and te Velde ER (2005a) Repeated clomiphene citrate challenge testing in the prediction of outcome in IVF: a comparison with basal markers for ovarian reserve. Hum Reprod 20,163–169.
Hendriks DJ, Broekmans FJ, Bancsi LF, Looman CW, de Jong FH and te Velde ER (2005b) Single and repeated GnRH agonist stimulation tests compared with basal markers of ovarian reserve in the prediction of outcome in IVF. J Assist Reprod Genet 22,65–73.[CrossRef][Web of Science][Medline]
Hendriks DJ, Mol BW, Bancsi LF, te Velde ER and Broekmans FJ (2005c) Antral follicle count in the prediction of poor ovarian response and pregnancy after in vitro fertilization: a meta-analysis and comparison with basal follicle-stimulating hormone level. Fertil Steril 83,291–301.[CrossRef][Web of Science][Medline]
Hendriks DJ, te Velde ER, Looman CW, Bancsi LF and Broekmans FJ (2005d). The role of poor response in the prediction of the cumulative ongoing pregnancy rate in in vitro fertilisation. Dynamic and basal ovarian reserve tests for outcome prediction in IVF: comparisons and meta-analyses. Academic Thesis, Utrecht, 162–179.
Honest H and Khan KS (2002) Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC Health Serv Res 2,4.[CrossRef][Medline]
Hsieh YY, Chang CC and Tsai HD (2001) Antral follicle counting in predicting the retrieved oocyte number after ovarian hyperstimulation. J Assist Reprod Genet 18,320–324.[CrossRef][Web of Science][Medline]
Hunault CC, Habbema JD, Eijkemans MJ, Collins JA, Evers JL and te Velde ER (2004) Two new prediction rules for spontaneous pregnancy leading to live birth among subfertile couples, based on the synthesis of three previous models. Hum Reprod 19,2019–2026.
Hunault CC, Laven JS, van Rooij IA, Eijkemans MJ te Velde ER and Habbema JD (2005) Prospective validation of two models predicting pregnancy leading to live birth among untreated subfertile couples. Hum Reprod 20,1636–1641.
Huyser C, Fourie FL, Pentz J and Hurter P (1995) The predictive value of basal follicle stimulating and growth hormone levels as determined by immunofluorometry during assisted reproduction. J Assist Reprod Genet 12,244–251.[CrossRef][Web of Science][Medline]
Irwig L, Macaskill P, Glasziou P and Fahey M (1995) Meta-analytic methods for diagnostic test accuracy. J Clin Epidemiol 48,119–130.[CrossRef][Web of Science][Medline]
Irwig L, Tosteson AN, Gatsonis C, Lau J, Colditz G, Chalmers TC and Mosteller F (1994) Guidelines for meta-analyses evaluating diagnostic tests. Ann Intern Med 120,667–676.
Jain T, Soules MR and Collins JA (2004) Comparison of basal follicle-stimulating hormone versus the clomiphene citrate challenge test for ovarian reserve screening. Fertil Steril 82,180–185.[CrossRef][Web of Science][Medline]
Jarvela IY, Sladkevicius P, Kelly S, Ojha K, Campbell S and Nargund G (2003) Quantification of ovarian power Doppler signal with three-dimensional ultrasonography to predict response during in vitro fertilization. Obstet Gynecol 102,816–822.[CrossRef][Web of Science][Medline]
Jinno M, Hoshiai T, Nakamura Y, Teruya K and Tsunoda T (2000) A novel method for assessing assisted female fertility: bioelectric impedance. J Clin Endocrinol Metab 85,471–474.[Abstract]
Kahraman S, Vicdan K, Isik AZ, Ozgun OD, Alaybeyoglu L, Polat G and Biberoglu K (1997) Clomiphene citrate challenge test in the assessment of ovarian reserve before controlled ovarian hyperstimulation for intracytoplasmic sperm injection. Eur J Obstet Gynecol Reprod Biol 73,177–182.[CrossRef][Web of Science][Medline]
Khalifa E, Toner JP, Muasher SJ, Acosta AA (1992). Significance of basal follicle-stimulating hormone levels in women with one ovary in a program of in vitro fertilization. Fertil Steril 57,835–839.[Web of Science][Medline]
Kim SH, Ku SY, Jee BC, Suh CS, Moon SY and Lee JY (2002) Clinical significance of transvaginal color Doppler ultrasonography of the ovarian artery as a predictor of ovarian response in controlled ovarian hyperstimulation for in vitro fertilization and embryo transfer. J Assist Reprod Genet 19,103–112.[CrossRef][Web of Science][Medline]
Klinkert ER (2005) Clinical significance and management of poor response in IVF. Academic Thesis, Utrecht.
Klinkert ER, Broekmans FJ Looman CW and te Velde ER (2004) A poor response in the first in vitro fertilization cycle is not necessarily related to a poor prognosis in subsequent cycles. Fertil Steril 81,1247–1253.[CrossRef][Web of Science][Medline]
Klinkert ER, Broekmans FJ, Looman CW, Habbema JD and te Velde ER (2005a) Expected poor responders on the basis of an antral follicle count do not benefit from a higher starting dose of gonadotrophins in IVF treatment: a randomized controlled trial. Hum Reprod 20,611–615.
Klinkert ER, Broekmans FJ, Looman CW, Habbema JD and te Velde ER (2005b) The antral follicle count is a better marker than basal follicle-stimulating hormone for the selection of older patients with acceptable pregnancy prospects after in vitro fertilization. Fertil Steril 83,811–814.[CrossRef][Web of Science][Medline]
de Koning CH, Benjamins T, Harms P, Homburg R, van Montfrans JM, Gromoll J, Simoni M and Lambalk CB (2006) The distribution of FSH receptor isoforms is related to basal FSH levels in subfertile women with normal menstrual cycles. Hum Reprod 21,443–446.
van Kooij RJ, Looman CW, Habbema JD, Dorland M and te Velde ER (1996) Age-dependent decrease in embryo implantation rate after in vitro fertilization. Fertil Steril 66,769–775.[Web of Science][Medline]
Kupesic S and Kurjak A (2002) Predictors of IVF outcome by three-dimensional ultrasound. Hum Reprod 17,950–955.
Kupesic S, Kurjak A, Bjelos D and Vujisic S (2003) Three-dimensional ultrasonographic ovarian measurements and in vitro fertilization outcome are related to age. Fertil Steril 79,190–197.[CrossRef][Web of Science][Medline]
Kwee J, Elting MW, Schats R, Bezemer PD, Lambalk CB and Schoemaker J (2003) Comparison of endocrine tests with respect to their predictive value on the outcome of ovarian hyperstimulation in IVF treatment: results of a prospective randomized study. Hum Reprod 18,1422–1427.
Lambalk CB, de Koning CH, Flett A, van Kasteren Y, Gosden R and Homburg R (2004) Assessment of ovarian reserve. Ovarian biopsy is not a valid method for the prediction of ovarian reserve. Hum Reprod 19,1055–1059.
Lass A (2001) Assessment of ovarian reserve – is there a role for ovarian biopsy? Hum Reprod 16,1055–1057.
Lass A (2004) Assessment of ovarian reserve: is there still a role for ovarian biopsy in the light of new data? Hum Reprod 19,467–469.
Lass A, Silye R, Abrams DC, Krausz T, Hovatta O, Margara R and Winston RM (1997a) Follicular density in ovarian biopsy of infertile women: a novel method to assess ovarian reserve. Hum Reprod 12,1028–1031.
Lass A, Skull J, McVeigh E, Margara R and Winston RM (1997b) Measurement of ovarian volume by transvaginal sonography before ovulation induction with human menopausal gonadotrophin for in- vitro fertilization can predict poor response. Hum Reprod 12,294–297.
Lawson R, El Toukhy T, Kassab A, Taylor A, Braude P, Parsons J and Seed P (2003) Poor response to ovulation induction is a stronger predictor of early menopause than elevated basal FSH: a life table analysis. Hum Reprod 18,527–533.
Leridon H (1998) [30 years of contraception in France]. Contracept Fertil Sex 26,435–438.[Web of Science][Medline]
Levi AJ, Raynault MF, Bergh PA, Drews MR, Miller BT and Scott RT (2001) Reproductive outcome in patients with diminished ovarian reserve. Fertil Steril 76,666–669.[CrossRef][Web of Science][Medline]
Licciardi FL, Liu HC and Rosenwaks Z (1995) Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization. Fertil Steril 64,991–994.[Web of Science][Medline]
Littenberg B and Moses LE (1993) Estimating diagnostic accuracy from multiple conflicting reports: a new meta-analytic method. Med Decis Making 13,313–321.
Loumaye E, Billion JM, Mine JM, Psalti I, Pensis M and Thomas K (1990) Prediction of individual response to controlled ovarian hyperstimulation by means of a clomiphene citrate challenge test. Fertil Steril 53,295–301.[Web of Science][Medline]
Martin JS, Nisker JA, Tummon IS, Daniel SA, Auckland JL and Feyles V (1996) Future in vitro fertilization pregnancy potential of women with variably elevated day 3 follicle-stimulating hormone levels. Fertil Steril 65,1238–1240.[Web of Science][Medline]
Midgette AS, Stukel TA and Littenberg B (1993) A meta-analytic method for summarizing diagnostic test performances: receiver-operating-characteristic-summary point estimates. Med Decis Making 13,253–257.
Mikkelsen AL, Andersson AM, Skakkebaek NE and Lindenberg S (2001) Basal concentrations of oestradiol may predict the outcome of in-vitro maturation in regularly menstruating women. Hum Reprod 16,862–867.
Mol BW, Dijkman B, Wertheim P, Lijmer J, van d V and Bossuyt PM (1997) The accuracy of serum chlamydial antibodies in the diagnosis of tubal pathology: a meta-analysis. Fertil Steril 67,1031–1037.[CrossRef][Web of Science][Medline]
Moses LE, Shapiro D and Littenberg B (1993) Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat Med 12,1293–1316.[Web of Science][Medline]
Muttukrishna S, Suharjono H, McGarrigle H and Sathanandan M (2004) Inhibin B and anti-Mullerian hormone: markers of ovarian response in IVF/ICSI patients? BJOG 111,1248–1253.[Web of Science][Medline]
Muttukrishna S, McGarrigle H, Wakim R, Khadum I, Ranieri DM and Serhal P (2005) Antral follicle count, anti-mullerian hormone and inhibin B: predictors of ovarian response in assisted reproductive technology? BJOG 112,1384–1390.[Web of Science][Medline]
Nahum R, Shifren JL, Chang Y, Leykin L, Isaacson K and Toth TL (2001) Antral follicle assessment as a tool for predicting outcome in IVF – is it a better predictor than age and FSH? J Assist Reprod Genet 18,151–155.[CrossRef][Web of Science][Medline]
National Collaborating Center for Women’s and Children’s Health. (2004) Fertility: Assessment and Treatment for People with Fertility Problems. RCOG press, UK.
Ng EH, Tang OS and Ho PC (2000) The significance of the number of antral follicles prior to stimulation in predicting ovarian responses in an IVF programme. Hum Reprod 15,1937–1942.
Padilla SL, Bayati J and Garcia JE (1990) Prognostic value of the early serum estradiol response to leuprolide acetate in in vitro fertilization. Fertil Steril 53,288–294.[Web of Science][Medline]
Penarrubia J, Balasch J, Fabregues F, Carmona F, Casamitjana R, Moreno V, Calafell JM and Vanrell JA (2000) Day 5 inhibin B serum concentrations as predictors of assisted reproductive technology outcome in cycles stimulated with gonadotrophin-releasing hormone agonist-gonadotrophin treatment. Hum Reprod 15,1499–1504.
Penarrubia J, Fabregues F, Manau D, Creus M, Casals G, Casamitjana R, Carmona F, Vanrell JA and Balasch J (2005) Basal and stimulation day 5 anti-Mullerian hormone serum concentrations as predictors of ovarian response and pregnancy in assisted reproductive technology cycles stimulated with gonadotropin-releasing hormone agonist – gonadotropin treatment. Hum Reprod 20,915–922.
Perez MM, Gromoll J, Behre HM, Gassner C, Nieschlag E and Simoni M (2000) Ovarian response to follicle-stimulating hormone (FSH) stimulation depends on the FSH receptor genotype. J Clin Endocrinol Metab 85,3365–3369.
Phophong P, Ranieri DM, Khadum I, Meo F and Serhal P (2000) Basal 17beta-estradiol did not correlate with ovarian response and in vitro fertilization treatment outcome. Fertil Steril 74,1133–1136.[CrossRef][Web of Science][Medline]
Popovic-Todorovic B, Loft A, Bredkjaeer HE, Bangsboll S, Nielsen IK and Andersen AN (2003a) A prospective randomized clinical trial comparing an individual dose of recombinant FSH based on predictive factors versus a ‘standard’ dose of 150 IU/day in ‘standard’ patients undergoing IVF/ICSI treatment. Hum Reprod 18,2275–2282.
Popovic-Todorovic B, Loft A, Lindhard A, Bangsboll S, Andersson AM and Andersen AN (2003b) A prospective study of predictive factors of ovarian response in ‘standard’ IVF/ICSI patients treated with recombinant FSH. A suggestion for a recombinant FSH dosage normogram. Hum Reprod 18,781–787.
Anonymous (1995) Pregnancies and births resulting from in vitro fertilization: French national registry analysis of data 1986 to 1990. FIVNAT (French In Vitro National). Fertil Steril 64,746–756.[Web of Science][Medline]
Pruksananonda K, Boonkasemsanti W and Virutamasen P (1996) Basal follicle – stimulating hormone levels on day 3 of previous cycle are predictive of in vitro fertilization outcome. J Med Assoc Thai 79,365–369.[Medline]
Qu J, Godin PA, Nisolle M and Donnez J (2000) Distribution and epidermal growth factor receptor expression of primordial follicles in human ovarian tissue before and after cryopreservation. Hum Reprod 15,302–310.
Ranieri DM, Quinn F, Makhlouf A, Khadum I, Ghutmi W, McGarrigle H, Davies M and Serhal P (1998) Simultaneous evaluation of basal follicle-stimulating hormone and 17 beta-estradiol response to gonadotropin-releasing hormone analogue stimulation: an improved predictor of ovarian reserve. Fertil Steril 70,227–233.[CrossRef][Web of Science][Medline]
Ranieri DM, Phophong P, Khadum I, Meo F, Davis C and Serhal P (2001) Simultaneous evaluation of basal FSH and oestradiol response to GnRH analogue (F-G-test) allows effective drug regimen selection for IVF. Hum Reprod 16,673–675.
Roberts JE, Spandorfer S, Fasouliotis SJ, Kashyap S and Rosenwaks Z (2005) Taking a basal follicle-stimulating hormone history is essential before initiating in vitro fertilization. Fertil Steril 83,37–41.[CrossRef][Web of Science][Medline]
van Rooij IAJ, Broekmans FJ, te Velde ER, Fauser BCJM, Bancsi LFJMM, de Jong FH and Themmen APN (2002) Serum anti-Mullerian hormone levels: a novel measure of ovarian reserve. Hum Reprod 17,101–107.
van Rooij IA, Broekmans FJ, Hunault CC, Scheffer GJ, Eijkemans MJ, de Jong FH, Themmen AP and te Velde ER (2006) The use of ovarian reserve tests for the prediction of ongoing pregnancy in couples with unexplained female subfertility. Reprod Biomed Online 12,182–190.[Web of Science][Medline]
Schild R L, Knobloch C, Dorn C, Fimmers R, van d V and Hansmann M (2001). The role of ovarian volume in an in vitro fertilization programme as assessed by 3D ultrasound. Arch Gynecol Obstet 265,67–72.[CrossRef][Medline]
Schmidt KL, Ernst E, Byskov AG, Nyboe AA and Yding AC (2003) Survival of primordial follicles following prolonged transportation of ovarian tissue prior to cryopreservation. Hum Reprod 18,2654–2659.
Scott RT Jr and Hofmann GE (1995) Prognostic assessment of ovarian reserve [see comments]. Fertil Steril 63,1–11.[Web of Science][Medline]
Scott RT, Toner JP, Muasher SJ, Oehninger S, Robinson S and Rosenwaks Z (1989) Follicle-stimulating hormone levels on cycle day 3 are predictive of in vitro fertilization outcome. Fertil Steril 51,651–654.[Web of Science][Medline]
Seifer DB, Lambert Messerlian G, Hogan JW, Gardiner AC, Blazar AS and Berk CA (1997) Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome [see comments]. Fertil Steril 67,110–114.[CrossRef][Web of Science][Medline]
Sharara FI and McClamrock HD (1999) The effect of aging on ovarian volume measurements in infertile women. Obstet Gynecol 94,57–60.[CrossRef][Web of Science][Medline]
Sharara FI and McClamrock HD (2000) Antral follicle count and ovarian volume predict IVF outcome. Fertil Steril 74,S176.
Sharara FI and Scott RT (2004) Assessment of ovarian reserve. Is there still a role for ovarian biopsy? First do no harm! Hum Reprod 19,470–471.
Sharif K, Elgendy M, Lashen H and Afnan M (1998) Age and basal follicle stimulating hormone as predictors of in vitro fertilisation outcome. Br J Obstet Gynaecol 105,107–112.[Web of Science][Medline]
Sharma V, Allgar V and Rajkhowa M (2002) Factors influencing the cumulative conception rate and discontinuation of in vitro fertilization treatment for infertility. Fertil Steril 78,40–46.[CrossRef][Web of Science][Medline]
Smeenk JM, Stolwijk AM, Kremer JA and Braat DD (2000) External validation of the templeton model for predicting success after IVF. Hum Reprod 15,1065–1068.
Smotrich DB, Widra EA, Gindoff PR, Levy MJ, Hall JL and Stillman RJ (1995) Prognostic value of day 3 estradiol on in vitro fertilization outcome. Fertil Steril 64,1136–1140.[Web of Science][Medline]
Snick HK, Snick TS, Evers JL and Collins JA (1997) The spontaneous pregnancy prognosis in untreated subfertile couples: the Walcheren primary care study. Hum Reprod 12,1582–1588.
Spira A (1988) The decline of fecundity with age. Raturitas 10 (Suppl),15–22.
van der Stege JG and van der Linden PJ (2001) Useful predictors of ovarian stimulation response in women undergoing in vitro fertilization. Gynecol Obstet Invest 52,43–46.[CrossRef][Web of Science][Medline]
Stolwijk AM, Zielhuis GA, Hamilton CJ, Straatman H, Hollanders JM, Goverde HJ, van Dop PA and Verbeek AL (1996) Prognostic models for the probability of achieving an ongoing pregnancy after in-vitro fertilization and the importance of testing their predictive value. Hum Reprod 11,2298–2303.
Stolwijk AM, Straatman H, Zielhuis GA, Jansen CA, Braat DD, van Dop PA and Verbeek AL (1998) External validation of prognostic models for ongoing pregnancy after in-vitro fertilization. Hum Reprod 13,3542–3549.
Syrop CH, Willhoite A and Van Voorhis BJ (1995) Ovarian volume: a novel outcome predictor for assisted reproduction. Fertil Steril 64,1167–1171.[Web of Science][Medline]
Tanbo T, Dale PO, Abyholm T and Stokke KT (1989) Follicle-stimulating hormone as a prognostic indicator in clomiphene citrate/human menopausal gonadotrophin-stimulated cycles for in-vitro fertilization. Hum Reprod 4,647–650.
Tanbo T, Abyholm T, Bjoro T and Dale PO (1990) Ovarian stimulation in previous failures from in-vitro fertilization: distinction of two Groups of poor responders. Hum Reprod 5,811–815.
Tanbo T, Dale PO, Lunde O, Norman N and Abyholm T (1992) Prediction of response to controlled ovarian hyperstimulation: a comparison of basal and clomiphene citrate-stimulated follicle- stimulating hormone levels. Fertil Steril 57,819–824.[Web of Science][Medline]
Tarlatzis BC, Zepiridis L, Grimbizis G and Bontis J (2003) Clinical management of low ovarian response to stimulation for IVF: a systematic review. Hum Reprod Update 9,61–76.
Templeton A, Morris JK and Parslow W (1996) Factors that affect outcome of in-vitro fertilisation treatment [see comments]. Lancet 348,1402–1406.[CrossRef][Web of Science][Medline]
Toner JP, Philput CB, Jones GS and Muasher SJ (1991) Basal follicle-stimulating hormone level is a better predictor of in vitro fertilization performance than age. Fertil Steril 55,784–791.[Web of Science][Medline]
Ulug U, Ben Shlomo I, Turan E, Erden HF, Akman MA and Bahceci M (2003) Conception rates following assisted reproduction in poor responder patients: a retrospective study in 300 consecutive cycles. Reprod Biomed Online 6,439–443.[Medline]
Vazquez ME, Verez JR, Stern JJ, Gutierrez NA and Asch RH (1998) Elevated basal estradiol levels have no negative prognosis in young women undergoing ART cycles. Gynecol Endocrinol 12,155–159.[Web of Science][Medline]
te Velde ER and Pearson PL (2002) The variability of female reproductive aging. Hum Reprod Update 8,141–154.
Ventura SJ, Mosher WD, Curtin SC, Abma JC and Henshaw S (2001) Trends in pregnancy rates for the United States 1976–97: an update. Natl Vital Stat Rep 49,1–9.[Medline]
Webber LJ, Stubbs S, Stark J, Trew GH, Margara R, Hardy K and Franks S (2003) Formation and early development of follicles in the polycystic ovary. Lancet 362,1017–1021.[CrossRef][Web of Science][Medline]
Weinstein M, Wood AJ and Chang MC (1993) Age patterns in fecundability. In Gray R, Leridon H and Spira A (eds) Biomedical and Demographic Determinants of Reproduction. Clarendon Press, Oxford, pp. 209–220.
Winslow KL, Toner JP, Brzyski RG, Oehninger SC, Acosta AA and Muasher SJ (1991) The gonadotropin-releasing hormone agonist stimulation test – a sensitive predictor of performance in the flare-up in vitro fertilization cycle. Fertil Steril 56,711–717.[Web of Science][Medline]
Wood JW (1989) Fecundity and natural fertility in humans. Oxf Rev Reprod Biol 11,61–109.[Medline]
Yanushpolsky EH, Hurwitz S, Tikh E and Racowsky C (2003) Predictive usefulness of cycle day 10 follicle-stimulating hormone level in a clomiphene citrate challenge test for in vitro fertilization outcome in women younger than 40 years of age. Fertil Steril 80,111–115.[Web of Science][Medline]
Yong PY, Baird DT, Thong KJ, McNeilly AS and Anderson RA (2003) Prospective analysis of the relationships between the ovarian follicle cohort and basal FSH concentration, the inhibin response to exogenous FSH and ovarian follicle number at different stages of the normal menstrual cycle and after pituitary down-regulation. Hum Reprod 18,35–44.
Zaidi J, Barber J, Kyei Mensah A, Bekir J, Campbell S and Tan SL (1996). Relationship of ovarian stromal blood flow at the baseline ultrasound scan to subsequent follicular response in an in vitro fertilization program. Obstet Gynecol 88,779–784.[CrossRef][Web of Science][Medline]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J.-N. Hugues, L. Theron-Gerard, C. Coussieu, M. Pasquier, D. Dewailly, and I. Cedrin-Durnerin Assessment of theca cell function prior to controlled ovarian stimulation: the predictive value of serum basal/stimulated steroid levels Hum. Reprod., November 6, 2009; (2009) dep378v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Demeestere, P. Simon, S. Emiliani, A. Delbaere, and Y. Englert Orthotopic and heterotopic ovarian tissue transplantation Hum. Reprod. Update, November 1, 2009; 15(6): 649 - 665. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Brodin, T. Bergh, L. Berglund, N. Hadziosmanovic, and J. Holte High basal LH levels in combination with low basal FSH levels are associated with high success rates at assisted reproduction Hum. Reprod., November 1, 2009; 24(11): 2755 - 2759. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Kyrou, B. Popovic-Todorovic, H.M. Fatemi, C. Bourgain, P. Haentjens, L. Van Landuyt, and P. Devroey Does the estradiol level on the day of human chorionic gonadotrophin administration have an impact on pregnancy rates in patients treated with rec-FSH/GnRH antagonist? Hum. Reprod., November 1, 2009; 24(11): 2902 - 2909. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. A. Wise, K. J. Rothman, E. M. Mikkelsen, H. T. Sorensen, A. Riis, and E. E. Hatch An internet-based prospective study of body size and time-to-pregnancy Hum. Reprod., October 14, 2009; (2009) dep360v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. La Marca, G. Sighinolfi, D. Radi, C. Argento, E. Baraldi, A. C. Artenisio, G. Stabile, and A. Volpe Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum. Reprod. Update, September 30, 2009; (2009) dmp036v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Deb, M. Batcha, B.K. Campbell, K. Jayaprakasan, J.S. Clewes, J.F. Hopkisson, C. Sjoblom, and N.J. Raine-Fenning The predictive value of the automated quantification of the number and size of small antral follicles in women undergoing ART Hum. Reprod., September 1, 2009; 24(9): 2124 - 2132. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. La Marca, F.J. Broekmans, A. Volpe, B.C. Fauser, N.S. Macklon, and on behalf of the ESHRE Special Interest Group for Anti-Mullerian hormone (AMH): what do we still need to know? Hum. Reprod., September 1, 2009; 24(9): 2264 - 2275. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. J. Broekmans, M. R. Soules, and B. C. Fauser Ovarian Aging: Mechanisms and Clinical Consequences Endocr. Rev., August 1, 2009; 30(5): 465 - 493. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Devroey, B.C.J.M. Fauser, K. Diedrich, and on behalf of the Evian Annual Reproduction (EVAR) Approaches to improve the diagnosis and management of infertility Hum. Reprod. Update, July 1, 2009; 15(4): 391 - 408. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Hickey, M. Peate, C.M. Saunders, and M. Friedlander Breast cancer in young women and its impact on reproductive function Hum. Reprod. Update, May 1, 2009; 15(3): 323 - 339. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. M. Nelson, R. W. Yates, H. Lyall, M. Jamieson, I. Traynor, M. Gaudoin, P. Mitchell, P. Ambrose, and R. Fleming Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum. Reprod., April 1, 2009; 24(4): 867 - 875. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Garcia-Velasco and E. Somigliana Management of endometriomas in women requiring IVF: to touch or not to touch Hum. Reprod., March 1, 2009; 24(3): 496 - 501. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M.L. Haadsma, H. Groen, V. Fidler, L.H.M. Seinen, F.J.M. Broekmans, M.J. Heineman, and A. Hoek The predictive value of ovarian reserve tests for miscarriage in a population of subfertile ovulatory women Hum. Reprod., March 1, 2009; 24(3): 546 - 552. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Rosendahl, C. Y. Andersen, E. Ernst, L. G. Westergaard, P. E. Rasmussen, A. Loft, and A. N. Andersen Ovarian function after removal of an entire ovary for cryopreservation of pieces of cortex prior to gonadotoxic treatment: a follow-up study Hum. Reprod., November 1, 2008; 23(11): 2475 - 2483. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M.L. Haadsma, H. Groen, V. Fidler, A. Bukman, E.M.A. Roeloffzen, E.R. Groenewoud, F.J.M. Broekmans, M.J. Heineman, and A. Hoek The predictive value of ovarian reserve tests for spontaneous pregnancy in subfertile ovulatory women Hum. Reprod., August 1, 2008; 23(8): 1800 - 1807. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Yakin and B. Urman What next for preimplantation genetic screening? A clinician's perspective Hum. Reprod., August 1, 2008; 23(8): 1686 - 1690. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Jayaprakasan, J.F. Hopkisson, B.K. Campbell, J. Clewes, I.R. Johnson, and N.J. Raine-Fenning Quantification of the effect of pituitary down-regulation on 3D ultrasound predictors of ovarian response Hum. Reprod., July 1, 2008; 23(7): 1538 - 1544. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. van Disseldorp, M. J. Faddy, A. P. N. Themmen, F. H. de Jong, P. H. M. Peeters, Y. T. van der Schouw, and F. J. M. Broekmans Relationship of Serum Antimullerian Hormone Concentration to Age at Menopause J. Clin. Endocrinol. Metab., June 1, 2008; 93(6): 2129 - 2134. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Gnoth, A.N. Schuring, K. Friol, J. Tigges, P. Mallmann, and E. Godehardt Relevance of anti-Mullerian hormone measurement in a routine IVF program Hum. Reprod., June 1, 2008; 23(6): 1359 - 1365. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M.J. Vercammen, A. Verloes, H. Van de Velde, and P. Haentjens Accuracy of soluble human leukocyte antigen-G for predicting pregnancy among women undergoing infertility treatment: meta-analysis Hum. Reprod. Update, May 1, 2008; 14(3): 209 - 218. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Gremlich, S. Fratta, E. Rebellato, R. Uras, D. Reymondin, F. Damnon, M. Germond, and S. Gerber Interleukin-1 receptor antagonist gene (IL-1RN) polymorphism is a predictive factor of clinical pregnancy after IVF Hum. Reprod., May 1, 2008; 23(5): 1200 - 1206. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. T. Scott and R. A. Reijo Pera The road to pluripotence: the research response to the embryonic stem cell debate Hum. Mol. Genet., April 15, 2008; 17(R1): R3 - R9. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Abir, A. Ben-Haroush, C. Felz, E. Okon, H. Raanani, R. Orvieto, S. Nitke, and B. Fisch Selection of patients before and after anticancer treatment for ovarian cryopreservation Hum. Reprod., April 1, 2008; 23(4): 869 - 877. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T.E.M. Verhagen, D.J. Hendriks, L.F.J.M.M. Bancsi, B.W.J. Mol, and F.J.M. Broekmans The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization: a meta-analysis Hum. Reprod. Update, March 1, 2008; 14(2): 95 - 100. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B.C.J.M. Fauser, K. Diedrich, P. Devroey, and on behalf of the Evian Annual Reproduction (EVAR) Predictors of ovarian response: progress towards individualized treatment in ovulation induction and ovarian stimulation Hum. Reprod. Update, January 1, 2008; 14(1): 1 - 14. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. H Balen and A. J Rutherford Management of infertility BMJ, September 22, 2007; 335(7620): 608 - 611. [Full Text] [PDF]
|
|
|
|
|
|
S. M. Nelson, R. W. Yates, and R. Fleming Serum anti-Mullerian hormone and FSH: prediction of live birth and extremes of response in stimulated cycles implications for individualization of therapy Hum. Reprod., September 1, 2007; 22(9): 2414 - 2421. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Siristatidis and S. Bhattacharya Unexplained infertility: does it really exist? Does it matter? Hum. Reprod., August 1, 2007; 22(8): 2084 - 2087. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Jayaprakasan, N. Hilwah, N.R. Kendall, J.F. Hopkisson, B.K. Campbell, I.R. Johnson, and N.J. Raine-Fenning Does 3D ultrasound offer any advantage in the pretreatment assessment of ovarian reserve and prediction of outcome after assisted reproduction treatment? Hum. Reprod., July 1, 2007; 22(7): 1932 - 1941. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. W. van der Steeg, P. Steures, M. J. C. Eijkemans, J. D. F. Habbema, P. G. A. Hompes, F. J. Broekmans, P. X. J. M. Bouckaert, P. M. M. Bossuyt, F. van der Veen, B. W. J. Mol, et al. Predictive Value and Clinical Impact of Basal Follicle-Stimulating Hormone in Subfertile, Ovulatory Women J. Clin. Endocrinol. Metab., June 1, 2007; 92(6): 2163 - 2168. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. J. Stillman A 47-Year-Old Woman With Fertility Problems Who Desires a Multiple Pregnancy JAMA, February 28, 2007; 297(8): 858 - 867. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||