This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Background
Previously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Müllerian hormone level–antral follicle count–follicle-stimulating hormone level–age) model.
Objective
This study aims to explore the possibility of establishing a model for predicting ovarian reserves using only 3 factors: AMH level, FSH level, and age. The proposed model is referred to as the AFA (anti-Müllerian hormone level–follicle-stimulating hormone level–age) model.
Methods
Oocytes from ovarian cycles stimulated by gonadotropin-releasing hormone antagonist were collected retrospectively at our reproductive center. Poor ovarian response (<5 oocytes retrieved) was defined as an outcome variable. The AFA model was built using a multivariable logistic regression analysis on data from 2017; data from 2018 were used to validate the performance of AFA model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predicative value were used to evaluate the performance of the model. To rank the ovarian reserves of the whole population, we ranked the subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 clusters, A-D, according to cut-off values consistent with the AAFA model.
Results
The AUCs of the AFA and AAFA models were similar for the same validation set, with values of 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. We further ranked the ovarian reserves according to their predicted probability of poor ovarian response, which was calculated using our AFA model. The actual incidences of poor ovarian response in groups from A-D in the AFA model were 0.037 (95% CI 0.029-0.046), 0.128 (95% CI 0.099-0.165), 0.294 (95% CI 0.250-0.341), and 0.624 (95% CI 0.577-0.669), respectively. The order of ovarian reserve from adequate to poor followed the order from A to D. The clinical pregnancy rate, live-birth rate, and specific differences in groups A-D were similar when predicted using the AFA and AAFA models.
Conclusions
This AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.
The antral follicle count (AFC) is the number of follicles <8 mm in diameter in early gonadotropin-dependent follicular growth. It has been widely accepted that the pool of primordial follicles in the ovary—the ovarian reserve—is related to the number of growing antral follicles. Thus, in theory, the AFC reflects the remaining ovarian follicle pool [1-3]. However, obtaining an accurate AFC demands a time- and resource-consuming ultrasound examination by a skilled transvaginal sonography specialist. The lack of standardization in AFC measurements [4], AFC changes through the menstrual cycle, contraceptive use [5], and the sensitivity and resolution of transvaginal sonography equipment are all confounding factors making the reliable assessment of AFC difficult.
We have previously published a model for estimating ovarian reserves, using 4 predictors: anti-Müllerian hormone (AMH) level, the AFC, follicle-stimulating hormone (FSH) level, and age. This model was named as the AAFA (anti-Müllerian hormone level–antral follicle count–follicle-stimulating hormone level–age) model [6]. With the development of accurate AMH assays [7,8], the level of this hormone might replace the use of AFC in the measurement of ovarian reserve, avoiding the complexity, cost, and interobserver variation in the AFC [9,10]. Here, we aimed to explore the possibility of establishing a model for assessing a true ovarian reserve using the 3 predictors: AMH levels, FSH levels, and age. This model is referred to as the AFA (anti-Müllerian hormone level–follicle-stimulating hormone level–age) model. If the performance of the AFA model without using the AFC is only slightly worse or even similar to the 4-predictor AAFA model, it might be of better clinical significance, especially in physical examination centers or third-party clinical laboratories, which cannot perform AFC measurements by transvaginal sonography.
MethodsSubjects
This was a retrospective observational cohort study using the same dataset as in our previous study [6]. Briefly, data from 2017 to 2018 were selected according to the inclusion and exclusion criteria. In total, we selected 1523 oocytes from ovarian cycles stimulated by a gonadotropin-releasing hormone (GnRH) antagonist 2017 and 3273 oocytes, from 2018. The first and second stimulation cycles were included as described by Xu et al [6], and there were no strict restrictions on the women’s age or body mass index. Diseases potentially related to defects in follicular development were excluded, including ovarian cysts, previous ovarian surgery, polycystic ovarian syndrome, previous metabolic or endocrinological diseases, previous tuberculosis, chromosomal abnormalities, and women with pregnancies within the previous 3 months. The need for informed consent by the patients was waived, and institutional review board approval was not needed for the de-identified data in this retrospective analysis, as per the Declaration of Helsinki [11].
Sampling and Endocrine Assays
Venous blood samples were drawn, and the sample tubes were immediately inverted 5 times to facilitate thorough blood clotting. Serum was collected by centrifugation and used for endocrine assessment. The circulating FSH level was measured on menstrual cycle day 2, and the circulating AMH level was measured on any day of the menstrual cycle. Serum FSH measurements were performed using a Siemens Immulite 2000 immunoassay system (Siemens Healthcare Diagnostics). The quality controls used for the FSH assay were Lypocheck Immunoassay Plus Control, Trilevel, catalog number 370, lot number 40340 (Bio-Rad Laboratories). Serum AMH concentrations were measured by an ultrasensitive 2-site enzyme-linked immunosorbent assay (Ansh Laboratories), using quality controls supplied within the kits. The coefficients of variation for each assay were indicated previously [6].
Statistical Analysis
In this study, poor ovarian response with <5 oocytes retrieved was defined as an outcome variable. The predictor variables were age and basal serum FSH and AMH concentrations. A multivariable logistic regression analysis was performed to construct a predictive model for poor ovarian response to stimulation using 2017 data; the data from 2018 were used to validate the performance of that model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predicative value (NPV) were used to evaluate the predictive models. The main effect of each predicting variable measures the variation over the distribution of xj in the mean poor ovarian response. Venn diagrams were used to compare the differences between the AAFA and AFA models.
To rank the ovarian reserve of the whole population, we ranked subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 groups A-D, according to cut-off values consistent with our established AAFA model [6]. Analyses were conducted using SAS JMP Pro (version 14.2; SAS Institute), and P<.05 was considered statistically significant.
Results
We previously established an AAFA model, using the 4 predictors of AMH, AFC, FSH, and age [6]. We used this to classify the study population into 4 subgroups: A, B, C and D.
However, the lack of standardization in AFC measurements [4] makes the reliable assessment of AFC very difficult. Moreover, the AFCs were well correlated with AMH, FSH, or age [12,13], implying collinearity. Therefore, we sought to explore the possibility of establishing a model for assessment of the true ovarian reserve using only 3 predictors—AMH, FSH, and age (ie, the AFA model)—instead of the previous 4-predictor AAFA model [6].
Multivariable Logistic Regression to Build a Predictive Model for Poor Ovarian Response Using the 2017 Data
Basic characteristics of the treatment cycles are shown in Table 1.
As in our previous study, we transformed the 3 continuous variables of age, AMH, and FSH into categorical variables. The data used here were exactly the same as those from 2017, when we built our AAFA model [6]. The cut-off values of each predictor in both AFA and AAFA models are listed in Table 2.
Basic characteristics of treatment cycles.
Characteristics
2017 (n=1523)
2018 (n=3273)
Age (years), Mean (SD)
33.4 (5.3)
32.7 (4.8)
Basal FSHa (IU/L), Mean (SD)
7.5 (3.3)
7.2 (3.1)
AMHb (ng/mL), Median (IQR)
2.2 (1.1-4.0)
2.7 (1.2-4.8)
aFSH: follicle-stimulating hormone.
bAMH: anti-Müllerian hormone.
Comparison of grouping criteria of the AFA and AAFA models.
The transformed categorical variables were then analyzed using multivariable logistic regression. The main effects that each independent variable exerted in this model were AMH (85.2%), followed by FSH (6.8%), and age (2.8%). Thus, we have named this model as AFA based on the order of the main effects of each predictor. The odds ratios of each predictor are indicated in Table 3.
The odd ratios of each predictor in the AFA model.
Predictors
OR (95% CI)
P value
Intercept
1.753 (1.177-2.611)
<.001
Categorical age (1 vs 0)
2.239 (1.344-3.731)
.006
Categorical age (2 vs 0)
1.863 (1.270-2.734)
.002
Categorical FSHa (1 vs 0)
2.300 (1.438-3.681)
.002
Categorical FSH (2 vs 0)
3.594 (2.333-5.538)
.001
Categorical FSH (3 vs 0)
24.641 (14.997-40.488)
<.001
Categorical AMHb (0 vs 4)
11.431 (7.281-17.945)
<.001
Categorical AMH (1 vs 4)
5.010 (3.167-7.928)
<.001
Categorical AMH (2 vs 4)
2.211 (1.259-3.882)
<.001
Categorical AMH (3 vs 4)
1.753 (1.177-2.611)
.006
aFSH: follicle-stimulating hormone.
bAMH: anti-Müllerian hormone.
Comparing the Performances of the AFA and AAFA Models
To further evaluate the performance of this AFA model, we calculated the AUC, sensitivity, specificity, PPV, and NPV in the training set (2017 data) and the validation set (2018 data) as indicated in Table 4.
A calibration plot was drawn to evaluate the calibration performance of the AFA model in the training set and validation set (Multimedia Appendix 1). The performance of the AAFA model in the validation set (2018 data) is indicated in Table 4. A comparison shows that the AUCs of the AFA and AAFA models for the same validation set are similar at 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. The specificity, sensitivity, PPV, and NPV are also indicated in Table 4. The AUC between AFA model and AAFA model was tested with DeLong test. The difference of the 2 models in AUC level is 0.009 (95% CI –0.004 to 0.022), indicating no significant difference.
The numbers of overlapping and nonoverlapping cases in the predicted positive (poor ovarian response) and negative estimated by the 2 models are shown in Figure 1.
There were 328 positive (poor responders) and 2732 negative (nonpoor responders) cases overlapping in the 2 models. The 2 models had 93.5% (3060/3273) overlapping positive and negative cases in the 2018 validation set.
Comparison of the performances of the AFA and AAFA models in the 2018 validation data.
Ranking the Ovarian Reserve Based on the Predicted Probability of a Poor Ovarian Response
We previously ranked the ovarian reserve of the whole population according to the predicted probability of a poor ovarian response [6], given that the number of oocytes retrieved is closely related to the number of primordial follicles in the ovarian cortex [14-16]. In this study, we used the same method to rank the ovarian reserve according to the predicted probability of a poor ovarian response calculated using the AFA model. The 60 groups were further divided into 4 subgroups: A, B, C, and D (Multimedia Appendix 2).
The order of ovarian reserve from adequate to poor followed the order of predicted probability of a poor ovarian response from low to high. Women with a predicted probability of more than 50% were classified into the population with diminished ovarian reserve (namely, group D that includes subgroups 43-60), as shown in Multimedia Appendix 2. The actual incidences of poor ovarian response, clinical pregnancy rate per starting cycle, clinical pregnancy rate per embryo transfer cycle, live-birth rate per starting cycle, and live-birth rate per embryo transfer cycle (with 95% CIs) are also indicated in Table 5.
The clinical pregnancy rate and live-birth rate in the 4 ovarian reserve groups.
Ovarian reserve group and model
Actual incidence of poor ovarian response
(95% CI)
Comparing Specific Differences Between the AFA and AAFA Models in Groups A-D
Figure 2 displays the specific differences between the AFA and AAFA models in classifying the whole population into groups A-D.
The horizontal axis includes the 3273 cases in the 2018 validation data. The 2 models did not show a 3-level difference; that is, there was no case classified as A (good ovarian reserve) by the AAFA model but as D (diminished ovarian reserve) by the AFA model. In addition, most cases were classified into the same groups by both models. However, there were differences for some cases. We focus on 3 groups having 2-level differences defined by the AFA or AAFA models, as shown by the red, green, and purple arrows in Figure 2. The same colors are used to indicate those 3 groups in Figure 3.
Specific differences between the AAFA and AFA models in A-D groups.
Specific differences between the AAFA and AFA models in A-D groups (tabular form).
The raw data and the corresponding predicted probability of a poor ovarian response in the 2 models are listed in Multimedia Appendix 3. The actual incidences of poor ovarian response in the 3 subgroups were 4/45 (red), 5/11 (green), and 1/16 (brown). These results suggest that for the red subgroup, the AAFA classification might be closer to the actual incidence of poor ovarian response (4/45). Thus, these cases should have been placed in group A, rather than in the group C. However, for the purple subgroup with a poor ovarian response incidence of 1/16, the group A classified by AFA model might be more suitable. For the green subgroup with a poor ovarian response incidence of 5/11, not group B by the AAFA model or group D by the AFA model, but group C is more appropriate with its predicted probability of 30% to 50%. For groups having 1-level differences, specific cohorts are shown in Figure 2, Figure 3, and Multimedia Appendix 3.
Discussion
We previously established our AAFA model to assess ovarian reserve based on AMH, AFC, FSH, and age [6]. However, standardization of the AFC has long been difficult for fertility clinics worldwide. In this study, using the same 2018 validation data without the AFC predictor, the AFA model showed similar performance as that of the AAFA model, with an AUC of 0.853 (95% CI 0.841-0.865) vs 0.850 (95% CI 0.838-0.862) for the AAFA model. Since it does not require the AFC, the applicability and cost-effectiveness of the AFA model is better than the AAFA model. Thus, a large number of first- and second-tier hospitals, physical examination centers, or third-party clinical laboratories, which cannot conduct AFC tests, can now assess ovarian reserve using our AFA model.
There were no large (3-level) differences, in that no subject was classified into the A group by the AAFA model and the D group by the AFA model (Figure 2 and Multimedia Appendix 3). There were at most 2 levels of difference, as shown in Figure 2, indicated in red, green, and purple. After referring to the actual rate of poor ovarian response in these groups, we came to the conclusion that the 2 models have their own benefits and can complement each other in assessing ovarian reserve. Integration of these 2 models might give infertility clinics more individualized recommendations before starting controlled ovarian stimulation.
The global infertility rate is increasing, affecting about 1 in 7 couples [17]. A large proportion of women worldwide choose to delay having their first child for pursuit of opportunities to improve their education and workforce participation. It has long been acknowledged that fertility (the ability to establish a clinical pregnancy) decreases with increasing female age. Thus, the prevalence of infertility is increasing worldwide due to the postponement of childbearing. However, many women of reproductive age are not aware of the existing large heterogeneity in ovarian reserve for the same age [18]. In response to the increasing of infertility rate, to achieve a successful pregnancy, an increasing number of couples seek for assisted reproductive treatment. However, not all couples will benefit from it, as the beneficial effect of assisted reproductive treatment is limited in women with diminished ovarian reserve or in women with premenopause [19,20]. If women with potential diminished ovarian reserve could evaluate their ovarian reserve status earlier, it might be possible to avoid the subsequent infertility problem. Our new AFA model provides better means for assessing ovarian reserve, so that women of childbearing age, especially those who hesitate to start a family, might be able to evaluate their ovarian reserve in time.
The circulating AMH concentration is well-correlated with the AFC, and it is considered to be the best predictor for an ovarian response [3,14,21,22]. However, it should be noted that AMH concentrations and AFC are not necessarily linked. The term “ovarian reserve” refers to the number of primordial follicles remaining in the ovarian cortex. AMH is secreted by immature granulosa cells in the gonadotropin-independent phase of follicular development, while the AFC reflects the later gonadotropin-dependent phase. For example, in patients with hypogonadotropic hypogonadism, AFC is undetectable because of the extremely low level of FSH, but such young patients can have a sufficient ovarian reserve, manifested by normal AMH levels and good pregnancy outcomes when undergoing assisted reproductive technology. In addition, some patients exhibit a diminished ovarian reserve and low AMH concentrations but have a satisfactory AFC. AMH gene knockout mice might help us to understand the underlying mechanisms in such patients. In these mice, diminished ovarian reserve induced by the absence of AMH leads to accelerated follicular activation and an increase in the AFC in 4-month-old AMH-null mice (young adult) [23]. Therefore, it is possible that the AMH concentration is a more accurate measure of the actual ovarian reserve than the AFC. Furthermore, the main effect of AMH level was 62.0% in our AAFA model, and 85.2% in the AFA model, meaning that this hormone is the best predictor of ovarian reserve among the existing indicators.
The relationship between AMH concentration and pregnancy outcomes has been investigated extensively [14,24-27]. Fertility is defined as the natural capability to establish a clinical pregnancy [28]. The most accepted predictor for fertility is the ovarian reserve. Within a certain range, the number of primordial follicles does not correlate well with fertility [6], but when the number falls below a certain threshold, as in the case of diminished ovarian reserve defined by our AAFA [6] or AFA models (Table 5), female fertility declines significantly. This might explain the relatively weak relationship between fertility and ovarian reserve. There is a large variation in the number of granulosa cells needed to maintain at least 1 healthy oocyte; however, if there are too few granulosa cells to support at least 1 healthy oocyte, pregnancy is not possible.
There were some limitations to our study. First, it had a retrospective and nonrandomized design. However, as one of the largest reproductive centers in China, there is no strict limit on our selection of patients, thus helping avoid selection bias among our study population. Therefore, our AFA model is relevant to daily clinical practice. Second, our AFA model divides the population into 60 subgroups (3×4×5) rather than the 16 subgroups in the AAFA model. Thus, the sample sizes in our groups were relatively small, such as the 20th group (Multimedia Appendix 2) with only 1 case. We aim to include more samples in the future to verify and improve our formula used in the AFA model. Our last concern is that the positive rate predicted by the validation set (2018 data) is lower than the training set (2017 data), which may be induced by the lower rate of actual poor responders in 2018 data (315/1523 in 2017 data vs 499/3273 in 2018 data). Although the predicted positive rate of the validation set is low, considering the similarity of the AUC of the training set and the validation set, and the main purpose of our research, which is to classify the whole population into more groups according to the predicted probability of poor ovarian response, we believe that the AFA model is satisfactory and comparable to AAFA model. For subsequent related software, we will also integrate the AFA model, the AAFA model, and the actual rate of poor ovarian response in each subgroup together to further optimize the algorithm of this ovarian reserve assessment–related software.
A calibration plot of the AFA model in the training set and validation set.
Ranking the ovarian reserve into 4 subgroups of A-D based on predicted probability of poor ovarian response in validation data.
Raw data and corresponding predicted groups and subgroups classified by the AFA or AAFA models.
We thank James Cummins, PhD, from Liwen Bianji, Edanz Group China, for editing the English text of a draft of this manuscript. This study was supported by the National Key Research and Development Program of China (Grant No. 2016YFC1000201, 2018YFC1002104, 2018YFC1002106, 2016YFC1000302); National Natural Science Foundation of China (Grant No. 81300373, 81771650); the Capital Health Research and Development of Special Project (Grant No. 2018-1-4091); Program for the Innovative Research Team of Yunnan, China (Grant No. 2017HC009); and Major National R & D Projects of China (Grant No. 2017ZX09304012-012).
None declared.
de CarvalhoBRRosa e SilvaACJDSRosa e SilvaJCdos ReisRMFerrianiRASilva de SáMarcos FelipeOvarian reserve evaluation: state of the artJ Assist Reprod Genet2008072573112210.1007/s10815-008-9241-218679790PMC2596675BroerSLMolBWJHendriksDBroekmansFJThe role of antimullerian hormone in prediction of outcome after IVF: comparison with the antral follicle countFertil Steril2009039137051410.1016/j.fertnstert.2007.12.01318321493S0015-0282(07)04195-7BroerSLDóllemanMOpmeerBCFauserBCMolBWBroekmansFJMAMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation: a meta-analysisHum Reprod Update2011171465410.1093/humupd/dmq03420667894dmq034BroekmansFJde ZieglerDHowlesCMGougeonATrewGOlivennesFThe antral follicle count: practical recommendations for better standardizationFertil Steril20100894310445110.1016/j.fertnstert.2009.04.04019589513S0015-0282(09)01008-5SomunkiranAYavuzTYucelOOzdemirIAnti-Müllerian hormone levels during hormonal contraception in women with polycystic ovary syndromeEur J Obstet Gynecol Reprod Biol200710134219620110.1016/j.ejogrb.2007.01.01217335955S0301-2115(07)00070-XXuHFengGWangHHanYYangRSongYChenLShiLZhangMQLiRQiaoJA novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort studyJ Assist Reprod Genet20200437496397210.1007/s10815-020-01700-13231890510.1007/s10815-020-01700-1PMC7183040LiHWongBIpWYeungWHoPNgEComparative evaluation of three new commercial immunoassays for anti-Müllerian hormone measurementHum Reprod20161231122796280210.1093/humrep/dew24827702835dew248FergusonJMPépinDavidDuruCMatejtschukPDonahoePKBurnsCJTowards international standardization of immunoassays for Müllerian inhibiting substance/anti-Müllerian hormoneReprod Biomed Online20181137563164010.1016/j.rbmo.2018.08.01230241771S1472-6483(18)30406-1PMC6302068IndranIRHuangZKhinLWChanJKViardot-FoucaultVYongELSimplified 4-item criteria for polycystic ovary syndrome: A bridge too far?Clin Endocrinol (Oxf)20180889220221110.1111/cen.1375529851127EilertsenTBVankyECarlsenSMAnti-Mullerian hormone in the diagnosis of polycystic ovary syndrome: can morphologic description be replaced?Hum Reprod201208278249450210.1093/humrep/des21322693172des213World Medical AssociationWorld Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjectsJAMA20131127310202191410.1001/jama.2013.281053241417141760318NyströmAnnaMörseHelenaNordlöfHannaWiebeKArtmanMØraIngridGiwercmanAHenicEElfvingMAnti-müllerian hormone compared with other ovarian markers after childhood cancer treatmentActa Oncol20190258221822410.1080/0284186X.2018.152942330558460BakerVLGraciaCGlassnerMJSchnellVLDoodyKCoddingtonCCShinSSMarshallLAAlperMMMoralesAJPavoneMEBeheraMAZbellaEAShapiroBSStraseskiJABroylesDLMulticenter evaluation of the Access AMH antimüllerian hormone assay for the prediction of antral follicle count and poor ovarian response to controlled ovarian stimulationFertil Steril2018081103506513.e310.1016/j.fertnstert.2018.03.03129960708S0015-0282(18)30286-3XuHZengLYangRFengYLiRQiaoJRetrospective cohort study: AMH is the best ovarian reserve markers in predicting ovarian response but has unfavorable value in predicting clinical pregnancy in GnRH antagonist protocolArch Gynecol Obstet201703295376377010.1007/s00404-016-4274-82801207710.1007/s00404-016-4274-8BroerSvan DisseldorpJeroenBroezeKDollemanMadeleineOpmeerBrent CBossuytPatrickEijkemansMarinus J CMolBen-Willem JBroekmansFrank J MIMPORT study groupAdded value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approachHum Reprod Update2013191263610.1093/humupd/dms04123188168dms041QiaoJWangZFengHMiaoYi-LiangWangQiangYuYangWeiYan-ChangYanJieWangWei-HuaShenWeiSunShao-ChenSchattenHeideSunQing-YuanThe root of reduced fertility in aged women and possible therapentic options: current status and future perspectsMol Aspects Med20140838548510.1016/j.mam.2013.06.00123796757S0098-2997(13)00041-1TalmorADunphyBFemale obesity and infertilityBest Pract Res Clin Obstet Gynaecol20150529449850610.1016/j.bpobgyn.2014.10.01425619586S1521-6934(14)00241-7DaanNMFauserBCMenopause prediction and potential implicationsMaturitas2015118232576510.1016/j.maturitas.2015.07.01926278873S0378-5122(15)30026-8HoorensSGalloFCaveJGrantJCan assisted reproductive technologies help to offset population ageing? An assessment of the demographic and economic impact of ART in Denmark and UKHum Reprod2007092292471510.1093/humrep/dem18117586831dem181HabbemaJDFEijkemansMJNargundGBeetsGLeridonHTe VeldeEgbert RThe effect of in vitro fertilization on birth rates in western countriesHum Reprod2009062461414910.1093/humrep/dep00419233869dep004BuyukESeiferDBYoungerJGraziRVLiemanHRandom anti-Müllerian hormone (AMH) is a predictor of ovarian response in women with elevated baseline early follicular follicle-stimulating hormone levelsFertil Steril20110695723697210.1016/j.fertnstert.2011.03.07121497340S0015-0282(11)00501-2NardoLGGelbayaTAWilkinsonHRobertsSAYatesAPembertonPLaingICirculating basal anti-Müllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilizationFertil Steril20091192515869310.1016/j.fertnstert.2008.08.12718930213S0015-0282(08)03837-5DurlingerA LKramerPKarelsBde JongF HUilenbroekJ TGrootegoedJ AThemmenA PControl of primordial follicle recruitment by anti-Müllerian hormone in the mouse ovaryEndocrinology1999121401257899610.1210/endo.140.12.720410579345MerhiZZapantisABergerDSJindalSKDetermining an anti-Mullerian hormone cutoff level to predict clinical pregnancy following in vitro fertilization in women with severely diminished ovarian reserveJ Assist Reprod Genet20131030101361510.1007/s10815-013-0077-z23963620PMC3824854LukaszukKLissJKunickiMJakielGWasniewskiTWoclawek-PotockaIPastuszekEAnti-Müllerian hormone (AMH) is a strong predictor of live birth in women undergoing assisted reproductive technologyReprod Biol2014091431768110.1016/j.repbio.2014.03.00425152514S1642-431X(14)00041-2TalRSeiferDBWantmanEBakerVTalOAntimüllerian hormone as a predictor of live birth following assisted reproduction: an analysis of 85,062 fresh and thawed cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System database for 2012-2013Fertil Steril201802109225826510.1016/j.fertnstert.2017.10.02129331235S0015-0282(17)31991-XStyerAKGaskinsAJBradyPCSlussPMChavarroJEHauserRBTothTLDynamic antimüllerian hormone levels during controlled ovarian hyperstimulation predict in vitro fertilization response and pregnancy outcomesFertil Steril2015111045115361.e110.1016/j.fertnstert.2015.07.116126315051S0015-0282(15)01673-8Zegers-HochschildFAdamsonGDDyerSRacowskyCde MouzonJSokolRRienziLSundeASchmidtLCookeIDSimpsonJLvan der PoelSThe International Glossary on Infertility and Fertility Care, 2017Fertil Steril201709108339340610.1016/j.fertnstert.2017.06.00528760517S0015-0282(17)30429-6