Antral Follicle Diameter Variance Within Each Ovary May Be A Predictor For Poor Response In Cases With Normal Ovarian Reserve

2018 ◽  
Vol 126 (08) ◽  
pp. 521-527
Author(s):  
Ilhan Sanverdi ◽  
Enis Ozkaya ◽  
Suna Kucur ◽  
Dilsat Bilen ◽  
Meryem Eken ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Poor Response ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Multivariate Regression Analysis ◽  
Antral Follicle Count ◽  
Estradiol Level ◽  
Poor Ovarian Response ◽  
Follicle Diameter ◽  
The Right

Abstract Objectives To determine the predictive value of antral follicle diameter variance within each ovary for ovarian response in cases with normal ovarian reserve tests. Methods This is a prospective observational study. One hundred and thirty nine infertile women who underwent ART in IVF-ICSI unit of Zeynep Kamil women and children’s Health Training and research hospital between January 2017 to June 2017 were recruited. Blood samples were collected on day 2/day 3 for assessment of serum FSH and estradiol. Trans-vaginal sonography was done for antral follicle count. During antral follicle count, in order to determine antral follicle diameter variance, diameters of the largest and smallest follicles were recorded. Variance was calculated by subtracting the smallest diameter from the largest one. Following ovarian stimulation with antagonist protocol, poor response was determined in cases with total oocyte number≤3. Ovarian reserve tests and antral follicle diameter variance were utilized to predict cases with poor response in women with normal ovarian reserve. Results Antral follicle diameter variance both in right (AUC=0.737, P<0.001) and left (AUC=0.651, P<0.05) ovaries significantly predicted poor ovarian response. Variance>3.5 mm was found to have 75% sensitivity to predict poor response. Basal serum FSH with estradiol levels and AFC failed to predict poor response (P>0.05). Other significant predictors for poor response were day 5 estradiol level and estradiol level at trigger day (P<0.05). In multivariate regression analysis, both AFC and antral follicle diameter variance in the right ovary were found to be significantly associated with clinical pregnancy, on the other hand peak estradiol concentration and antral follicle diameter variance in the right ovary were significantly associated with poor response. Conclusion Antral follicle diameter variance may be utilized to predict poor ovarian response in cases with normal ovarian reserve.

scholarly journals Diminished Ovarian Reserve, Causes, Assessment and Management

2013 ◽  
Vol 4 (2) ◽  
pp. 45-55 ◽  
Author(s):  
Mala Arora ◽  
Mandeep Kaur
Keyword(s):  
Ovarian Reserve ◽  
Treatment Options ◽  
Poor Response ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Diminished Ovarian Reserve ◽  
Assisted Hatching ◽  
Ovum Donation ◽  
Antagonist Protocol

ABSTRACT Diminished ovarian reserve predicts diminished ovarian response to stimulation but does not predict cycle fecundity. It has been recently defined by ESHRE, the Bologna's criteria, according to which at least two of the following three features should be present: (1) Age >40 years/any other risk factor for DOR, (2) abnormal ovarian reserve test, i.e. antral follicle count, AMH, (3) poor ovarian response in a previous stimulated cycle, i.e. less than three follicles after standard gonadotropin stimulation. Poor response to maximal stimulation on two previous occasions also defines DOR. The treatment options are limited. Avoiding the GnRH agonist long protocol and stimulation with microdose flare or antagonist protocol yields better results. Adjuvant therapy with LH, DHEAS and growth hormone shows some benefit in improving the oocyte yield. It is advisable to perform ICSI for all obtained oocytes and some advocate assisted hatching. Pregnancy rates are, however, poor and often these patients require ovum donation. Developing tests that will diagnose DOR in a low-risk population will allow women to plan their reproductive careers early. How to cite this article Kaur M, Arora M. Diminished Ovarian Reserve, Causes, Assessment and Management. Int J Infertility Fetal Med 2013;4(2):45-55.

scholarly journals An Ovarian Reserve Assessment Model Based on Anti-Müllerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Huiyu Xu ◽  
Li Shi ◽  
Guoshuang Feng ◽  
Zhen Xiao ◽  
Lixue Chen ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Hormone Level ◽  
Follicle Stimulating Hormone ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Poor Ovarian Response ◽  
Predicted Probability ◽  
Age Model ◽  
Stimulating Hormone

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 (&lt;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.

scholarly journals An Ovarian Reserve Assessment Model Based on Anti-Müllerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study

10.2196/19096 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19096 ◽  
Author(s):  
Huiyu Xu ◽  
Li Shi ◽  
Guoshuang Feng ◽  
Zhen Xiao ◽  
Lixue Chen ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Hormone Level ◽  
Follicle Stimulating Hormone ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Poor Ovarian Response ◽  
Predicted Probability ◽  
Age Model ◽  
Stimulating Hormone

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.

scholarly journals Comparison of Automated Anti-Müllerian Hormone Assays and Antral Follicle Count in Predicting Ovarian Response During Ovarian Stimulation

2019 ◽  
Vol 01 (02) ◽  
pp. 99-105
Author(s):  
Eek Chaw Tan ◽  
Pallavi Chincholkar ◽  
Su Ling Yu ◽  
Serene Liqing Lim ◽  
Rajkumaralal Renuka ◽  
...  
Keyword(s):  
Ovarian Stimulation ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
High Response ◽  
Controlled Ovarian Stimulation ◽  
Poor Ovarian Response ◽  
Vitro Fertilization ◽  
And Performance

Objective: Various parameters had been used to predict ovarian response. Among them, Anti-Müllerian Hormone (AMH) and antral follicle count (AFC) demonstrate the most favourable analytical and performance characteristics. In this pilot study, we aim to determine the cut-off levels of AMH using automated AMH assays and AFC in the prediction of poor and high responders. Study Design: Prospective study of 43 women between 21 to 45 years old scheduled for assisted reproduction. AMH levels on day 3 of menstruation were analysed using two immunoassay kits, namely the Beckman Coulter Access AMH and the Roche Elecsys AMH on the two automated analysers Beckman Coulter DxI 800 and Roche Cobas e602 respectively. AFC was also assessed on day 3 of menstruation prior to in vitro fertilization (IVF). These were compared with the number of oocytes retrieved after controlled ovarian stimulation. Results: AMH (Beckman Coulter Access AMH and Roche Elecsys AMH) highly correlated with AFC and the number of oocytes retrieved after ovarian stimulation. Beckman Coulter Access AMH was the better predictor for poor ovarian response with ROC [Formula: see text] of 0.83. For the prediction of a high response, AFC had a higher ROC [Formula: see text] of 0.95. Through ROC, the AMH cut-off level for poor ovarian response was 2.23 ng/ml with Beckman Coulter Access AMH and 2.02 ng/ml with Roche Elecsys AMH, while the AMH cut-off for a high ovarian response was 5.19 ng/ml with Beckman Coulter Access AMH and 4.60 ng/ml with Roche Elecsys AMH. For AFC, the cut-off for poor ovarian response was 18 and for high response was 34. Conclusion: AMH and AFC are reliable predictors of ovarian response. Establishment of specific levels may improve individualised controlled ovarian stimulation and optimise the oocyte yield. Larger studies are required to establish these findings.

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