scholarly journals 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

2004 ◽  
Vol 21 (2) ◽  
pp. 37-45 ◽  
Author(s):  
Mehmet Erdem ◽  
Ahmet Erdem ◽  
Rifat Gursoy ◽  
Kutay Biberoglu
Keyword(s):  
Ovarian Reserve ◽  
Clomiphene Citrate ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Inhibin B ◽  
Poor Ovarian Response ◽  
Ovarian Volume ◽  
Antral Follicle Counts

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 Ovarian reserve in patients who have undergone endometriosis surgery

2018 ◽  
Vol 75 (7) ◽  
pp. 644-650
Author(s):  
Olivera Dzatic-Smiljkovic ◽  
Mladenko Vasiljevic ◽  
Ivana Rudic ◽  
Jelena Vugdelic ◽  
Aleksandar Ristic ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Ovarian Function ◽  
Uterine Cavity ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Inhibin B ◽  
Ca 125 ◽  
Control Group ◽  
Study Group ◽  
Ovarian Volume

Background/Aim. Endometriosis is a gynaecological disorder characterized by the presence of endometrial tissue outside the uterine cavity. The aim of this paper was to determine the effect of laparoscopic cystotomy and cystectomy on ovarian function, as well as to compare these two methods in terms of qualitative and quantitative damage to the ovaries, achieved pregnancies and recurrence. Methods. The prospective study, conducted in ?Narodni Front? Obstetrics and Gynaecology Clinic in Belgrade at the Endoscopic Infertility Treatment Ward, included a total of 150 patients. The study group was represented by 100 patients who underwent a surgical treatment of endometrial ovarian cysts. The patients in the study group were divided into 2 subgroups: Subgroup I consisted of 50 patients who underwent a laparoscopic cystotomy (incision of the cyst and thermal coagulation) and subgroup II which included 50 women who underwent a laparoscopic cystectomy. The control group consised of patients who underwent a surgery due to tubal factor infertility. The following parameters of the ovarian function were tested: the ovarian volume, the antral follicle count, the presence of the preovulatory follicle on the operated ovary, the serum levels of anti- M?llerian hormone (AMH), follicle-stimulating hormone (FSH), ovarian tumor marker (Ca 125), inhibin B, as well as the rate of achieved pregnancies one year after the surgery. Results. The ovarian volume and the antral follicle count as well as the FSH values were significantly higher in the control group in comparison with the patients in the study group. There were no significant differences in the ovarian volume, the antral follicle count, the AMH values and inhibin B values in the study group between the patients with cystectomy and those with the incision and coagulation of the cyst. Conclusion. Both surgical techniques diminished the ovarian reserve: cystectomy was more aggressive method in terms of the damage inflicted on the ovarian tissue, and incision with coagulation carried a higher risk of recurrence.

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 Ovarian reserve in women with obesity

2019 ◽  
Vol 16 (3) ◽  
pp. 69-75
Author(s):  
Olga R. Grigoryan ◽  
Robert K. Mikheev ◽  
Elena N. Andreeva ◽  
Ivan I. Dedov
Keyword(s):  
Ovarian Reserve ◽  
Antral Follicle ◽  
Reproductive Age ◽  
Reference Ranges ◽  
Obese Patients ◽  
Class Iii ◽  
Multicenter Studies ◽  
Ovarian Volume ◽  
Obesity Class ◽  
Significant Difference

BACKGROUND: One of the consequences of obesity for the female body is a decrease in fertility. It is shown that impaired reproductive function in obese patients can be associated, in particular, with a decrease in ovarian reserve. AIMS: To evaluate the ovarian reserve function in female patients of reproductive age with different classes of obesity in comparison with women without obesity. MATERIALS AND METHODS: This study evaluated 320 caucasian women, age 20-30 years, without obesity (BMI30, n=80) and with obesity WHO class I-III (n=80 per class). Anthropometrics, serum concentrations of anti-Mullerian hormone (AMH), inhibin B, follicle stimulating hormone (FSH), luteinizing hormone (LH), estradiol, progesterone, and testosterone were compared on the 2-3 day of menstrual cycle as ovarian volume and antral follicle count (AFC). RESULTS: We reveal statistically significant difference in following parameters in normal BMI women in comparison with obesity women: AMH, testosterone, ovarian volume and AFC. Moreover, we reveal significant difference between patients with different WHO class of obesity. But even in class III obesity parameters remained within reference ranges. CONCLUSIONS: Ovarian reserve function parameters progressively decrease with increase of obesity class in subjects, but ovarian reserve parameters were in normal reference range even in class III obese patients. Further large randomized multicenter studies are required to find influence of obesity in relation to ethnicity and other factors to ovarian reserve function.

scholarly journals Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers

2021 ◽  
Vol 12 ◽  
Author(s):  
Haixia Song ◽  
Qin Qin ◽  
Caixia Yuan ◽  
Hong Li ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Ovarian Reserve ◽  
Ovarian Response ◽  
Support Vector ◽  
Control Group ◽  
Signal Pathways ◽  
Poor Ovarian Response ◽  
Metabolism Pathway ◽  
Metabolomic Profiling ◽  
Vector Machines

ObjectiveTo characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).Patient(s)Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-five women with POR (19-35 years, AFC &lt;5, AMH &lt; 1.2 ng/ml) as the study group were collected in our study. The serum levels of the women in both groups were determined from their whole blood by untargeted liquid chromatography–mass spectrometry (LC-MS). Multivariate statistical analysis and cell signal pathways analysis were used to reveal the results.ResultsA total of 538 different metabolites were finally identified in the two groups. Tetracosanoic acid, 2-arachidonoylglycerol, lidocaine, cortexolone, prostaglandin H2,1-naphthylamine, 5-hydroxymethyl-2-furancarboxaldehyde, 2,4-dinitrophenol, and D-erythrulose1-phosphate in POR were significantly different from control as were most important metabolites in support vector machines (p &lt;0.05). Metabolomic profiling, together with support vector machines and pathway analysis found that the nicotinate and nicotinamide metabolism pathway, including L-aspartic acid, 6-hydroxynicotinate, maleic acid, and succinic acid semialdehyde, was identified to have significant differences in POR women compared to control women, which may be associated with ovarian reserve.ConclusionThis study indicated that LC–MS-based untargeted metabolomics analysis of serum provided biological markers for women with POR. The nicotinate and nicotinamide metabolism pathway may offer new insight into the complementary prediction and therapeutic potential of POR. The functional associations of these metabolites need further investigation.

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