scholarly journals Effects of Pressing Needles at Four Umbilical Points and Bushen Huoxue Tiaozhou Recipe Combined with Western Medicine on Follicle Stimulating Hormone, Estradiol, Anti-Mullerian Hormone, Antral Follicle Count and Ovarian Artery Blood Flow Parameters in Patients with Decreased Ovarian Reserve

2021 ◽  
Vol 83 (s5) ◽  
Author(s):  
Xiaoqing Sang ◽  
Yueying Huang ◽  
Yinli Ye ◽  
Lu Zheng ◽  
Jingjing Xue
Keyword(s):  
Blood Flow ◽  
Ovarian Reserve ◽  
Western Medicine ◽  
Follicle Stimulating Hormone ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Artery Blood Flow ◽  
Ovarian Artery ◽  
Flow Parameters ◽  
Umbilical Points

scholarly journals Correlation Between Follicle Stimulating Hormone, Anti-Müllerian Hormone and Antral Follicle Count with Different Age Groups in Infertile Women

2021 ◽  
Vol 20 (1) ◽  
pp. 22-27
Author(s):  
Juthi Bhowmik ◽  
Parveen Fatima ◽  
Jesmine Banu ◽  
Farzana Deeba ◽  
Sheuli Chowdhury ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Ovarian Reserve ◽  
Follicle Stimulating Hormone ◽  
Age Groups ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Age Group ◽  
Infertile Women ◽  
Group A ◽  
Study Population

Background: Reduced ovarian reserve predicts poor ovarian response and poor suc-- cess rates in infertile women who undergo Assisted Reproductive Technology (ART). Ovarian reserve decreases with age but the rate of decline varies from one woman to another. Follicle Stimulating Hormone (FSH) Anti-Müllerian Hormone (AMH) and antral follicle count (AFC) represent the three most frequently utilized laboratory tests in determining Ovarian Reserve (OR). To determine correlation between FSH, AMH and AFC in infertile female. Materials and methods: It was an observational (Cross sectional) study. This study was done in the Department of Reproductive Endocrinology and Infertility, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, between July 2018 to June 2019. The study population consisted of all the diagnosed female infertility patients of reproductive age. The women attending the study center during study period having primary or secondary infertility was considered as study population. They were divided in 4 age groups 21-25, 26-30, 31-35 yrs and 36-40 yrs years. Data was collected using a structured questionnaire following physical & lab examination. For D2 FSH level fasting blood was collected on D2/3 of menstrual cycle, serum FSH level was measured by ADVIA Centraur(R) XP immunoassay system. For S. AMH level blood sample was collected on 2nd day of cycle and measured by BECKMAN COULTER machine using Chemiluminescent Immunoassay method. For AFC count TVS was done on D2-5 of cycle using KONTRON medical USG machine. Collected data were classified, edited, coded and entered into the computer for statistical analysis by using SPSS version 23. Results: Out of 74 patients the mean age was found 32.6±5.5 years. Serum FSH, AMH and AFC were significantly associated with different age group. A negative correlation was found between serum FSH and serum AMH in all age group. But strong correlation found in age group 31-35 yrs and in 36-40 years age group. A negative correlation was found between serum FSH and total AFC in age group 26- 30 years, 31-35 years and 36-40 years respectively. A positive correlation was found between serum AMH and total AFC in all age group but most strong in age group 31-35 years. In multivariate logistic regression analysis serum AMH (<1.0 ng/ml) and total AFC (<5 number) were found to be significantly associated with age group >35 years patients. Conclusion: In all age grqoup, FSH, AMH and AFC correlates but it is more pronounced in advanced age that means >35 years age group. Chatt Maa Shi Hosp Med Coll J; Vol.20 (1); January 2021; Page 22-27

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 Antral Follicles Count and Anti-Müllerian Hormone Levels after Gonadotoxic Chemotherapy in Patients with Breast Cancer: Cohort Study

2017 ◽  
Vol 39 (04) ◽  
pp. 162-168 ◽  
Author(s):  
Ângela D'Avila ◽  
Edison Capp ◽  
Helena Corleta
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Ovarian Reserve ◽  
Follicle Stimulating Hormone ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Antral Follicles ◽  
Breast Cancer Cohort ◽  
Estradiol Levels ◽  
Stimulating Hormone

Aim To assess ovarian reserve (OVR) by means of follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH), and antral follicle count (AFC) measurement in eumenorrheic women with breast cancer, exposed to gonadotoxic chemotherapy. Method Fifty-two women (35.3 ± 3.8 years old) with breast cancer and undergoing cyclophosphamide-containing chemotherapy were enrolled. The assessment was performed before chemotherapy (T1) and after 2 (T2) and 6 months (T3). Results Six months after chemotherapy, the prevalence of regular cycles was 60%. Anti-Müllerian hormone decreased down to undetectable levels at T2 and T3 (T1: 2.53 [1.00–5.31]; T2 < 0.08; T3: < 0.08 [< 0.08–1.07] ng/mL), (p < 0.0001). Antral follicle count was 11 [8.0–13.5] follicles at T1 and lower at T2 (5.50 [3.75–8.0] and T3 (5.0 [2.5–7.0]) (p < 0.0001). In patients who remained with regular cycles during chemotherapy or resumed normal menses, FSH and estradiol levels remained unchanged. Conclusion Anti-Müllerian hormone and AFC are useful as markers of OVR decline in women exposed to chemotherapy. Follicle-stimulating hormone is only adequate in women who become amenorrheic.

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