Dataset Information

A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study.

ABSTRACT:

Purpose

To establish a mathematical model for assessing the true ovarian reserve based on the predicted probability of poor ovarian response (POR).

Methods

In this retrospective cohort study, a total of 1523 GnRH-antagonist cycles in 2017 were firstly analyzed. The ovarian responses were calculated based on the number of retrieved oocytes. The continuous variables were converted into categorical variables according to cutoff values generated by the decision tree method. The optimal model was identified using forward stepwise multiple logistic regression with 5-fold cross-validation and further verified its performances using outer validation data.

Results

The predictors in our model were anti-Müllerian hormone (AMH), antral follicle counts (AFC), basal follicle-stimulating hormone (FSH), and age, in order of their significance, named AAFA model. The AUC, sensitivity, specificity, positive predictive value, and negative predictive value of AAFA model in inner validation and outer validation data were 0.861 and 0.850, 0.603 and 0.519, 0.917 and 0.930, 0.655 and 0.570, and 0.899 and 0.915. Ovarian reserve of 16 subgroups was further ranked according to the predicted probability of POR and further divided into 4 groups of A-D using clustering analysis. The incidence of POR in the four groups was 0.038 (0.030-0.046), 0.139 (0.101-0.177), 0.362 (0.308-0.415), and 0.571 (0.525-0.616), respectively. The order of ovarian reserve from adequate to poor followed the order of A to D.

Conclusion

We have established an easy applicable AAFA model for assessing true ovarian reserve and may have important implications in both infertile women and general reproductive women in Chinese or Asian population.

SUBMITTER: Xu H

PROVIDER: S-EPMC7183040 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study.

Xu Huiyu H Feng Guoshuang G Wang Haiyan H Han Yong Y Yang Rui R Song Ying Y Chen Lixue L Shi Li L Zhang Meng Qian MQ Li Rong R Qiao Jie J

Journal of assisted reproduction and genetics 20200421 4

<h4>Purpose</h4>To establish a mathematical model for assessing the true ovarian reserve based on the predicted probability of poor ovarian response (POR).<h4>Methods</h4>In this retrospective cohort study, a total of 1523 GnRH-antagonist cycles in 2017 were firstly analyzed. The ovarian responses were calculated based on the number of retrieved oocytes. The continuous variables were converted into categorical variables according to cutoff values generated by the decision tree method. The optima ...[more]

PMID: 32318905

Similar Datasets

Project description:BackgroundPreviously, 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.ObjectiveThis 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.MethodsOocytes 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.ResultsThe 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.ConclusionsThis AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.

Project description:PurposeTo explore the impact of inactivated COVID-19 vaccination on ovarian reserve as assessed by serum anti-Müllerian hormone (AMH) concentration.MethodsA total of 3160 women were included in this single-center retrospective cohort study between June 2021 and October 2022. Vaccination information were collected from official immunization records available in personal mobile apps. Serum AMH was qualified by electrochemiluminescence immunoassay and compared with previous measurement data within three years. Women were categorized to the vaccinated group if they received two doses of inactivated COVID-19 vaccines (Sinopharm or Sinovac) between AMH tests (n = 488), and to the control group if not vaccinated (n = 2672). Propensity score matching and multivariate linear regression were performed to control for potential confounders. The main outcome measures were the numeric AMH change and percentage AMH change between the two tests.ResultsThere were 474 women left in each group after matching all baseline characteristics. The mean interval from the first to second AMH measurement was 508.0 ± 250.2 and 507.5 ± 253.6 days for vaccinated and unvaccinated women, respectively (P = 0.680). Both groups had a significant AMH decrease in the second test compared with the first test (P = 0.001). However, the second AMH level remained comparable between groups (3.26 ± 2.80 vs. 3.24 ± 2.61 ng/mL, P = 0.757). Similarly, no significant differences were observed in numerical (-0.14 ± 1.32 vs. -0.20 ± 1.56 ng/mL, P = 0.945) and percentage (2.33 ± 58.65 vs. 0.35 ± 48.42%, P = 0.777) AMH changes. The results were consistent in sub-analyses for women aged <35 and ≥35 years. There were also no significant differences when vaccinated women were divided according to the time interval after vaccination: ≤30, 31-60, 61-90, and ≥91 days.ConclusionOur study provides the first evidence that inactivated COVID-19 vaccination has no measurable detrimental effect on ovarian reserve, regardless of female age and vaccination interval. This reassuring finding adds to the safety evidence of COVID-19 vaccine in fertility, and should be useful to promote vaccine acceptance. Multicenter prospective cohort studies are needed to validate our conclusion.

Project description:ObjectiveTo evaluate noninferiority of virtual transvaginal ultrasonography compared with in-clinic ultrasonography for ovarian reserve assessment.MethodsWe conducted a single-site, head-to-head crossover trial. Participants performed self-administered virtual transvaginal ultrasonography at home, guided by a remote-certified ultrasound technologist, then underwent transvaginal ultrasonography in-clinic with another ultrasound technologist. Participants were women in the greater Boston area interested in evaluating ovarian reserve and recruited through social media, health care referrals, and professional networks. The uterus and ovaries were captured in sagittal and transverse views. These randomized recordings were reviewed by two or three independent, blinded reproductive endocrinologists. The primary outcome was noninferiority of the rate of clinical quality imaging produced at home compared with in clinic. Sample size was selected for greater than 90% power, given the 18% noninferiority margin. Secondary outcomes included antral follicle count equivalency and net promoter score superiority.ResultsFifty-six women were enrolled from December 2020 to May 2021. Participants varied in age (19-35 years), BMI (19.5-33.9), and occupation. Ninety-six percent of virtual and 98% of in-clinic images met "clinical quality." The difference of -2.4% (97.5% CI lower bound -5.5%) was within the noninferiority margin (18%). Antral follicle counts were equivalent across settings, with a difference in follicles (0.23, 95% CI -0.36 to 0.82) within the equivalence margin (2.65). Virtual examinations had superior net promoter scores (58.1 points, 97.5% CI of difference 37.3-79.0, P<.01), indicating greater satisfaction with the virtual experience.ConclusionVirtual transvaginal ultrasonography remotely guided by an ultrasonography technologist is noninferior to in-clinic transvaginal ultrasonography for producing clinical quality images and is equivalent for estimating antral follicle count. Virtual transvaginal ultrasonography had superior patient satisfaction and has potential to significantly expand patient access to fertility care.Funding sourceThis study was sponsored by Turtle Health.Clinical trial registrationClinicalTrials.gov, NCT04687189.

Project description:Background The possible impact of ovarian endometriomas (OMAs) on in vitro fertilization (IVF) outcomes remains controversial. Therefore, this study aimed to assess the impact of OMAs on IVF cycle parameters, including ovarian reserve and response to stimulation, embryo quality and pregnancy outcomes. Methods This retrospective cohort study included 2067 patients undergoing their first IVF/ICSI cycles between January 2018 and December 2020. The study group included 154 infertile women who had OMAs. The control group consisted of 1913 women without endometriosis, and finally 305 women were matched according to maternal age, body mass index (BMI), and infertility duration by propensity score matching (PSM). Cumulative live birth rate (CLBR) was set as the primary outcome measure. Logistic regression analysis was conducted on the basis of clinical covariates assessed for their association with CLBRs. Subgroup analyses were performed to evaluate the effect of ovarian surgery, cyst size and laterality on CLBRs. Results Women with OMAs had significantly lower ovarian reserve markers (AMH and AFC), number of follicles, oocytes, embryos, and top-quality embryos than women in the control group (p < 0.05). However, the CLBRs were comparable between the two groups (55.64% versus 54.34%, p = 0.806), regardless of previous history of ovarian surgery. Multivariate analysis revealed association between age (OR = 0.861; 95% CI [0.806–0.921]; p = 0.000), top-quality embryos (OR = 1.829; 95% CI [1.526–2.193]; p = 0.000) and the CLBRs. A negative correlation between OMA size and AFC levels in patients with unoperated OMAs was detected (r = -0.264, p = 0.007). Meanwhile, significant decrease in ovarian reserve with lower AFC, fewer oocytes, embryos and top-quality embryos were observed in patients with OMAs size ≥ 6 cm (p < 0.05). Moreover, ovaries with OMAs had a significantly lower AFC (P = 0.006) but similar number of oocytes when compared with contralateral ovaries without OMAs. Conclusion Infertile women with OMAs were implicated in considerable decreases in ovarian reserve and response to stimulation, but no apparent adverse effects on oocyte quality or clinical outcomes. OMAs surgery and OMAs size may adversely affect ovarian reserve, but not CLBR. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-022-01042-9.

Project description:BackgroundAcetyl-CoA acetyltransferase 2 (ACAT2) is a lipid metabolism enzyme and rarely was researched in epithelial ovarian cancer (EOC).MethodsACAT2 expressions were confirmed in two pairs of cell lines (A2780 and A2780/DDP, OVCAR8 and OVCAR8/DDP) from Gene Expression Omnibus database by bioinformatics analysis, and in A2780 and A2780/DDP cell lines by quantitative real-time polymerase chain reaction and western blotting. Tissue samples were stained by immunohistochemistry and scored for ACAT2 expression. The relationships between ACAT2 expression and clinicopathological characteristics were analyzed by χ2 test. The prognosis of ACAT2 was analyzed by the log-rank tests and Cox regression models.ResultsACAT2 was remarkably upregulated in the above drug-resistant cell lines by mRNA (all P < 0.05) and protein expression (P = 0.026) than those in sensitive ones. Patients were classified as ACAT2-high (n = 51) and ACAT2-low (n = 26) according to immunohistochemical score. ACAT2 expression had a significantly inverse correlation with FIGO stage (P = 0.030) and chemo-response (P = 0.041). A marginal statistical significance existed in ACAT2 expression and ascites volume (P = 0.092). Univariate analysis suggested that high-expressed ACAT2 was associated with decreased platinum-free interval (PFI) (8.57 vs. 14.13 months, P = 0.044), progression-free survival (PFS) (14.12 vs. 19.79 months, P = 0.039) and overall survival (OS) (36.89 vs. 52.40 months, P = 0.044). Multivariate analysis demonstrated that ACAT2 expression (hazard ratio = 2.18, 95% confidence interval: 1.15-4.11, P = 0.017) affected OS independently, rather than PFI and PFS.ConclusionThe expression of ACAT2 in A2780/DDP and OVCAR8/DDP was higher than the corresponding A2780 and OVCAR8. High-expressed ACAT2 was associated with advanced FIGO stage, chemo-resistance, and decreased PFI, PFS and OS. It was an independent prognostic factor of OS in EOC.

Project description:BackgroundEarly detection of ovarian aging is of huge importance, although no ideal marker or acknowledged evaluation system exists. The purpose of this study was to develop a better prediction model to assess and quantify ovarian reserve using machine learning methods.MethodsThis is a multicenter, nationwide population-based study including a total of 1,020 healthy women. For these healthy women, their ovarian reserve was quantified in the form of ovarian age, which was assumed equal to their chronological age, and least absolute shrinkage and selection operator (LASSO) regression was used to select features to construct models. Seven machine learning methods, namely artificial neural network (ANN), support vector machine (SVM), generalized linear model (GLM), K-nearest neighbors regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to construct prediction models separately. Pearson's correlation coefficient (PCC), mean absolute error (MAE), and mean squared error (MSE) were used to compare the efficiency and stability of these models.ResultsAnti-Müllerian hormone (AMH) and antral follicle count (AFC) were detected to have the highest absolute PCC values of 0.45 and 0.43 with age and held similar age distribution curves. The LightGBM model was thought to be the most suitable model for ovarian age after ranking analysis, combining PCC, MAE, and MSE values. The LightGBM model obtained PCC values of 0.82, 0.56, and 0.70 for the training set, the test set, and the entire dataset, respectively. The LightGBM method still held the lowest MAE and cross-validated MSE values. Further, in two different age groups (20-35 and >35 years), the LightGBM model also obtained the lowest MAE value of 2.88 for women between the ages of 20 and 35 years and the second lowest MAE value of 5.12 for women over the age of 35 years.ConclusionMachine learning methods combining multi-features were reliable in assessing and quantifying ovarian reserve, and the LightGBM method turned out to be the approach with the best result, especially in the child-bearing age group of 20 to 35 years.

Dataset Information

A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study.

Purpose

Methods

Results

Conclusion

Publications

A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets