Prediction Of Ovarian Response Research Articles

P-556 Genetic variation in genes related to folliculogenesis and steroidogenesis in Caucasian and Asian women: Baby steps towards a pharmacogenetic approach in assisted reproductive techniques

Abstract Study question What is the impact of single nucleotide polymorphisms (SNPs) in genes related to the folliculogenesis and steroidogenesis on ovarian response to stimulation? Summary answer SMAD2 rs13381619 and COMT rs4680 variants are associated with the follicular output rate (FORT). What is known already The prediction of ovarian response (OR) is crucial for an optimal and individualized management. Despite the good accuracy of ovarian reserve markers in the prediction of OR, a proportion of patients are unexpected hypo-responders. Therefore, a link between OR and individual genotype has been suggested. However, the available evidence is limited by small sample sizes and heterogeneous study designs, including patients with different profiles and allowing for dose adjustments during treatment. Our aim was to analyze the association between SNPs in genes related to folliculogenesis and steroidogenesis and OR in an ethnic diverse population treated with the same fixed-dose protocol. Study design, size, duration This is a secondary analysis of a multicenter multinational prospective study, including 368 patients from Vietnam, Belgium, and Spain (168 from Europe and 200 from Asia) recruited from 11/2016-06/2019. The study included predicted normal responders <38 years old undergoing their first or second ovarian stimulation (OS) cycle. All patients underwent OS in an antagonist protocol with a fixed daily dose of 150 IU rFSH until triggering. Participants/materials, setting, methods First, an interactome was developed using “FSHR” and “LHCGR” as keyword imputed in the Homo sapiens STRING database for protein network analysis. Genetic information from sixty-eight final SNPs involved in the folliculogenesis and steroidogenesis was obtained and used for clustering analysis. Secondly, genotype-phenotype analysis was performed. The prevalence of hypo-responders (<10 oocytes retrieved), number of oocytes retrieved, FORT and follicle to oocyte index (FOI) were compared between different SNPs genotypes. Main results and the role of chance Clustering analysis revealed four distinct genetic clusters according to the selected SNPs related to folliculogenesis and steroidogenesis. Principal component analysis (PCA) was used to display individuals’ distribution according to the fertility centre, resulting in an obvious distribution according to ethnicity. However, PCA failed to demonstrate a clinically significant association between our interactome and the outcomes of interest. We further refined our SNP panel by categorizing SNPs according to their physiological pathway and analyzed their individual impact on OR. Following multivariable linear regression adjusting for patient’s age, Anti-Mullerian hormone (AMH), duration of stimulation and ethnicity, SMAD2 rs13381619 genotype GC (estimated mean difference (EMD) 11.57, 95% Confidence interval (CI) 2.66-20.49), and COMT rs4680 genotype AA (EMD 18.31, 95% CI 4.91-31.72) were associated with improved FORT. No impact of the studied polymorphisms was found on the prevalence of hypo-response, number of oocytes retrieved nor FOI. Limitations, reasons for caution The study was performed in young women with a normal ovarian reserve to eliminate biases related to age-related fertility decline, precluding the extrapolation of the results to other populations. Moreover, the low prevalence of some genotypes might have limited the analysis of some of the included variants. Wider implications of the findings Our findings confirm the variation of SNPs prevalence according to ethnic diversity, highlighting the importance of the intercontinental design of our study. Moreover, a novel association between SMAD2 and COMT variants and OR was reported, opening new opportunities for further pharmacogenetics studies and paving the way towards personalized medicine. Trial registration number Not applicable

P-605 Effectiveness of follitropin delta in patients with potential poor response: A post hoc analysis from the ESTHER-1 trial

Abstract Study question What is the effectiveness of individualised follitropin delta dosing compared to conventional follitropin alfa dosing in patients with potential poor response undergoing ovarian stimulation? Summary answer Individualised follitropin delta dosing provides a favourable efficacy and safety balance and is as good as follitropin alfa in patients with potential poor ovarian response. What is known already Poor ovarian response to controlled ovarian stimulation constitutes a challenge in in-vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) treatment. The dosing algorithm of follitropin delta incorporates the prediction of ovarian response based on anti-Müllerian hormone (AMH), an accurate marker of ovarian reserve, and body weight, for an individualised dosing. In the general IVF/ICSI population and potential high responders, individualised follitropin delta reduces the risk of ovarian hyperstimulation syndrome (OHSS) and/or preventive interventions for OHSS, with sustained efficacy compared to conventional follitropin alfa. However, the performance of follitropin delta in the subpopulation of patients with potential poor response has not been evaluated. Study design, size, duration Post hoc analysis of 332 patients with potential poor response from the ESTHER-1 trial (determined by AMH level at screening <9 pmol/L). Patients received the maximum daily dose of 12 µg follitropin delta (fixed dosing throughout stimulation) as stipulated by the dosing algorithm for patients with AMH <15 pmol/l, or follitropin alfa at a starting dose of 150 IU, with potential dose adjustments from stimulation day 6 and onwards (maximum daily dose of 450 IU). Participants/materials, setting, methods Participants were women, 18–40 years, undergoing their first IVF/ICSI cycle in a GnRH antagonist protocol, including triggering with human chorionic gonadotrophin, IVF/ICSI insemination, and single or double blastocyst transfer on Day 5. Ultrasound was performed 10–11 weeks after transfer to confirm ongoing pregnancy. All pregnancies were followed until birth. Study outcomes include number of oocytes retrieved and number of good quality blastocysts, ongoing pregnancy and live birth rates, cycle cancellation and OHSS rates. Main results and the role of chance A total of 332 patients (25.0% of the overall ESTHER-1 population) had serum AMH at screening of < 9 pmol/l and were included in the analysis: 168 were treated with follitropin delta and 164 were treated with follitropin alfa. Baseline characteristics were similar between the treatment groups, with a mean age of 34.8 years, a median AMH of 5.7 pmol/l and a mean weight of 64.5 kg. With follitropin delta, the mean daily dose was 12.0 µg and with follitropin alfa 12.4 µg, resulting in mean total doses of 104.6 µg and 112.3 µg, respectively. The mean number of oocytes retrieved was 5.8 ± 3.5 with follitropin delta and 5.5 ± 3.6 with follitropin alfa and the mean number of good-quality blastocysts was 1.2 ± 1.4 with both treatments. The ongoing pregnancy rate was 29.8% with follitropin delta and 29.3% with follitropin alfa and the live birth rates were 29.2% and 28.7%, respectively. Thirteen patients (7.7%) treated with follitropin delta and 14 (8.5%) treated with follitropin alfa had their cycles cancelled due to poor response. One case of OHSS (0.6%) was observed in the follitropin delta group, and 3 cases (1.8%) were observed in the follitropin alfa group. Limitations, reasons for caution The main limitation of the study is its post hoc nature. Furthermore, the study is based on a relatively small number of patients. Wider implications of the findings This subgroup analysis, alongside the normoresponder and the potential hyperresponder subgroup of the ESTHER-1 trial, adds evidence for the effectiveness of follitropin delta across all ovarian reserve subgroups of the infertile population. Trial registration number NCT01956110

O-255 Ovarian Response Prediction Index (ORPI) as a predictor tool for ovarian response and clinical pregnancy in IVF/ICSI cycle : A Prospective Cohort Study

Abstract Study question Can incorporation of the Ovarian Response Prediction Index (ORPI), integrating AMH, AFC and age, offer a reliable prognostication of ovarian stimulation response in ART cycles? Summary answer The study affirms that ORPI, a three-variable index, emerges as a robust predictor for both ovarian response and clinical pregnancy rates within IVF/ICSI cycles. What is known already In the realm of individualized Controlled Ovarian Stimulation, precision in forecasting ovarian response proves pivotal. Ovarian reserve, influenced by age, AMH, and AFC, exhibits considerable heterogeneity among women of analogous age. While AMH and AFC function as pivotal markers, a holistic approach, exemplified by ORPI, stands out as a more comprehensive tool for predicting ovarian response. Study design, size, duration This prospective cohort study enrolled 302 subjects undergoing IVF cycles at a tertiary infertility center spanning from 1st January 2022 to 31st December 2023. Rigorous inclusion criteria mandated age ≤38 years, a BMI between 20–25 kg/m2, regular menstrual cycles, intact bilateral ovaries, no history of ovarian surgeries, absence of severe endometriosis, and no evidence of endocrine disorders. Participants/materials, setting, methods Subjects adhering to the inclusion criteria underwent venous blood sampling for AMH measurement and transvaginal ultrasound for AFC assessment. The ORPI was meticulously computed as (AMH × AFC) / patient age. Key endpoints encompassed the total oocytes retrieved, mature MII oocytes, and occurrences of clinical pregnancy. Rigorous statistical methodologies were employed for robust analysis. Main results and the role of chance The mean values for age (years), AMH (ng/mL), ORPI, AFC, and fertilized oocytes were 31.75 ± 4, 3.75 ± 3.39, 1.06 ± 1.22, 7.1 ± 2.94, and 6.11 ± 3.26, respectively. A highly significant positive correlation (p-value < 0.0001) was observed between ORPI and the numbers of oocytes and MII oocytes, with correlation coefficients of 0.714 and 0.746, respectively. Interpretation of the area under the ROC curve revealed that ORPI was a significant predictor of obtaining ≥4 oocytes at a cutoff point of > 0.2, with an area under the curve of 0.907. Similarly, for MII oocytes ≥4, ORPI was a significant predictor at a cutoff point of > 0.2, with an area under the curve of 0.937. In the case of positive clinical pregnancy, ORPI emerged as a significant predictor at a cutoff point of > 0.75, with an area under the curve of 0.822. The study underscored a substantial positive correlation between ORPI and both oocyte and MII oocyte counts, affirming ORPI’s pivotal role as a predictor for clinical pregnancy. Limitations, reasons for caution Limitations due to the constraint of sample size temper the generalizability of findings. The study did not account for live birth rates, and a comparative analysis with alternative markers was omitted. A more expansive, meticulously designed study is imperative. Wider implications of the findings ORPI, an innovative ovarian response biomarker, showcases considerable potential for predicting both ovarian response and clinical pregnancy rates. Its integration into individualized Controlled Ovarian Stimulation regimens holds promise for augmenting the counselling and prognostication process for patients embarking on IVF/ICSI cycles. Trial registration number MCDH/2023/28

Association of Anti-Müllerian Hormone on Oocyte Maturation, Fertilization, and Pregnancy Rates in Patients under Assisted Reproductive Technology Cycles: A Cross-Sectional Study.

Ovarian reserve is one of the most important factors that influences the success of assisted reproductive technology (ART). Recently, the role of anti-müllerian hormone (AMH) in ART has been investigated as a marker for the prediction of ovarian response. We aim to examine this relationship within a large Iranian population. In this cross-sectional study, we obtained data from 1000 infertile couples who referred to the Research and Clinical Centre of Yazd Infertility Clinic for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI). Serum AMH levels, oocyte count, numbers of fertilised oocytes, endometrial thickness, and percentage of mature oocytes were measured. The relationship between AMH serum levels and the number and quality of oocytes and embryos in ART cycles was analysed. In the linear regression model, the log of the variables total dose of gonadotropin, two pronuclei (2PN), log oestradiol, total embryos, duration of stimulation, number of embryos transferred, protocol, and cause of infertility were significant predictors of log AMH. There appears to be a relationship between serum AMH levels in the early follicular phase and ovarian reserve. Higher serum AMH levels were also associated with shorter ART cycles.

Evaluation of Ovarian Response Indexes as Predictors to the Outcome of Intracytoplasmic Sperm Injection

Abstract Background: Knowing predictors of pregnancy in assistant reproductive technology, knowing of predictors of pregnancy is helpful for clinicians to individualize treatment 14 Plans and improve patient counseling, and for patients to decide whether to undergo infertility treatment. Therefore, it is important to evaluate ovarian reserve prediction indexes as an index to predict response to assistant reproductive technology. Objectives: To evaluate the ovarian reserve index (AMH*AFC/Age), taking into account the total number of MII oocytes retrieved and the quality of embryo obtained as well as the pregnancy rate. Materials and Methods: This study is designed as a cohort prospective study and was conducted in Al-Sadder Teaching Medical City, Al Najaf Province. Sixty couples were enrolled. The gynecological examination was performed on day 2 of the cycle (CD2): vaginal ultrasound (U/S) and blood tests for FSH, LH, AMH, estradiol (E2), serum prolactin, and thyroid function test. The ovarian response prediction index (OPRI) values were measured by multiplying the AMH serum level (ng/mL) by the number of antral follicles with a diameter of 2–9 mm and then dividing the result by the age of the subjects (years). Results: The current study showed that the pregnancy rate of the studied group was 33.33%. There was a significant difference between pregnant and nonpregnant women in their AMH level (P value was 0.004). ORPI was significantly higher in pregnant women than nonpregnant women. Binary logistic regression analysis shows that there was a significant positive correlation of ORPI and higher AMH, AFC, lower BMI, MII, oocytes, and embryo number. ROC curve shows that ORPI had an AUC of 1.00 at cutoff point 2, giving a sensitivity and specificity of 97% and 100%, respectively, and an AUC of 1.00 at a cutoff point 1, giving a sensitivity and specificity of 100% and 95%, respectively, which indicate that ORPI is an important predicting factor for ICSI success rate. Conclusion: ORPI was significantly higher in pregnant women than in the nonpregnant group; AMH was significantly higher in pregnant women than in the nonpregnant group. Women with higher AMH, AFC, and lower BMI are more likely to have higher ORPI.

Effects of ovarian response prediction index and follicle-oocyte index on pregnancy outcomes: a retrospective cohort study of 12,218 fresh transfer cycles

Objective: To investigate the potential relationships among the ovarian response prediction index (ORPI), follicle-oocyte index (FOI), and clinical pregnancy rate (CPR) in women undergoing their first in vitro fertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI-ET) fresh cycle transfer. Methods: In this retrospective cohort study, we included 12,218 women who underwent their first IVF/ICSI-ET cycle between December 2014 and January 2021. The primary and secondary outcomes of our study were CPR and cumulative live birth rate (CLBR), respectively. The data were divided into three groups according to the ORPI and FOI tertiles. Multivariate logistic regression analyses, stratification analyses, interaction, restricted cubic splines, and receiver operating characteristic (ROC) curves were constructed to identify the relationships among ORPI, FOI, and CPR. Results: A statistically significant increase in CPR was detected from the lowest to the highest tertile group (ORPI: 48.12%, 54.07%, and 53.47%, P <0.001; FOI:49.99%, 52.95%, and 52.71%, P=0.012). A higher CLBR was observed in the high group (ORPI: 38.63%, 44.62%, and 44.19%, P <0.001; FOI: 41.02%, 43.78%, and 42.59%, P=0.039). Multivariate logistic regression analysis revealed no statistically significant differences between ORPI, FOI, and neither CPR [odds ratio (OR) (95% confidence interval (CI), 0.99 (0.97-1.00) vs. (1.02 (0.84-1.24)] nor CLBR [OR (95% CI), 0.99 (0.97-1.01) vs. 0.99 (0.81-1.20)]. No significant association was found among FOI, ORPI, and CPR, even in the subgroups. Restricted cubic spline analyses indicated the existence of a nonlinear relationship across the entire range of FOI and ORPI. The ORPI and FOI variables had poor predictive ability (AUC< 0.60) for CPR. Conclusions: Both ORPI and FOI are not reliable predictors of clinical pregnancy or live birth outcomes in fresh embryo transfers. Clinicians and researchers should avoid using FOI and ORPI to assess pregnancy outcomes after fresh embryo transfer because of their limited relevance and predictive value.

Significance of serum AMH and antral follicle count discrepancy for the prediction of ovarian stimulation response in Poseidon criteria patients.

To determine the risk of not being a poor responder in ovarian stimulation (OS) for in vitro fertilization (IVF) when ovarian reserve markers are discordant-one falling within Poseidon's criteria normal range (e.g., anti-Müllerian hormone (AMH) ≥ 1.2ng/mL or antral follicle count (AFC) ≥ 5), and the other in the poor ovarian reserve range. A tri-center retrospective cohort study (2015-2017) involving women with discordant AMH and AFC values undergoing their first IVF/ICSI cycle using conventional OS (cOS, ≥ 150IU/day of follicle-stimulating hormone). Discordant serum AMH and AFC values were defined according to Poseidon's criteria (AMH < 1.2ng/mL and AFC ≥ 5 or AMH ≥ 1.2ng/mL and AFC < 5). Poor ovarian response (POR) was < 4 retrieved oocytes. Receiver operating characteristic (ROC) curves were used to determine AMH and AFC cut-offs for non-POR. Logistic regression analysis evaluated factors associated with non-POR. Out of 8797 patients who underwent assessment with both AMH and AFC, 1172 (13.3%) exhibited discordant values. Of these, 854 (72.9%) had ≥ 4 oocytes retrieved. Within this group, 726 (85.0%) had "low" AMH values, whereas 128 (15.0%) had "low" AFCs. An AFC of 6 had 77% sensitivity and 52% specificity (AUC = 0.700), while AMH of 1.19ng/mL had 31% sensitivity and 85% specificity (AUC = 0.492) for non-POR. AFC and the use of recombinant gonadotropins were positive predictors of non-POR. When serum AMH is < 1.19ng/mL, but AFC is ≥ 6, there is a moderate likelihood of a non-POR during stimulation. Conversely, if AFC is < 5 but serum AMH is ≥ 1.19ng/mL, the chances of non-POR are low. Among patients with discordant markers, AFC emerges as the primary predictor of oocyte yield.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044

The journal and authors wish to retract the article entitled ‘Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images’ cited above [...]

Prediction of a poor ovarian response in assisted reproductive technology programs in patients after surgical interventions on the ovaries

Prediction of a poor ovarian response in assisted reproductive technology programs in patients after surgical interventions on the ovaries

Alteration of relative telomere length and telomerase reverse transcriptase expression in the granulosa cells of women during aging and assessment of in vitro fertilization outcomes.

Telomere attrition plays a critical role in the reproductive aging process in humans. Telomere length (TL) is typically regulated by telomerase, the main component of which is telomerase reverse transcriptase (TERT). The aim of the present study was to investigate the changes of relative TL (RTL) and TERT expression in granulosa cells (GCs) during aging and its association with reproduction. Clinical data on the frozen‑thawed embryo transfer cycles of older (>35year old) and younger (≤35year old) women from a single center over a 3‑year period were retrospectively analyzed. Preimplantation genetic testing for chromosome aneuploidies in older women during the same period was also analyzed. Following the analysis of the data, several biological characteristics of senescent GCs were explored. In addition, a total of 160women who were undergoing their first fresh cycle of invitro fertilization (IVF) or intracytoplasmic sperm injection were included in the study. GCs were collected from all participants. The changes of RTL and TERT expression in GCs during aging were investigated using quantitative PCR and western blotting. The associations of RTL and TERT with IVF outcomes were also assessed. The clinical data demonstrated that the pregnancy and live birth rates of women aged >35years were ~20% lower than those of women aged ≤35years, and the number of embryos with aneuploidy was 7‑fold of that without euploidy in the older age group. An aging‑induced change in follicle stimulating hormone receptor expression was observed. A shorter TL and increased TERT expression were detected in the older women. A significant inverse correlation between the expression levels of TERT and oocyte yield was identified. However, no association of RTL and TERT with blastocyst formation rate and the probability of clinical pregnancy was detected. It may be concluded that RTL and TERT alterations in GCs are potential determinants of ovarian aging. TERT expression in GCs appears to be a potential biomarker for the prediction of ovarian response, which provides a novel strategy for the assessment of female fertility.

Evaluative effectiveness of follicular output rate, ovarian sensitivity index, and ovarian response prediction index for the ovarian reserve and response of low-prognosis patients according to the POSEIDON criteria: a retrospective study.

The aim was to explore the implications of follicular output rate (FORT), ovarian sensitivity index (OSI), ovarian response prediction index (ORPI), and follicle-to-oocyte index (FOI) in low-prognosis patients defined by POSEIDON criteria. In total, 4030 fresh in vitro fertilization (IVF) cycles from January 2013 to October 2021 were included in this retrospective cohort analysis and were categorized into four groups based on the POSEIDON criteria. The FORT between Groups 1 and 2 (0.61 ± 0.34 vs. 0.65 ± 0.35, P = 0.081) and Groups 3 and 4 (1.08 ± 0.82 vs. 1.09 ± 0.94, P = 0.899) were similar. The OSI in the order from the highest to the lowest were 3.01 ± 1.46 in Group 1, 2.28 ± 1.09 in Group 2, 1.54 ± 1.04 in Group 3, and 1.34 ± 0.96 in Group 4 (P < 0.001). The trend in the ORPI values was consistent with that in the OSI. FORT, OSI, ORPI, and FOI complemented each other and offered excellent effectiveness in reflecting ovarian reserve and response, but they were not good predictors of clinical pregnancy rate (CPR) from IVF.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.

Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images. We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response. The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results. This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

Predicting the total number of retrieved oocytes following double ovarian stimulation (DuoStim).

Can anti-Müllerian hormone (AMH) help predict how many oocytes will be retrieved following double stimulation (DuoStim)? A simple clinical tool can use serum AMH values to predict ovarian response following DuoStim in IVF cycles. The knowledge that multiple follicular waves arise during a single ovarian cycle has led to the introduction of unconventional ovarian stimulation protocols. The DuoStim protocol involves two successive ovarian stimulations performed during a single ovarian cycle and has been proposed as an approach for patients with poor ovarian response and for medical fertility preservation. As AMH has been used as a marker of ovarian reserve and stimulation response, the current study aimed to investigate the diagnostic performance of AMH in predicting the number of retrieved oocytes following DuoStim. This is a retrospective observational study involving 116 patients who received IVF treatment from January 2021 to September 2022. The study was conducted at a private IVF centre. Only patients who had their AMH measured prior to treatment and had complete patient records regarding their clinical and IVF/ICSI cycle characteristics were included. The primary outcome was the correlation between AMH values and the number of oocytes retrieved following DuoStim. Parametric and non-parametric tests were used to compare baseline characteristics and outcomes. Spearman's R was used to analyse correlations between variables, while the C statistic was used to calculate the diagnostic performance of AMH. AMH levels were significantly correlated with the total number of oocytes retrieved after the DuoStim (R 0.61; CI 0.44-0.70; P < 0.0001). The difference in the total number of oocytes retrieved between the first (median 4 oocytes, interquartile range (IQR) 2-6) and second (median 6 oocytes, IQR 3.2-8) stimulation was statistically significant (P < 0.0001). However, there was no significant difference in the number of mature oocytes that were retrieved (median of 3 and 4 in the first and second stimulations, respectively). After the first stimulation, 68% of patients had at least one blastocyst available, while after the second stimulation, 74% did (NS). Based on linear regression, each 0.25 ng/ml increase in basal AMH corresponds to one additional oocyte recovered at the end of both stimulations (R2: 0.32, P < 0.0001). The results are limited owing to the observational nature of the study and the number of participants. Counselling infertile couples regarding the intermediate outcome of IVF (i.e. number of retrieved oocytes) is one of the most demanding tasks that clinicians face. To our knowledge, this is the first study that provides an easy-to-use clinical tool that enables the quantitative prediction of ovarian response following DuoStim, based on serum AMH values. No external funding was obtained for this study. The authors declare no conflicts of interest. N/A.

Comparison of anti-Müllerian hormone and antral follicle count in the prediction of ovarian response: a systematic review and meta-analysis

BackgroundIncreasingly studies reported that the Anti-Müllerian hormone (AMH) seems to be a promising and reliable marker of functional ovarian follicle reserve, even better than the AFC test. Our study aimed to conduct a meta-analysis to assess the predictive value of AMH and AFC for predicting poor or high response in IVF treatment. An electronic search was conducted, and the following databases were used: PubMed, EMBASE, and the Cochrane Library (up to 7 May 2022). The bivariate regression model was used to calculate the pooled sensitivity, specificity, and area under the receiver operator characteristic (ROC) curve. Subgroup analyses and meta-regression also were used in the presented study. Overall performance was assessed by estimating pooled ROC curves between AMH and AFC.ResultsForty-two studies were eligible for this meta-analysis. Comparison of the summary estimates for the prediction of poor or high response showed significant difference in performance for AMH compared with AFC [poor (sensitivity: 0.80 vs 0.74, P < 0.050; specificity: 0.81 vs 0.85, P < 0.001); high (sensitivity: 0.81 vs 0.87, P < 0.001)]. However, there were no significant differences between the ROC curves of AMH and AFC for predicting high (P = 0.835) or poor response (P = 0.567). The cut-off value was a significant source of heterogeneity in the present study.ConclusionsThe present meta-analysis demonstrated that both AMH and AFC have a good predictive ability to the prediction of poor or high responses in IVF treatment.

P-655 A prospective, observational, multivariate study to evaluate the best predictor of ovarian response, between AMH measured with fully automated assay and AFC.

Abstract Study question Which of AMH (measured with fully-automated assay) or AFC is the best predictive biomarker of ovarian response to exogenous gonadotropins in IVF/ICSI cycles? Summary answer In patients &amp;gt;35 years, AMH predicts the number of oocytes retrieved better than AFC and it has greater discriminating ability to detect suboptimal response. What is known already Individual variability in ovarian response to a starting dose of gonadotropins is a well-known aspect during controlled ovarian stimulation and many efforts have been made for obtaining the personalization of the treatment, identifying different biomarkers (age, basal FSH, BMI, AMH, AFC) that may predict the ovarian response. Among these biomarkers, AMH and AFC demonstrated the best performance in predicting ovarian response. Nevertheless, approximately one in five patients in clinical practice has a discordance between AMH and AFC; in these cases, the clinicians do not know which indicator should be chosen to individualize the protocol and the starting dose of gonadotropins. Study design, size, duration This prospective, observational, multivariate study compared fully-automated AMH with AFC in the prediction of ovarian response, defined as the number of oocytes retrieved and it involved 161 couples attending their first IVF/ICSI cycle between 2019 and 2022 (ClinicalTrials.gov NCT04168892). The patients underwent a GnRH antagonist protocol and received a HMG starting dose exclusively based on their age (150 IU if ≤ 35 years, 225 IU if &amp;gt; 35 years). Two physicians performed all the AFC determinations. Participants/materials, setting, methods The study was conducted at ANDROS Day Surgery Clinic, Palermo, Italy. Inclusion criteria were: female age 18-40 years, BMI 18-30 kg/m2, FSH ≤15 IU/l, normal menstrual cycles, normal uterine cavity, presence of both ovaries. Exclusion criteria were: PCOS, severe endometriosis, previous ovarian surgery, ovarian cysts, endocrinological diseases. Considering a power (1-β) of 90% and an alpha of 0.01, a total sample size of 160 couples was considered sufficient to verify the study hypothesis. Main results and the role of chance Three out of 161 patients were excluded after randomization. Eighty-eight out of 158 patients (55.7%) received 150 IU as starting dose (age=31.03±3.33; range 21-35 years); 70 patients (44.3%) received 225 IU (age=37.68±1.31, range 36-40). 9/158 cycles (5.6%) were suspended for hypo-response. The two groups were different in number of oocytes retrieved (p &amp;lt; 0.05), with higher number in ≤ 35 years group (M≤35=11.30±7.14, M&amp;gt;35=8.77±5.30). Univariate analyses revealed that the number of oocytes retrieved was correlated, in both groups, with AMH (ps &amp;lt; 0.01) and AFC (ps &amp;lt; 0.01), and in ≤ 35 years group with basal FSH (p = 0.03). No relationships were found with cause of infertility, BMI, smoking. Correlation coefficient (ρ) between AMH and AFC was 0.74 (p &amp;lt; 0.01) in ≤ 35 years group and 0.57 (p &amp;lt; 0.01) in &amp;gt; 35 years group. Multivariate Poisson regressions confirmed that AMH and AFC significantly predicted the number of oocytes retrieved in ≤ 35 years group (p &amp;lt; 0.01, Exp(B)=1.08; and p &amp;lt; 0.01, Exp(B)=1.03, respectively), while only AMH was a significant predictor in &amp;gt; 35 years group (p &amp;lt; 0.01, Exp(B)=1.18) (AFC: p = 0.34, Exp(B)=1.01). For prediction of suboptimal ovarian response (&amp;lt;8 oocytes retrieved), ROC analysis showed that AMH was not different from AFC in ≤ 35 years group (AUCAMH=0.811, AUCAFC=0.767; p = 0.331) but significantly better in &amp;gt; 35 years group (AUCAMH=0.858, AUCAFC=0.675; p &amp;lt; 0.001). Limitations, reasons for caution This is a single-center study; a multicenter trial could be useful for a further validation of the results. Moreover, many laboratories do not use automated assays for measuring AMH and this could make the results of this study hardly applicable in all contexts. Wider implications of the findings The results of this prospective trial highlight that, in patients &amp;gt;35 years, automatized AMH predicts the ovarian response better than AFC and it shows greater discriminating ability to detect suboptimal response. This could open new possibilities of individualizing treatment with AMH-based algorithms, especially in patients with the worst reproductive prognosis. Trial registration number NCT04168892

O-187 The combination of machine learning and pharmacogenetics for the prediction of sub-optimal ovarian response

Abstract Study question Can an artificial intelligence (AI)-based model predict the risk of suboptimal ovarian response using patient genetic data? Summary answer AI-based predictive models can identify relevant predicting factors for suboptimal ovarian response, including several genetic variants. What is known already A substantial proportion of women classified as “normo-responders” may not reach the optimal range of oocytes retrieved after COS. Polyzos and Sunkara (2015) defined a new category of women (15-36%) with a worse prognosis and a “suboptimal” level of ovarian response in which the number of oocytes retrieved ranges between 4 and 9 oocytes. The explanation for this behavior has been suggested to be genetic and indeed variants in FSH receptor and LH subunit-B have been identified that predispose to lower ovarian sensitivity to stimulation. Therefore, these women might require higher doses of gonadotropins or longer stimulations. Study design, size, duration This observational study included a retrospective analysis of 1370 ovarian stimulations from egg donors performed between March 2018 and April 2022. The oocyte donor candidates were selected according to our clinic donation program requirements and ASRM and ESHRE guidelines for oocyte donation. They were stimulated following a progesterone-primed ovarian stimulation protocol. All donors started stimulation with 150-300 IU/day of FSH according to AFC and BMI. A GnRH agonist was used for final oocyte maturation. Participants/materials, setting, methods Oocyte donors were healthy women between 18 and 35 years old (n = 504). In order to establish the predictive machine learning models, patient characteristics and controlled ovarian stimulation data were recorded in a data frame. In addition, donors were genotyped for 31 variants corresponding to 16 genes associated with ovarian response. The association between the different variables and risk of suboptimal ovarian response was analysed using SPSS (v23.0) and R (v. 4.2.0) statistical software. Main results and the role of chance The oocyte donors had a mean age of 25.4±4.0 and a high ovarian reserve (AFC: 18.4±7). The mean number of oocytes retrieved after ovarian stimulation was 16.3±7.5, of which 13.1±6.4 were mature. Despite being young women with good ovarian reserve, 16.4% of the stimulations were suboptimal (4-9 oocytes retrieved). Classical statistical method (binary logistic regression) and 5 different supervised classification machine learning algorithms (multi-layer perceptron, support vector-machines, k-nearst neighbors, random forest and eXtreme Gradient Boosting (XGBoost)) were used to establish a prediction model for suboptimal ovarian response. The model with the highest AUC value was the XGBoost (0.876). The accuracy, sensitivity and specificity were 0.798, 0.809 and 0.787 respectively. The variables that had the greatest predictive power in the best machine learning algorithm (XGBoost) were: age, BMI, AFC and total gonadotrophin dose. In addition, four genetic variants were identified that modified the risk of a suboptimal ovarian response: -ESR2 (oestrogen receptor; c.*39G&amp;gt;A). -LHB (LH beta subunit; c.82T&amp;gt;C/p.Trp28Arg). -SOD2 (superoxide dismutase; c.47T&amp;gt;C/p.Val16Ala). A mitochondrial enzyme that catalyses the detoxification and protection against redox damage that can occur in COS. -and TP53 (tumour suppressor protein; c.215C&amp;gt;T/p.Pro72Leu) that exerts a protective effect on DNA damage in folliculogenesis. Limitations, reasons for caution Our investigation was performed with retrospectively collected data, and hence it will be of importance to collect data prospectively to confirm that the new identified variants are associated with suboptimal ovarian response also in the IVF population. Wider implications of the findings The combination of AI and pharmacogenetics has led to the identification of genetic variants that might predispose to suboptimal ovarian response. Women who carry these variants (erroneously considered as normo-responders) could be candidates for personalization of their ovarian stimulation treatment with higher doses of gonadotrophins and/or longer stimulations. Trial registration number Not applicable

P-396 Modified Flare/GnRH antagonist protocol versus GnRH antagonist protocol in oocyte cryopreservation cycles

Abstract Study question Is there an advantage for modified flare/ GnRH antagonist protocol in comparison to antagonist protocol in oocyte cryopreservation cycles? Summary answer Modified flare/ GnRH antagonist protocol is at least as good as antagonist protocol in oocyte cryopreservation cycles, potentially improving maturity rate in subgroup of patients. What is known already Ultrashort flare GnRH agonist (GnRHa)/GnRH -antagonist protocol offer benefits of stimulatory effect of repeated microdose flare on endogenous FSH and the advantages of GnRH antagonist suppression, including the option to use GnRHa ovulation trigger to avoid ovarian hyperstimulation syndrome. GnRHa ovulation trigger has been associated with suboptimal response that refers to lesser than expected oocyte yield. Our research group previously demonstrated the value of GnRHa follicular challenge test (FACT) in prediction of suboptimal response assessed by post ovulation trigger LH levels and as a dynamic ovarian reserve test (not published yet). Study design, size, duration A retrospective, age- matched, cohort study that included all non-medical oocyte cryopreservation cycles from October 2020 to December 2021. The study group included 110 women that received modified flare/ GnRH antagonist protocol and were matched with 110 women that received antagonist protocol. Participants/materials, setting, methods Women in the modified flare/ GnRH antagonist protocol were administered GnRHa (Decapeptyl 0.2 mg) on day 2 to menstrual cycle followed by flexible antagonist protocol. They were compared to age matched women that received flexible antagonist protocol. In both study groups GnRHa final ovulation trigger was administered. Main results and the role of chance Among 110 women that underwent non-medical fertility preservation with modified Flare/GnRH antagonist protocol, in comparison to 110 women that received antagonist protocol, a reduced dosage of gonadotropins was consumed with more oocytes retrieved (14.63 vs. 13.86) and more mature oocytes cryopreserved (11.32 vs. 10.14), but differences were not significantly different. In a subgroup with more than 6 oocytes retrieved significantly more mature oocytes were cryopreserved in women that received modified flare/antagonist protocol (14.03± 0.90 vs. 11.63± 0.71, p = 0.03) in comparison to women that received antagonist protocol. Limitations, reasons for caution Limitations of our study are its retrospective nature and lack of information on the oocyte’s fertilization competence and pregnancy outcomes. Wider implications of the findings Future studies will further explore the advantages of this protocol in terms of higher oocytes maturity rate, intra cycle prediction of ovarian response and suboptimal response to GnRHa triggering. Trial registration number not applicable

O-067 Does the measurement of different AMH isoforms in poor responder patients improve the prediction of ovarian response?

Abstract Study question Does the measurement of AMH isoforms in low ovarian reserve patients improve the prediction of the number of oocytes after ovarian stimulation (OS)? Summary answer AMH assays measuring a broader range of AMH isoforms combined with antral follicle count (AFC) improve the prediction of oocytes collected after OS. What is known already Granulosa cells secrete AMH as a non-active prohormone (proAMH). After cleavage, it forms a complex (AMHN,C) which activates the receptor (AMHR2). Circulating AMH is a mixture of isoforms (proAMH, AMHN,C and other sub-fragments) after proteolysis, targeted by the monoclonal antibodies included in the AMH assays commonly used in clinical practice, potentially affecting the quantification of the hormone. Novel AMH assays include antibodies directed towards a broader range of the molecule. They may provide more accurate and reliable results, especially important in poor responders, as results may involve clinical decisions on whether or not to proceed with OS. Study design, size, duration Prospective observational study measuring AMH isoforms using five different AMH assays, including 72 women with low ovarian reserve performing OS for IVF/ICSI at a tertiary referral fertility center, from February 2019 to December 2021. Low ovarian reserve was defined as AMH serum levels &amp;lt;1.1ng/ml (Elecsys, Roche), following Bologna criteria. All patients performed OS for IVF/ICSI with antagonist protocol. Participants/materials, setting, methods On day 2/3 of cycle, prior to initiating the OS, AFC, serum FSH, LH, estradiol, progesterone and AMH levels were measured using Elecsys assay (Roche). Extra serum samples were frozen at -20C for subsequent analysis, using four different AMH assays (AnshLabs, Texas): AL-196 (PCOCheck ELISA), AL-124 (PicoAMH ELISA), AL-105 (US-AMH ELISA) and AL-133 (Total Mature-AMH ELISA). Ethical approval was obtained (Research Ethics Committee-REFA033c) and informed consent was signed by all participants. Main results and the role of chance Patient characteristics were [Median(IQR)]: age:39(36-42)years, BMI:28.51(26.1-30.1) Kg/m2, AFC:5.5(4-7), FSH:8.23(6.4-12.5) mIU/mL, LH 6.54(4.51-8.91) mIU/mL, Estradiol: 41.18(28.47-56.44)pg/mL, AMH-Elecsys: 0.64(0.29-0.81)ng/mL, AL-196:0.64(0.39-0.99)ng/mL, AL-124:0.79(0.49-1.24ng/mL, AL-105: 0.89(0.57-1.39)ng/mL, AL-133:1.06(0.63-1.59)ng/mL. Stimulation outcomes were: follicle number on the day of trigger (Fdot):5(3-7), cumulus-oocyte-complexes (COC’s):3(2-4.5) and metaphase II oocytes (MII):3(1-4). Spearman correlation was performed between AMH assays, AFC and OS outcomes. Elecsys revealed a good correlation with the other AMH assays (rs = 0.60-0.63,p&amp;lt;0.001), however, it was higher among ELISA assays (rs = 0.954-0.993,p&amp;lt;0.001). All AMH assays showed a significant positive correlation with AFC, Fdot, COC’s and MII. However, AL-196 showed the highest correlation (rs = 0.524, rs = 0.615, rs = 0.594, rs = 0.595, respectively;p&amp;lt;0.001). Different models combining one AMH assay plus AFC were created to predict COC’s and MII: AFC+AL-196 revealed the best prediction (Adjusted R2=0.474[p &amp;lt; 0.001] and 0.485[p &amp;lt; 0.001], respectively) when compared to the same AMH assay alone. AUROC analysis was performed to investigate which model predicted better &amp;lt; =3COC’s after OS. Although all models demonstrated fair values, AUROC was better for AFC+AL-196 model (AUROC:0.796), yet no significant differences were seen when compared to AUROC for AFC+AMH-Elecsys (p = 0.39). AFC and AMH were good predictors for COC’s collected [RR(95%CI):1.49(1.11-2.04),p&amp;lt;0.01; RR(95%CI):1.26(1.13-1.49),p&amp;lt;0.001, respectively). AFC+AMH-Elecsys predicted COC’s better than AFC alone (R2=0.47 vs 0.29, respectively;Performance-Score:42.56% vs 13.21%, respectively). AFC+AL-196 had the highest performance (R2=0.599;Performance-Score:90.12%). Limitations, reasons for caution Although clinical assessment was performed in the same centre following the same methodology, inter-observer variability for AFC is a limitation. Besides, fresh AMH serum samples were analysed using Elecsys assay, whereas frozen serum samples were shipped to Ansh Lab (Texas) for batched analysis. Wider implications of the findings For patients with serum AMH&amp;lt;1.1ng/mL, a wide range of oocytes might be expected after OS. Novel AMH assays, targeting a wider broad of the molecule, together with AFC, can be used for these patients to obtain a better prediction for clinical outcomes and anticipate very poor response. Trial registration number Not applicable

Nomogram to predict FSH starting dose in poor ovarian response women in progestin primed ovarian stimulation protocol

Prediction of individual ovarian response to exogenous gonadotropin is a cornerstone for success and safety in all controlled ovarian stimulation (COS) protocols. Providing the best FSH starting dose according to each woman’s own characteristics is the key to the success of individualized treatment. The objective of this investigation was to evaluate the potential application of a novel nomogram based on antral follicle counting (AFC), anti-Müllerian hormone (AMH) and body mass index (BMI) as a tool to optimize the follicle-stimulating hormone (FSH) starting dose in women with poor ovarian response in in-vitro fertilization (IVF)/intra-cytoplasmic sperm injection (ICSI) cycles in progestin-primed ovarian stimulation (PPOS). We performed a retrospective analysis involving 130 poor ovarian responders undergoing IVF/ICSI cycles in a PPOS protocol from June 2017 to February 2019 in our reproductive center. The individual FSH starting dose was selected according to patients’ clinical history and characteristics. The influence of variables including age, BMI, AMH and AFC on the FSH starting dose was assessed through multiple regression analysis. We used the variables reaching the statistical significance for calculation for the final predictive model. In the univariate analysis, BMI, AMH and AFC were significant (P < 0.05) predictors of FSH starting dose, age was canceled. In the multivariate analysis, BMI, AMH and AFC remained significant (P < 0.05). According to the nomogram, 118 patients (90.77% of 130) would have received a higher FSH starting dose and 12 patients (9.23% of 130) a lower FSH starting dose than practice dose. The application of the nomogram based on three variables easily determined in clinical practice: BMI, AMH and AFC would lead to a more tailored FSH starting dose in women with poor ovarian response.

The serum follicle stimulating hormone-to-luteinizing hormone ratios can predict assisted reproductive technology outcomes in women undergoing gonadotropin releasing hormone antagonist protocol.

The basal follicle stimulating hormone (FSH)/luteinizing hormone (LH) ratio is a useful predictor of ovarian response. In this study, we investigated whether the FSH/LH ratios during the entire controlled ovarian stimulation (COS) can be used as effective predictors of outcomes in women undergoing in vitro fertilization (IVF) treatment using the gonadotropin releasing hormone antagonist (GnRH-ant) protocol. A total of 1,681 women undergoing their first GnRH-ant protocol were enrolled in this retrospective cohort study. A Poisson regression model was used to analyze the association between the FSH/LH ratios during COS and embryological outcomes. Receiver operating characteristic analysis was performed to determine the optimal cutoff values for poor responders (≤ 5 oocytes) or poor reproductive potential (≤ 3 available embryos). A nomogram model was constructed to provide a tool for predicting the cycle outcomes of individual IVF treatments. The FSH/LH ratios (at the basal day, stimulation day 6 (SD6) and trigger day) were significantly correlated with the embryological outcomes. The basal FSH/LH ratio was the most reliable predictor of poor responders with a cutoff value of 1.875 (area under the curve (AUC) = 72.3%, P < 0.05), or of poor reproductive potential with a cutoff value of 2.515 (AUC = 66.3%, P < 0.05). The SD6 FSH/LH ratio predicted poor reproductive potential with a cutoff value of 4.14 (AUC = 63.8%, P < 0.05). The trigger day FSH/LH ratio predicted poor responders with a cutoff value of 9.665 (AUC = 63.1%, P < 0.05). The basal FSH/LH ratio, combined with the SD6 and trigger day FSH/LH ratios, slightly increased these AUC values and improved the prediction sensitivity. The nomogram provides a reliable model with which to assess the risk of poor response or poor reproductive potential directly based on the combined indicators. FSH/LH ratios are useful predictors of poor ovarian response or reproductive potential throughout the entire COS with the GnRH antagonist protocol. Our findings also provide insights into the potential for LH supplementation and regimen adjustment during COS to achieve improved outcomes.