Exploration of the value of progesterone and progesterone/estradiol ratio on the hCG trigger day in predicting pregnancy outcomes of PCOS patients undergoing IVF/ICSI: a retrospective cohort study

Contributing factors for pregnancy outcomes in women with PCOS after their first FET treatment: a retrospective cohort study

OBJECTIVE

We aim to explore the contributing factors of clinical pregnancy outcomes in PCOS patients undergoing their first FET treatment.

METHODS

A retrospective analysis was conducted on 574 PCOS patients undergoing their first FET treatment at a private fertility center from January 2018 to December 2021.

RESULTS

During the first FET cycle of PCOS patients, progesterone levels (aOR 0.109, 95% CI 0.018-0.670) and endometrial thickness (EMT) (aOR 1.126, 95% CI 1.043-1.419) on the hCG trigger day were associated with the clinical pregnancy rate. Similarly, progesterone levels (aOR 0.055, 95% CI 0.007-0.420) and EMT (aOR 1.179, 95% CI 1.011-1.376) on the hCG trigger day were associated with the live birth rate. In addition, AFC (aOR 1.179, 95% CI 1.011-1.376) was found to be a risk factor for preterm delivery.

CONCLUSIONS

In women with PCOS undergoing their first FET, lower progesterone levels and higher EMT on hCG trigger day were associated with clinical pregnancy and live birth, and AFC was a risk factor for preterm delivery. During FET treatment, paying attention to the patient's endocrine indicators and follicle status may have a positive effect on predicting and improving the pregnancy outcome of PCOS patients.

Constructing a predictive model for live birth following fresh embryo transfer in antagonist protocol for polycystic ovary syndrome

OBJECTIVE

The present research aims to assess the factors that influence live birth outcomes following fresh embryo transfers using antagonist protocols in individuals diagnosed with polycystic ovary syndrome (PCOS). Furthermore, it seeks to develop a predictive nomogram model to facilitate clinical decision-making and provide personalized treatment strategies.

METHODS

This retrospective cohort research analyzed the clinical data of 1242 individuals having PCOS who went through fresh embryo transfers employing antagonist protocols and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) at Fujian Provincial Maternal and Child Health Hospital between January 2018 and December 2022. Individuals were assigned randomly to a modeling group (869 cases) and a validation group (373 cases) in a 7:3 ratio. The Boruta algorithm and multivariable logistic regression were utilized to identify independent risk factors linked to live births after transfer. A predictive nomogram was subsequently developed. The discriminatory power of the model and its accuracy were monitored by utilizing receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

Multivariable logistic regression analysis identified several independent factors that influence live birth rates in fresh embryo transfer cycles for individuals having PCOS using antagonist protocols, including female age, body mass index (BMI), infertility duration, serum testosterone levels, progesterone levels at the time of human chorionic gonadotropin (hCG) injection, number of high-quality cleavage-stage embryos, type of embryo transferred, and the total number of embryos transferred. Based on these findings, a predictive nomogram was developed. The area under the ROC curve stood at 0.804 (95% confidence interval (CI), 0.775-0.833) for the modeling group and 0.807 (95% CI, 0.762-0.851) for the validation group. Calibration curves confirmed that the predictions of the nomogram closely matched the actual live birth outcomes. Decision curve analysis highlighted that the model provides significant net benefits for predicting live birth rates, with optimal performance across a probability range of 16.5 to 88.6%.

CONCLUSION

Independent factors, including female age, infertility duration, BMI, serum testosterone levels, progesterone levels on the day of hCG injection, and the number and type of high-quality cleavage-stage embryos transferred are pivotal in influencing live birth outcomes in fresh embryo transfer cycles under antagonist protocols in individuals with PCOS undergoing IVF/ICSI treatments. The predictive nomogram developed from these factors offers substantial predictive accuracy and clinical utility, providing a reliable basis for clinical prognosis, targeted interventions, and the development of personalized treatment plans.

Clinical data-based modeling of IVF live birth outcome and its application

BACKGROUND

The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods.

METHODS

The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications.

RESULTS

Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO.

CONCLUSIONS

The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.

Predictive Role of Progesterone Levels for IVF Outcome in Different Phases of Controlled Ovarian Stimulation for Patients With and Without Endometriosis: Expert View

The study aimed to review the role of basal, trigger, and aspiration day progesterone levels (PLs) as predictors of in vitro fertilization (IVF) success for patients with and without endometriosis. A non-systematic review was conducted by searching papers published in English during the period of 1990-2023 in MEDLINE and PubMed, Embase, The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register), and Web of Science. The most widely used IVF predictor success was the trigger day progesterone serum level. Many studies utilize the threshold level of 1.5-2.0 ng/ml. However, the predictive power of only progesterone level failed to show high sensitivity and specificity. Contrary, progesterone level on the trigger day combined with the number of mature retrieved oocytes had the highest predictive power. High baseline progesterone level was associated with poor IVF outcomes. Research on progesterone and IVF success in patients with endometriosis is limited but indicates that endometriosis patients seem to benefit from higher progesterone concentrations (≥ 37.1 ng/ml) in IVF cycles. Currently, there is limited data for a definitive insight into the mportance of progesterone in the estimation of IVF success. Nonetheless, this summarized evidence could serve as up-to-date guidance for the role of progesterone in the prediction of IVF outcomes, both in patients with and without endometriosis.

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China

BACKGROUND

The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations.

METHODS

This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.

RESULTS

Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.

CONCLUSIONS

This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Lavender and metformin effectively propagate progesterone levels in patients with polycystic ovary syndrome: A randomized, double-blind clinical trial

BACKGROUND

The present study aimed to evaluate the impacts of lavender and metformin on polycystic ovary syndrome (PCOS) patients.

METHODS

We performed a randomized, double-blind clinical trial including 68 females aged 18 to 45, fulfilling the Rotterdam criteria for PCOS. The patients were randomized to receive lavender (250 mg twice daily) or metformin (500 mg three times a day) for 90 days. The serum progesterone was measured at baseline and after 90 days, one week before their expected menstruation. Moreover, the length of the menstrual cycle was documented.

RESULTS

Our results showed that lavender and metformin treatment notably increased the progesterone levels in PCOS patients (increasing from 0.35 (0.66) and 0.8 (0.69) to 2.5 (6.2) and 2.74 (6.27) ng/mL, respectively, P < 0.001). However, we found no significant differences between the increasing effects of both treatments on progesterone levels. In addition, all patients in the lavender or metformin groups had baseline progesterone levels <3 ng/mL, reaching 14 (45.2%) patients >3 ng/mL. Lavender and metformin remarkably attenuated the menstrual cycle length in PCOS patients (decreasing from 56.0 (20.0) and 60 (12.0) to 42.0 (5.0) and 50.0 (14.0) days, respectively, P < 0.001). Furthermore, the decreasing effects of lavender on the menstrual cycle length were greater than the metformin group; however, it was not statistically significant (P = 0.06).

CONCLUSION

Lavender effectively increased progesterone levels and regulated the menstrual cycles in PCOS patients, similar to metformin. Therefore, lavender may be a promising candidate for the treatment of PCOS.

Low-molecular-weight heparin in thrombophilic women receiving in vitro fertilization/intracytoplasmic sperm injection: A meta-analysis

INTRODUCTION

This meta-analysis aimed to evaluate the efficacy and safety of low-molecular-weight heparin (LMWH) on pregnancy outcomes in thrombophilic women receiving in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).

MATERIAL AND METHODS

A systematic literature search of PubMed, EMBASE, the Cochrane Library, and China National Knowledge Infrastructure was performed to identify randomized controlled trials (RCTs) comparing LMWH with no treatment or placebo published from database inception until February 19, 2023. Primary outcomes were the clinical pregnancy rate and implantation rate, and secondary outcomes were the live birth rate, miscarriage rate, and the risk of bleeding events. The certainty of the evidence was rated using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system. Meta-analysis was conducted using STATA 14.0.

RESULTS

Five RCTs involving 1094 thrombophilic women receiving IVF/ICSI were finally included. Administration of LMWH was associated with statistically higher clinical pregnancy rate (4 RCTs, risk ratio [RR] 1.50, 95% confidence interval [CI] 1.23-1.82, p < 0.001, low certainty evidence), implantation rate (5 RCTs, RR 1.49, 95% CI 1.25-1.78, p < 0.001, very low certainty evidence), and live birth rate (2 RCTs, RR 2.15, 95% CI 1.60-2.89, p < 0.001, very low certainty evidence), but with statistically lower miscarriage rate (2 RCTs, RR 0.36, 95% CI 0.15-0.86, p = 0.021, very low certainty evidence). However, using LMWH was linked to a higher risk of bleeding events (2 RCTs, RR 2.36, 95% CI 1.49-3.74, p < 0.001, very low certainty evidence).

CONCLUSIONS

Very low certainty evidence suggests that administration of LMWH may benefit pregnancy outcomes in thrombophilic women receiving IVF/ICSI treatment, although it may also increase the risk of bleeding events. However, before putting our findings into practice, healthcare professionals should conduct an in-depth evaluation of the available evidence and specific patient situations. Furthermore, due to the low methodological quality of the included studies, more high-quality studies are needed to validate our findings in the future.

Oocyte degeneration in a cohort adversely affects clinical outcomes in conventional IVF cycles: a propensity score matching study

Background

Oocyte degeneration was mostly described in intracytoplasmic sperm injection (ICSI) cycles; there is no report showing the relationship between oocyte degeneration and clinical outcomes in conventional in vitro fertilization (IVF) cycles. This retrospective study using the propensity score (PS) matching method aimed to explore whether the presence of oocyte degeneration in conventional IVF cycles would affect the sibling embryo development potential and clinical outcomes.

Methods

Patients with at least one oocyte degenerated after short-term insemination and stripping were defined as the degeneration (DEG) group, while patients with no oocyte degenerated were defined as the non-degeneration (NONDEG) group. The PS matching method was used to control for potential confounding factors, and a multivariate logistic regression analysis was made to evaluate whether the presence of oocyte degeneration would affect the cumulative live birth rate (CLBR).

Results

After PS matching, basic characteristics were similar between the two groups, oocyte yield was significantly higher in the DEG group than the NON-DEG group (P < 0.05), mature oocyte number, 2 pronuclear (2PN) embryo number, 2PN embryo clearage rate, "slow" embryo number, "accelerated" embryo number, rate of cycles with total day 3 embryo extended culture, number of frozen embryo transfer (FET) cycles, transferred embryo stage, transferred embryo number, and live birth rate in fresh embryo transfer cycles were all similar between the two groups (P > 0.05), but the 2PN fertilization rate, available embryo number, high-quality embryo number, "normal" embryo number, frozen embryo number, blastocyst formation rate, and no available embryo cycle rate were all significantly lower in the DEG group than the NON-DEG group (P < 0.05). The cumulative live birth rate was also significantly lower in the DEG group than in the NON-DEG group (70.2% vs. 74.0%, P = 0.0019). Multivariate logistic regression analysis further demonstrated that the presence of oocyte degeneration in conventional IVF cycles adversely affects the CLBR both before (OR = 0.83, 95% CI: 0.75-0.92) and after (OR = 0.82, 95% CI: 0.72-0.93) PS matching.

Conclusion

Our findings together revealed that the presence of oocyte degeneration in a cohort of oocytes may adversely affect subsequent embryo development potential and clinical outcomes in conventional IVF cycles.

Effects of trigger-day progesterone in the preimplantation genetic testing cycle on the embryo quality and pregnancy outcomes of the subsequent first frozen-thawed blastocyst transfer

Objective

To assess whether progesterone (P) levels on the trigger day during preimplantation genetic testing (PGT) cycles are associated with embryo quality and pregnancy outcomes in the subsequent first frozen-thawed blastocyst transfer (FET) cycle.

Methods

In this retrospective analysis, 504 eligible patients who underwent ICSI followed by frozen-thawed embryo transfer (FET) with preimplantation genetic test (PGT) between December 2014 and December 2019 were recruited. All patients adopted the same protocol, namely, the midluteal, short-acting, gonadotropin-releasing hormone agonist long protocol. The cutoff P values were 0.5 and 1.5 ng/ml when serum P was measured on the day of human chorionic gonadotropin (HCG) administration, and cycles were grouped according to P level on the day of HCG administration. Furthermore, the effect of trigger-day progesterone on embryo quality and the subsequent clinical outcome of FET in this PGT population was evaluated.

Results

In total, 504 PGT cycles were analyzed. There was no significant difference in the number of euploid blastocysts, top-quality blastocysts, euploidy rate, or miscarriage rate among the three groups (P>0.05). The 2PN fertilization rate (80.32% vs. 80.17% vs. 79.07%) and the top-quality blastocyst rate (8.71% vs. 8.24% vs. 7.94%) showed a downward trend with increasing P, and the between-group comparisons showed no significant differences (P>0.05). The clinical pregnancy rate (41.25% vs. 64.79%; P<0.05) and live birth rate (35.00% vs. 54.93%; P<0.05) in subsequent FET cycles were substantially lower in the high-P group than in the P ≤ 0.5 ng/ml group. After adjustments were made for confounding variables, multivariate logistic regression analysis revealed that the high-P group had a lower clinical pregnancy rate (adjusted OR, 0.317; 95% CI, 0.145-0.692; P=0.004) and live birth rate (adjusted OR, 0.352; 95% CI, 0.160-0.773; P=0.009) than the low-P group in subsequent FET cycles, and the differences were significant.

Conclusions

This study demonstrates that in the PGT population, elevated P on the trigger day may diminish the top-quality blastocyst rate (although there is no difference in the euploidy rate). Trigger-day P is an important factor influencing clinical outcomes in subsequent FET cycles.

Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks

Background

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS.

Objectives

The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated.

Methods

Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs.

Results

Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs.

Conclusions

The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

Effect of BMI on the value of serum progesterone to predict clinical pregnancy outcome in IVF/ICSI cycles: a retrospective cohort study

Background

Numerous research have investigated the predictor role of progesterone (P) level on the human Chorionic Gonadotropin (hCG) trigger day of assisted reproductive technology (ART) outcomes. However, the relationship of progesterone levels on hCG day to clinical pregnancy outcomes in IVF/ICSI cycles for patients with different BMI groups is still elusive. This study aimed to investigate the effects of progesterone elevation on triggering day on clinical pregnancy rate (CPR) of IVF/ICSI cycles in patients with different female BMI.

Methods

We conducted a retrospective cohort study included 6982 normal-weight parents (18.5Kg/m2≤BMI<25Kg/m2) and 2628 overweight/obese patients (BMI≥25Kg/m2) who underwent fresh day 3 cleavage embryo transfer (ET) in IVF/ICSI cycles utilizing GnRH agonist to control ovarian stimulation.

Results

The interaction between BMI and P level on triggering day on CPRs was significant (p<0.001). The average level of serum P was reduced with the increase in maternal BMI. Serum P adversely affected CPR in distinct BMI groups. In the normal weight group, CPRs were decreasedas serum P concentrations gradually increased (p<0.001 for overall trend). The CPRs (lower than 65.8%) of progesterone level > 1.00 ng/ml on triggering day were significantly lower than that (72.4%) of progesterone level <0.5 ng/ml. In the overweight/obese group, CPRs showed a decrease statistically with progesterone levels of ≥2.00 ng/ml compared to progesterone levels of <0.5 ng/ml (51.0% VS. 64.9%, p=0.016). After adjusting for confounders, progesterone elevation (PE) negatively correlated with CPRs only in the normal weight group (OR: 0.755 [0.677-0.841], p<0.001), not in the overweight/obese group (p=0.063).

Conclusion

Women with higher BMI exhibited a lower progesterone level on triggering day. Additionally, PE on hCG day is related to decreased CPRs in GnRH agonist IVF/ICSI cycles with cleavage embryo transfers regardless of women's BMI level (normal weight VS. overweight/obesity).

Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation

Background

Selecting an appropriate and personalized Gn starting dose (GSD) is an essential procedure for determining the quality and quantity of oocytes in the controlled ovarian stimulation (COS) process of the in-vitro fertilization (IVF) treatment cycle. The current approach for determining the GSD is mainly based on the experience of a clinician, lacking unified and scientific standards. This study aims to establish a prediction model of GSD, based on which good COS outcomes can be achieved with the influencing factors comprehensively evaluated quantitatively.

Material and methods

We collected a total of 1555 patients undergoing the first oocytes retrieving cycle and conducted correlation analysis to find the significant factors related to the GSD. Two GSD models are built based on two popular machine learning approaches, and the one with better model performance is selected as the final model. Finally, clinical application and validation were conducted to verify the effectiveness of the proposed model.

Results

(1) Age, duration of infertility, type of infertility, body mass index (BMI), antral follicle count (AFC), basal follicle stimulating hormone (bFSH), estradiol (E₂), luteinizing hormone (LH), anti-Müllerian hormone (AMH) and COS treatment regimen were closely related to the GSD (P < 0.05). (2) The selected model has good modeling performance in terms of both root mean square error (RMSE) (29.87 ~ 34.21) and regression coefficient R (0.947 ~ 0.953). (3) A comprehensive evaluation of influencing factors for GSD is conducted and shows that the top four most significant factors are age, AMH, AFC, and BMI. (4) The proposed GSD can approximate the actual value well in the clinical application, with the mean absolute error of only 11.26 units, and the recommended results can prompt the number of oocytes retrieved (NOR) close to the optimal number.

Conclusion

Modeling the GSD value with machine learning approaches is feasible and effective, and the proposed model has good clinical application for determining the GSD in the IVF treatment cycle.