Is a woman's chronological age or 'ovarian age' more important in determining perinatal outcome after assisted reproductive treatment?

Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification

PURPOSE

To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.

METHODS

The study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models.

RESULTS

Throughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age.

CONCLUSIONS

The Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.

Poor Response to Gonadotropin Stimulation and Perinatal Outcomes in Fresh In Vitro Fertilization Embryo Transfer Cycles-A Retrospective Cohort Study

Objective: The objective was to examine the association between poor ovarian response to gonadotropin stimulation for in vitro fertilization (IVF) and adverse perinatal outcomes in singleton gestations in young patients. Methods: This was a retrospective cohort study including women aged 17-39 who underwent fresh embryo transfer and delivered a singleton neonate at a single center (pre-implantation genetic testing excluded) (2007-2022). Patients were classified as one of the following categories: poor responders-daily follicle-stimulating hormone (FSH) ≥ 150 IU yielding ≤ 3 retrieved oocytes; normal responders-4-15 oocytes; and high responders with ≥16 oocytes. The primary outcome was a composite of pre-eclampsia (mild or severe), small-for-gestational-age, gestational diabetes mellitus, and preterm birth (<37 weeks). We compared maternal and neonatal outcomes between the three groups. Multivariable logistic regression was used to control for confounders. Results: Overall, 507 women met the inclusion criteria. Of them, there were 44 (8.68%) poor responders, 342 (67.46%) normal responders, and 121 (23.87%) high responders. Poor responders, compared to normal and high responders, were characterized by a higher maternal age (34.64 ± 4.01 vs. 31.4 ± 5.04 vs. 30.01 ± 4.93, p < 0.001, respectively) and total FSH dosage (3028.41 ± 1792.05 IU vs. 2375.11 ± 1394.05 IU vs. 1869.31 ± 1089.63 IU, p < 0.001). The perinatal outcomes examined, including cesarean delivery (CD) rate and the composite outcome, were comparable between groups. Using multivariable logistic regression and adjusting for ovarian response group, maternal age, nulliparity, and estradiol level and endometrial thickness before ovulation triggering, poor response was not associated with CD rate or the composite outcome, with maternal age associated with CD (p = 0.005), and nulliparity with the composite outcome (p = 0.007). Similar results were obtained when comparing poor responders to each other group separately or to all other responders. Conclusions: Poor ovarian response is not associated with increased adverse maternal or neonatal outcomes.

Ovarian cortical follicle density in infertile women with low anti-Müllerian hormone

PURPOSE

To evaluate the association between anti-Müllerian hormone (AMH) and follicle density in infertile women with diminished ovarian reserve (DOR) versus women with normal ovarian reserve?

METHODS

Case-control study comparing follicle densities in ovarian cortex from 20 infertile women with DOR (AMH ≤ 5 pmol/L) and 100 controls with presumed normal ovarian reserve.

RESULTS

For all women > 25 years, the follicle densities correlated positively with AMH levels. For each single picomole per liter increase in AMH the follicle density increased by 6% (95% CI 3.3-8.5%) when adjusted for age. This was similar for women with DOR and controls. The follicle density was 1.8 follicles/mm³ cortical tissue in women with DOR versus 7.0 in age-paired controls (p = 0.04). The women with DOR had a median AMH of 1.8 pmol/L versus 14.4 pmol/L in the age-paired control group (p < 0.001). The ratio of AMH/follicle density was 1:1 (1.8/1.8) in women with DOR and 2:1 (14.4/7.0) in the age-paired controls. Analyses for gonadotropin receptor polymorphisms could not explain the characteristics of women with DOR. The proportion of secondary follicles was higher in women with DOR compared with controls (4.6% versus 1.4%, p = 0.0003). Pooling all patients, the follicle density decreased significantly by 7.7% for every year added (p < 0.0001). The women with DOR had lower follicle densities than the controls, but the slopes were equal in the two cohorts.

CONCLUSIONS

Follicle density and AMH concentrations correlate also when AMH is low. However, AMH is only a reliable marker for the true ovarian reserve when age is included in the estimation and women with DOR may have more follicles than their AMH levels imply.