A decision-making algorithm for performing or cancelling embryo transfer in patients at high risk for ovarian hyperstimulation syndrome after triggering final oocyte maturation with hCG

Controlled ovarian stimulation leads to cardiovascular changes in patients undergoing in vitro fertilization

OBJECTIVE

To study hemodynamic changes along controlled ovarian stimulation in women undergoing in vitro fertilization.

STUDY DESIGN

Prospective observational cohort study conducted at Mother and Child Department of University Hospital Federico II, in Naples, Italy, between April 2021 and July 2022. Sixty-eight infertile patients undergoing controlled ovarian stimulation with gonadotropin, antagonist protocol and a fresh embryo transfer were included. Haemodynamic assessment was carried out using UltraSonic Cardiac Output Monitor at baseline (T1), estradiol peak (T2), fresh embryo-transfer day (T3). To evaluate relationships between quantitative variables and groups a Student T test for independent data was assessed. One-way analysis of variance (ANOVA) was used to determine the differences between the means of three time points (T1, T2 and T3) for quantitative variables. A mixed-model analysis of variance (ANOVA) was used to determine the differences between groups, among time points (T1, T2 and T3).

RESULTS

Sixty-eight patients were included. Significant differences over the three time points have been observed for CO (f = 3.78 l/min; p = 0.025), SVI (f = 3.56 ml/m2;p = 0.013), and RSVI (f = 4.84 dscm-5 m2; p = 0.009). No significant differences in trends have been found between beta hCG positive and beta hCG negative groups. There were no significant differences in maternal hemodynamic parameters at time-point T3 between patients treated with hCG 10,000 UI and with Triptorelin. Patients considered at increased risk of hyperstimulation reported a significant increase in SVI at baseline (26.9 ± 9.0 mL/m2 vs 21.9 ± 7.0 mL/m2; p = 0.010).

CONCLUSION

According to the results of our study, during controlled ovarian stimulation with antagonist protocol, patients undergo significant changes in maternal cardiovascular parameters over a very short period.

The molecular mechanism of naringin improving endometrial receptivity of OHSS rats

Controlling ovarian hyperstimulation syndrome (OHSS) in the controlled ovarian hyperstimulation treatment is necessary to increase the implantation success rate. This study aimed to explore the effect of naringin on the endometrial receptivity of OHSS rats. Female rats were randomly assigned to six groups: Blank, model, low-dose naringin (100 mg/kg/day), medium-dose naringin (200 mg/kg/day), high-dose naringin (400 mg/kg/day), and positive (0.18 mg/kg/day estradiol valerate) groups. Except for the blank group, rats established the OHSS model on Day 7, and their treatments were from Day 0 to 14, separately. Hematoxylin and eosin, immunohistochemical, and scanning electron microscopy were performed to detect the naringin effects on the endometrial receptivity of the OHSS model. Next, circRNAs transcriptome analysis was performed to screen circRNAs. Western blot analysis and real-time quantitative PCR were used to verify it. Our study showed that naringin treatments increased embryo number, endometrial thickness, pinopodes number, and Ki67 expression in the OHSS rats. Moreover, the result of circRNAs transcriptome sequencing showed that naringin significantly inhibited the rnocirc_008140 expression in the OHSS rats and significantly inhibited the changes of 28 gene ontology terms and three Kyoto Encyclopedia of Genes and Genomes pathways which were induced by OHSS. Abcc4 and Rps6ka5 genes were the enriched genes of those pathways. Finally, 24 miRNA target genes of rnocirc_008140 were predicted. Our study showed that naringin significantly improved the endometrial receptivity of OHSS rats to increase the embryo implantation success by reducing rnocirc_008140-adsorbed miRNAs to regulate Abcc4 and Rps6ka5 expression.

Metabolic landscape and pathogenic insights: a comprehensive analysis of high ovarian response in infertile women undergoing in vitro fertilization

BACKGROUND

In the realm of assisted reproduction, a subset of infertile patients demonstrates high ovarian response following controlled ovarian stimulation (COS), with approximately 29.7% facing the risk of Ovarian Hyperstimulation Syndrome (OHSS). Management of OHSS risk often necessitates embryo transfer cancellation, leading to delayed prospects of successful pregnancy and significant psychological distress. Regrettably, these patients have received limited research attention, particularly regarding their metabolic profile. In this study, we aim to utilize gas chromatography-mass spectrometry (GC-MS) to reveal these patients' unique serum metabolic profiles and provide insights into the disease's pathogenesis.

METHODS

We categorized 145 infertile women into two main groups: the CON infertility group from tubal infertility patients and the Polycystic Ovary Syndrome (PCOS) infertility group. Within these groups, we further subdivided them into four categories: patients with normal ovarian response (CON-NOR group), patients with high ovarian response and at risk for OHSS (CON-HOR group) within the CON group, as well as patients with normal ovarian response (PCOS-NOR group) and patients with high ovarian response and at risk for OHSS (PCOS-HOR group) within the PCOS group. Serum metabolic profiles were analyzed using GC-MS. The risk criteria for OHSS were: the number of developing follicles > 20, peak Estradiol (E2) > 4000pg/mL, and Anti-Müllerian Hormone (AMH) levels > 4.5ng/mL.

RESULTS

The serum metabolomics analysis revealed four different metabolites within the CON group and 14 within the PCOS group. Remarkably, 10-pentadecenoic acid emerged as a discernible risk metabolite for the CON-HOR, also found to be a differential metabolite between CON-NOR and PCOS groups. cysteine and 5-methoxytryptamine were also identified as risk metabolites for the PCOS-HOR. Furthermore, KEGG analysis unveiled significant enrichment of the aminoacyl-tRNA biosynthesis pathway among the metabolites differing between PCOS-NOR and PCOS-HOR.

CONCLUSION

Our study highlights significant metabolite differences between patients with normal ovarian response and those with high ovarian response and at risk for OHSS within both the tubal infertility control group and PCOS infertility group. Importantly, we observe metabolic similarities between patients with PCOS and those with a high ovarian response but without PCOS, suggesting potential parallels in their underlying causes.

A Personalized Management Approach of OHSS: Development of a Multiphase Prediction Model and Smartphone-Based App

OBJECTIVE

This study aimed to develop multiphase big-data-based prediction models of ovarian hyperstimulation syndrome (OHSS) and a smartphone app for risk calculation and patients' self-monitoring.

METHODS

Multiphase prediction models were developed from a retrospective cohort database of 21,566 women from January 2017 to December 2020 with controlled ovarian stimulation (COS). There were 17,445 women included in the final data analysis. Women were randomly assigned to either training cohort (n = 12,211) or validation cohort (n = 5,234). Their baseline clinical characteristics, COS-related characteristics, and embryo information were evaluated. The prediction models were divided into four phases: 1) prior to COS, 2) on the day of ovulation trigger, 3) after oocyte retrieval, and 4) prior to embryo transfer. The multiphase prediction models were built with stepwise regression and confirmed with LASSO regression. Internal validations were performed using the validation cohort and were assessed by discrimination and calibration, as well as clinical decision curves. A smartphone-based app "OHSS monitor" was constructed as part of the built-in app of the IVF-aid platform. The app had three modules, risk prediction module, symptom monitoring module, and treatment monitoring module.

RESULTS

The multiphase prediction models were developed with acceptable distinguishing ability to identify OHSS at-risk patients. The C-statistics of the first, second, third, and fourth phases in the training cohort were 0.628 (95% CI 0.598-0.658), 0.715 (95% CI 0.688-0.742), 0.792 (95% CI 0.770-0.815), and 0.814 (95% CI 0.793-0.834), respectively. The calibration plot showed the agreement of predictive and observed risks of OHSS, especially at the third- and fourth-phase prediction models in both training and validation cohorts. The net clinical benefits of the multiphase prediction models were also confirmed with a clinical decision curve. A smartphone-based app was constructed as a risk calculator based on the multiphase prediction models, and also as a self-monitoring tool for patients at risk.

CONCLUSIONS

We have built multiphase prediction models based on big data and constructed a user-friendly smartphone-based app for the personalized management of women at risk of moderate/severe OHSS. The multiphase prediction models and user-friendly app can be readily used in clinical practice for clinical decision-support and self-management of patients.