Clinical efficiency of embryo transfer performed in receptive vs non-receptive endometrium diagnosed by the endometrial receptivity array (era) test

Alonso, Maria Ruiz; Díaz-Gimeno, Patricia; Gómez, Eulalia; Rincón-Bertolín, Alejandro; Vladimirov, Yury A.; Garrido, Nicolás; Simón, Carlos

doi:10.1016/j.fertnstert.2014.07.994

Cited by 5 publications

(3 citation statements)

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“…Such data were collected in a study comparing the clinical outcome of pET in 205 receptive (R) patients versus embryo transfers performed in 52 non-receptive (NR) patients according to the ERA test. The clinical outcome was 23% PR and 13% IR after transfer in the NR phase, with 0% ongoing pregnancy rate (OPR); in contrast, when pET was performed based on the R phase, 60% PR, 45% IR and 74% OPR were achieved ( Ruiz-Alonso et al , 2014b ).…”

Section: A Decade Of Era Clinical Applicationmentioning

confidence: 99%

Endometrial Receptivity Analysis (ERA): data versus opinions

Ruiz-Alonso

Valbuena

Gómez

et al. 2021

Human Reproduction Open

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This article summarises and contextualises the accumulated basic and clinical data on the ERA test and addresses specific comments and opinions presented by the opponent as part of an invited debate. Progress in medicine depends on new technologies and concepts that translate to practice to solve long-standing problems. In a key example, combining RNA sequencing data (transcriptomics) with artificial intelligence (AI) led to a clinical revolution in personalising disease diagnosis and fostered the concept of precision medicine. The reproductive field is no exception. Translation of endometrial transcriptomics to the clinic yielded an objective definition of the limited time period during which the maternal endometrium is receptive to an embryo, known as the window of implantation (WOI). The WOI is induced by the presence of exogenous and/or endogenous progesterone (P) after proper oestradiol (E2) priming. The window lasts 30–36 hours and, depending on the patient, occurs between LH + 6 and LH + 9 in natural cycles or between P + 4 and P + 7 in hormonal replacement therapy (HRT) cycles. In approximately 30% of IVF cycles in which embryo transfer is performed blindly, the WOI is displaced and embryo-endometrial synchrony is not achieved. Extending this application of endometrial transcriptomics, the endometrial receptivity analysis (ERA) test couples next-generation sequencing (NGS) to a computational predictor to identify transcriptomic signatures for each endometrial stage: proliferative (PRO), pre-receptive (PRE), receptive (R) and post-receptive (POST). In this way, personalised embryo transfer (pET) may be possible by synchronising embryo transfer with each patient’s WOI. Data are the only way to confront arguments sustained in opinions and/or misleading concepts; it is up to the reader to make their own conclusions regarding its clinical utility.

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Section: A Decade Of Era Clinical Applicationmentioning

confidence: 99%

Endometrial Receptivity Analysis (ERA): data versus opinions

Ruiz-Alonso

Valbuena

Gómez

et al. 2021

Human Reproduction Open

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“…For a decade, endometrial RNA sequencing was used to classify patients as non-receptive endometrium, either pre-or post-receptive, and receptive endometrium, and guide personalized embryo transfer. Study has shown that the clinical outcome was 23% pregnancy rate and 13% implantation rate after transfer in the non-receptive phase, with 0% ongoing pregnancy rate; in contrast, embryo transfer during receptive phase, can achieve 60% pregnancy rate, 45% implantation rate and 74% ongoing pregnancy rate 3 . Ruiz Alonso et al have shown that after personalized embryo transfer, patients reached a 60% clinical pregnancy rate compared with a pregnancy rate of 19% after routine embryo transfer in a non-receptive endometrium diagnosed by ERA 4 .…”

Section: Introductionmentioning

confidence: 99%

A combined clinical and specific genes’ model to predict live birth for in vitro fertilization and embryo transfer patients

Meng

Shi

Jia

et al. 2023

Preprint

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Background: We aimed to develop an accurate model to predict live birth for patients receiving in vitro fertilization and embryo transfer (IVF-ET) treatment. Methods: This is a prospective nested case-control study. Women aged between 18 and 38 years, whose body mass index (BMI) were between the range of 18.5–24 kg/m2, who had an endometrium of ≥8 mm at the thickest were enrolled. All patients received IVF-ET treatment and were followed up until they had reproductive outcomes. Endometrial samples during the window of implantation (LH+ 6 to 9 days) were subjected to analyze specific endometrial receptivity genes’ expression using real-time PCR (RT-PCR). Patients were divided into live birth group and non-live birth group based on IVF-ET outcomes. Clinical signatures relevant to live birth were collected, analyzed, and used to establish a predictive model for live birth by univariate analysis (clinical model). Specific endometrial receptivity genes’ expression was analyzed, selected, and used to construct a predictive model for live birth by The Least Absolute Shrinkage and Selection Operator (LASSO) analysis (gene model). Finally, significant clinical factors and genes were used to construct a combined model for predicting live birth using multivariate logistical regression (combined model). Different models’ Area Under Curve (AUC) were compared to identify the most predictive model. Results: Thirty-nine patients were enrolled in the study, twenty-four patients had live births, fifteen did not. In univariate analysis, the odds of live birth for women with ovulation dysfunction was 4 times higher than that for women with other IVF-ET indications (OR=4.0, 95% CI: 1.125−8.910, P=0.018). Age, body mass index, duration of infertility, primary infertility, repeated implantation failure, antral follicle counting, ovarian sensitivity index, anti-Mullerian hormone, controlled ovarian hyperstimulation protocol and duration, total dose of FSH/hMG, number of oocytes retrieved, regiment of endometrial preparation, endometrium thickness before embryo transfer, type of embryo transferred were not associated with live birth (P>0.05). Only ovulation dysfunction was used to construct the clinical model and its AUC was 0.688. In lasso analysis, GAST, GPX3, THBS2 were found to promote the risk of live birth. AUCs for GAST, GPX3, THBS2 reached to 0.736, 0.672, and 0.678, respectively. The gene model was established based on these three genes and its AUC was 0.772. Ovulation dysfunction, GAST, GPX3, and THBS2 were finally used to construct the combined model, reaching the highest AUC (AUC=0.842). Conclusions: Compared to the single model, the combined clinical (Ovulation dysfunction) and specific genes’(GAST, GPX3, THBS2) model was more accurate to predict live birth for IVF-ET patients.

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“…In the non-receptive phase, the clinical outcome was 23% pregnancy rate and 13% implantation rate after transfer, with 0% ongoing pregnancy rate. In contrast, embryo transfer during receptive phase can achieve 60% pregnancy rate, 45% implantation rate and 74% ongoing pregnancy rate [ 5 ]. After ERA diagnosis, patients can reach a 60% clinical pregnancy rate in a receptive endometrium [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A combined clinical and specific genes’ model to predict live birth for in vitro fertilization and embryo transfer patients

Meng,

Shi,

Jia

et al. 2023

BMC Pregnancy Childbirth

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Background We aimed to develop an accurate model to predict live birth for patients receiving in vitro fertilization and embryo transfer (IVF-ET) treatment. Methods This is a prospective nested case–control study. Women aged between 18 and 38 years, whose body mass index (BMI) were between the range of 18.5–24 kg/m2, who had an endometrium of ≥ 8 mm at the thickest were enrolled from 2018/9 to 2020/8. All patients received IVF-ET treatment and were followed up until Jan. 2022 when they had reproductive outcomes. Endometrial samples during the window of implantation (LH + 6 to 9 days) were subjected to analyze specific endometrial receptivity genes’ expression using real-time PCR (RT-PCR). Patients were divided into live birth group and non-live birth group based on IVF-ET outcomes. Clinical signatures relevant to live birth were collected, analyzed, and used to establish a predictive model for live birth by univariate analysis (clinical model). Specific endometrial receptivity genes’ expression was analyzed, selected, and used to construct a predictive model for live birth by The Least Absolute Shrinkage and Selection Operator (LASSO) analysis (gene model). Finally, significant clinical factors and genes were used to construct a combined model for predicting live birth using multivariate logistical regression (combined model). Different models’ Area Under Curve (AUC) were compared to identify the most predictive model. Results Thirty-nine patients were enrolled in the study, twenty-four patients had live births, fifteen did not. In univariate analysis, the odds of live birth for women with ovulation dysfunction was 4 times higher than that for women with other IVF-ET indications (OR = 4.0, 95% CI: 1.125 − 8.910, P = 0.018). Age, body mass index, duration of infertility, primary infertility, repeated implantation failure, antral follicle counting, ovarian sensitivity index, anti-Mullerian hormone, controlled ovarian hyperstimulation protocol and duration, total dose of FSH/hMG, number of oocytes retrieved, regiment of endometrial preparation, endometrium thickness before embryo transfer, type of embryo transferred were not associated with live birth (P > 0.05). Only ovulation dysfunction was used to construct the clinical model and its AUC was 0.688. In lasso analysis, GAST, GPX3, THBS2 were found to promote the risk of live birth. AUCs for GAST, GPX3, THBS2 reached to 0.736, 0.672, and 0.678, respectively. The gene model was established based on these three genes and its AUC was 0.772. Ovulation dysfunction, GAST, GPX3, and THBS2 were finally used to construct the combined model, reaching the highest AUC (AUC = 0.842). Conclusions Compared to the single model, the combined model of clinical (Ovulation dysfunction) and specific genes (GAST, GPX3, THBS2) was more accurate to predict live birth for IVF-ET patients.

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Clinical efficiency of embryo transfer performed in receptive vs non-receptive endometrium diagnosed by the endometrial receptivity array (era) test

Cited by 5 publications

References 0 publications

Endometrial Receptivity Analysis (ERA): data versus opinions

Endometrial Receptivity Analysis (ERA): data versus opinions

A combined clinical and specific genes’ model to predict live birth for in vitro fertilization and embryo transfer patients

A combined clinical and specific genes’ model to predict live birth for in vitro fertilization and embryo transfer patients

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