20 research outputs found

    How to define, diagnose and treat poor responders? Responses from a worldwide survey of IVF clinics

    No full text
    Poor responders represent a significant percentage of couples treated in IVF units (10–24%), but the standard definition of poor responders remains uncertain and consequently optimal treatment options remain subjective and not evidence-based. In an attempt to provide uniformity on the definition, diagnosis and treatment of poor responders, a worldwide survey was conducted asking IVF professionals a set of questions on this complex topic. The survey was posted on www.IVF-worldwide.com, the largest and most comprehensive IVF-focused website for physicians and embryologists. A total of 196 centres replied, forming a panel of IVF units with a median of 400 cycles per year. The present study shows that the definition of poor responders is still subjective, and many practices do not use evidence-based treatment for this category of patients. Our hope is that by leveraging the great potential of the internet, future studies may provide immediate large-scale sampling to standardize both poor responder definition and treatment options

    The effect of hyaluronic acid in embryo transfer media in donor oocyte cycles and autologous oocyte cycles: a systematic review and meta-analysis

    No full text
    STUDY QUESTION: Does the addition of hyaluronic acid (HA) to embryo transfer medium improve pregnancy outcomes in both autologous and oocyte donation IVF cycles? SUMMARY ANSWER: The best available evidence indicates that the addition of HA to embryo transfer medium is clinically beneficial in cycles with autologous oocytes. WHAT IS KNOWN ALREADY: There is a known clinical benefit of HA addition to embryo transfer media but it is not known if HA affects donor and autologous oocyte cycles differently. STUDY DESIGN, SIZE, DURATION: A systematic review with meta-analysis was performed. The Cochrane Gynaecology and Fertility Group Trials Register, CENTRAL via Cochrane Register of Studies Online (CRSO), MEDLINE, Embase and PsycINFO electronic databases (until 8 January 2020) were searched for randomized controlled trials (RCTs) examining the effect of HA in embryo transfer medium on pregnancy outcomes. PARTICIPANTS/MATERIALS, SETTING, METHODS: RCTs with separate donor and autologous oocyte data that compared embryo transfer medium with functional HA concentrations (0.5 mg/ml) to those containing no or low HA concentrations (0.125 mg/ml) were included. Two review authors independently selected trials for inclusion, extracted data and assessed the included studies using the Cochrane risk of bias assessment tool. Pooled risk ratios and 95% CIs were calculated. A summary of findings table was generated using Grading of Recommendations, Assessment, Development and Evaluation criteria. Judgements about evidence quality were justified and incorporated into the reported results for each outcome. MAIN RESULTS AND THE ROLE OF CHANCE: Fifteen studies, totalling 4686 participants, were analysed. In autologous oocyte cycles, live birth increased from 32% to 39% when embryo transfer media contained functional HA concentrations (risk ratio (RR) 1.22, 95% CI 1.11-1.34; nine studies, 3215 participants, I2 = 39%, moderate-quality evidence (number needed to treat (NNT) 14). HA-enriched media increased clinical pregnancy and multiple pregnancy rates by 5% and 8%, respectively (RR 1.11, 95% CI 1.04-1.18; 13 studies, 4014 participants, I2 = 0%, moderate-quality evidence, NNT 21) and (RR 1.49, 95% CI 1.27-1.76; 5 studies, 2400 participants, I2 = 21%, moderate-quality evidence, number needed to harm 13). Conversely, in donor oocyte cycles, HA addition showed little effect on live birth and clinical pregnancy (RR 1.12 95% CI 0.86-1.44; two studies, 317 participants, I2 = 50%, low-quality evidence) and (RR 1.06, 95% CI 0.97-1.28; three studies, 351 participants, I2 = 23%, low-quality evidence). There was insufficient available information on multiple pregnancy in donor oocyte cycles and on total adverse effects in both groups to draw conclusions. LIMITATIONS, REASONS FOR CAUTION: There were limited studies with separate data on donor oocyte cycles and limited information on oocyte quality. Additionally, one-third of the included studies did not include the main outcome, live birth rate. WIDER IMPLICATIONS OF THE FINDINGS: There is a moderate level of evidence to suggest that functional HA concentration in embryo transfer medium increases clinical pregnancy, live birth and multiple pregnancy rates in IVF cycles using autologous oocytes. This effect was not seen in donor oocyte cycles, indicating either intrinsic differences between donor and autologous oocytes or lack of statistical power. The combination of HA addition to transfer media in cycles using autologous oocytes and a single embryo transfer policy might yield the best combination, with higher clinical pregnancy and live birth rates without increasing the chance of multiple pregnancies. STUDY FUNDING/COMPETING INTEREST(S): No financial assistance was received. The authors have no competing interests. REGISTRATION NUMBER: N/A

    Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

    No full text
    In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time‐lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation‐labeled and >5500 implantation‐labeled embryos. Prediction of embryo implantation is more accurate than the current state‐of‐the‐art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology
    corecore