The addition of bilateral salpingectomy to TLH for prevention of ovarian cancer in women who do not carry a BRCA1/2 mutations do not show negative effects on the ovarian function. In addition, no perioperative complications are related to the salpingectomy step in TLH.

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Objective(s): to evaluate the impact of the COVID-19 pandemic on infertile couples' emotions, anxiety and future plans. Study design: An observational study was perfomed by Italian ART centers and online forums. In this study, infertile couples candidate to ART and whose treatment was blocked due to the COVID-19 lockdown were enrolled through an online survey. The psychological impact of COVID-19 was measured by Impact of Event Scale-Revised (IES-R) and by a short form of the Spielberger State-Trait Anxiety Inventory (STAI); Self-perceived anxiety related either to pregnancy safety and to economic crisis measured by VAS scale. Results: 627 patients completed the survey. The COVID-19 lock-down had a moderate/severe psychological impact on infertile patients (mean IES-R score 36.4 AE 16.6). The mean STAI score was 49.8 AE 15.3, with an overall incidence of STAI > 36 of 71 %. The mean VAS scale for anxiety perception was 45.3 AE 15.3. Women were more emotionally distressed, anxious and depressed than men (36.8 AE 16.4 vs 31.0 AE 18.4 for IES-R, respectively; p = 0.03). Notwithstanding the uncertainty about pregnancy safety, 64.6 % of respondents chose to maintain their reproductive programme. Economic crisis induced 11.5 % of the surveyed patients to give up their ART program. Respondents who had at least one relative affected by COVID-19 had a significantly higher IES-R score and anxiety VAS, but not higher STAI scores, than patients belonging to unaffected families. Conclusion(s): COVID-19 pandemic itself and the recommendation to stop ART program generated higher distress levels in infertile couples. The psychological impact of COVID-19 pandemic in infertility patients should not be underestimated, and a specific psychological support should be planned.

BackgroundIn the last decade, both endocrine and ultrasound data have been tested to verify their usefulness for assessing ovarian reserve, but the ideal marker does not yet exist. The purpose of this study was to find, if any, a statistical advanced model able to identify a simple, easy to understand and intuitive modality for defining ovarian age by combining clinical, biochemical and 3D-ultrasonographic data.MethodsThis is a population-based observational study. From January 2012 to March 2014, we enrolled 652 healthy fertile women, 29 patients with clinical suspect of premature ovarian insufficiency (POI) and 29 patients with Polycystic Ovary syndrome (PCOS) at the Unit of Obstetrics & Gynecology of Magna Graecia University of Catanzaro (Italy). In all women we measured Anti Müllerian Hormone (AMH), Follicle Stimulating Hormone (FSH), Estradiol (E2), 3D Antral Follicle Count (AFC), ovarian volume, Vascular Index (VI) and Flow Index (FI) between days 1 and 4 of menstrual cycle. We applied the Generalized Linear Models (GzLM) for producing an equation combining these data to provide a ready to use information about women ovarian reserve, here called OvAge. To introduce this new variable, expression of ovarian reserve, we assumed that in healthy fertile women ovarian age is identical to chronological age. ResultsGzLM applied on the healthy fertile controls dataset produced the following equation OvAge = 48.05 - 3.14*AHM + 0.07*FSH - 0.77*AFC - 0.11*FI + 0.25*VI + 0.1*AMH*AFC + 0.02*FSH*AFC. This model showed a high statistical significance for each marker included in the equation. We applied the final equation on POI and PCOS datasets to test its ability of discovering significant deviation from normality and we obtained a mean of predicted ovarian age significantly different from the mean of chronological age in both groups.ConclusionsOvAge is one of the first reliable attempt to create a new method able to identify a simple, easy to understand and intuitive modality for defining ovarian reserve by combining clinical, biochemical and 3D-ultrasonographic data. Although design data prove a statistical high accuracy of the model, we are going to plan a clinical validation of model reliability in predicting reproductive prognosis and distance to menopause.Electronic supplementary materialThe online version of this article (doi:10.1186/s13048-015-0149-z) contains supplementary material, which is available to authorized users.

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