Sperm heads exhibit birefringence when examined with polarized light microscopy as a result of visualization of highly ordered structures within the acrosome and nucleus. DNA damage in sperm can be positively correlated with increased sperm head retardance. Polarized light microscopy has been used to assess sperm quality prior to intracytoplasmic sperm injection (ICSI). Selecting sperm with partial birefringence under polarized light identifies a normal sperm structure as well as DNA integrity and improves success of clinical pregnancies in patients with severe male factor infertility. Previous studies suggest a possible optimum range of retardance in sperm heads that reflects partial birefringence and DNA integrity of single sperm.This study was designed to establish a range of retardance in sperm heads examined under polarized light microscopy to select an optimum sperm prior to ICSI. At the time of ICSI, individual sperm heads of 63 couples undergoing ICSI in women 38 years or younger were imaged and saved. The images were later analyzed for retardance blinded to embryo and cycle outcomes.Fertilization rates were not related to sperm head retardance (ie, sperm head retardance was similar whether fertilization occurred). The best-quality embryos on days 3 and 5 were obtained from selection of sperm with head retardance ranging from 0.56 nm or greater to 0.91 nm or less. The likelihood of a clinical pregnancy occurring when sperm head retardance was within this range was high; the odds ratio was 3.74, with a 95% confidence interval, 1.43-9.77; P = 0.007.These data show for the first time that sperm head retardance between 0.56 and 0.91 nm is associated with optimal embryo quality, development, and clinical pregnancy rates. Measuring retardance appears to be a reproducible and reliable technique to increase embryo quality and clinical pregnancy rates.
BMI, parity, infertility diagnoses and live birth (>22 weeks, R300 grams) outcome were extracted from clinic SARTCORS data. All first, fresh, autologous IVF cycles 2012-2016 were included. This sample size exceeds the minimum 100 events and 100 nonevents for validation (1). The Luke logistic regression model derivation was previously described (2). To generate SARTPP individual probabilities, two authors entered each patient's variables into the calculator available at https://www.sartcorsonline.com/Predictor/Patient. Model performance was analyzed with SAS 9.4 and GraphPad Prism 7.03.RESULTS: Live birth resulted in 229 of 498 cycles (46.0%). Predicted individual probabilities were higher for the Luke model (2.5-59.5%, median 49.6%) than the SARTPP (3-52%, median 43%). Discrimination, as measured by the area under the ROC curve (AUC), was greater for the SARTPP, 0.628 (95% CI 0.580-0.677), than the Luke model, 0.618 (95% CI 0.569-0.667). The Luke model showed excellent calibration with a Hosmer-Lemeshow p-value of 0.99, while the SARTPP p-value of 0.11 was closer to rejecting the null hypothesis: that a straight line fits well. Table 1 shows the models' differences in expected and observed live births, the SARTPP underestimated outcomes in 6/10 deciles by more than 5%. The net reclassification index was -2.1% for the SARTPP compared to the Luke model. CONCLUSIONS: Use of the SARTPP is better informed by an understanding of its discrimination (proportion of outcome pairs where live birth was assigned higher prediction) and calibration (agreement between expected and observed outcomes of deciles). Similar to other validated IVF prediction models, both studied models have modest discrimination, but the SARTPP has a slightly higher AUC. This improved discrimination comes at the expense of inferior calibration, and calibration is clinically more relevant (3). In our external validation, the SARTPP underestimated IVF success and provided no gain in predictive performance according to net reclassification. We therefore favor the Luke model.References: 1. Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.