Under highly controlled circumstances across 7 years of data from a single institution, using a uniform uterine preparation, following a precise transfer technique with high-quality day 5-6 slow frozen-thawed blastocysts, a BMI in the overweight range of 25-29.9 kg/m is not associated with a poorer implantation rate or live-birth rate, nor is it associated with an increased risk of miscarriage when compared with a normal BMI range. The increased length of time required during transfer for women with higher BMI suggests body habitus may contribute to difficult transfers, although this may not translate into poorer implantation rates. By using a standardized protocol for slow freezing and thawing of embryos, using identical hormonal preparation and a uniform ET protocol, a homogenous uterine environment was created in this carefully selected cohort of women, thereby minimizing confounders and uniquely highlighting the neutral effect of overweight BMI on implantation rate.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

Background: The Medicaid Analytic eXtract (MAX) is a health care utilization database from publicly insured individuals that has been used for studies of drug safety in pregnancy. Claims-based algorithms for defining many important maternal and neonatal outcomes have not been validated.Objective: To validate claims-based algorithms for identifying selected pregnancy outcomes in MAX using hospital medical records. Methods:The medical records of mothers who delivered between 2000 and 2010 within a single large healthcare system were linked to their claims in MAX. Claimsbased algorithms for placental abruption, preeclampsia, postpartum hemorrhage, small for gestational age, and noncardiac congenital malformation were defined. Fifty randomly sampled cases for each outcome identified using these algorithms were selected, and their medical records were independently reviewed by two physicians to confirm the presence of the diagnosis of interest; disagreements were resolved by a third physician reviewer. Positive predictive values (PPVs) and 95% confidence intervals (CIs) of the claims-based algorithms were calculated using medical records as the gold standard.Results: The linked cohort included 10,899 live-birth pregnancies. The PPV was 92% (95% CI, 82%-97%) for placental abruption, 82% (95% CI, 70%-91%) for preeclampsia, 74% (95% CI, 61%-85%) for postpartum hemorrhage, 92% (95% CI, 82%-97%) for small for gestational age, and 86% (95% CI, 74%-94%) for noncardiac congenital malformation.Conclusions: Across the perinatal outcomes considered, PPVs ranged between 74% and 92%. These PPVs can inform bias analyses that correct for outcome misclassification. K E Y W O R D S databases, pharmacoepidemiology, pregnancy, validation