Incorporating prior knowledge into regularized regression.

Motivation: Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation.

Results: We propose a new penalized regression approach that allows a priori integration of external meta-features.

Preoperative hemoglobin A1c levels and increased risk of adverse limb events in diabetic patients undergoing infrainguinal lower extremity bypass surgery in the Vascular Quality Initiative.

Background: The impact of preoperative glycemic control on the risk of adverse perioperative outcomes in diabetic patients undergoing lower extremity bypass (LEB) surgery is not well-understood. We determined whether higher preoperative hemoglobin A1c (HbA1c) levels are associated with an increased risk of major adverse limb events, major adverse cardiovascular events, and mortality in diabetic patients undergoing infrainguinal LEB.

Methods: A retrospective review of all infrainguinal LEB surgeries in the Vascular Quality Initiative registry from January 2012 to February 2017 was performed.

Developmental trajectories for young children with 16p11.2 copy number variation.

Copy number variation at 16p11.2 is associated with diverse phenotypes but little is known about the early developmental trajectories and emergence of the phenotype. This longitudinal study followed 56 children with the 16p11.