Preclinical Aortic Atherosclerosis in Adolescents With Chronic Disease.

Background Adolescents with chronic disease are often exposed to inflammatory, metabolic, and hemodynamic risk factors for early atherosclerosis. Since postmortem studies have shown that atherogenesis starts in the aorta, the CDACD (Cardiovascular Disease in Adolescents with Chronic Disease) study investigated preclinical aortic atherosclerosis in these adolescents. Methods and Results The cross-sectional CDACD study enrolled 114 adolescents 12 to 18 years old with chronic disorders including juvenile idiopathic arthritis, cystic fibrosis, obesity, corrected coarctation of the aorta, and healthy controls with a corrected atrial septal defect.

Application of Deep Neural Network Models in Drug Discovery Programs.

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results.

Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data.

Background: Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice.

Objective: This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center.

Methods: We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht.

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.

Motivation: Selecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographic diagnoses of coronary artery disease (CAD).