Deep Learning for Cardiovascular Risk Stratification

Ambulatory blood pressure for cardiovascular risk stratification.

Ambulatory blood pressure (ABP) monitoring is increasingly recognized as a valuable tool to refine prediction of cardiovascular risk related to blood pressure (BP).1 After the first landmark study published by Perloff and colleagues 24 years ago,2 several longitudinal event-based studies provided unequivocal evidence of an independent association between ABP and risk of cardiovascular disease. ...

Evaluating new cardiovascular risk factors for risk stratification.

Cardiovascular risk stratification in familial hypercholesterolaemia

Familial hypercholesterolaemia (FH) is a common autosomal-dominant disorder in most European countries. Patients with FH are characterised by a raised level of low-density lipoprotein cholesterol and a high risk of premature coronary heart disease (CHD). Currently there is no consensus regarding the clinical utility to predict future coronary events or testing for the presence of subclinical at...

Cardiovascular Risk Stratification: From Phenotype to Genotype?

DOI: 10.5935/abc.20180010 Cardiovascular risk scores, such as the Framingham score, have been strongly recommended by clinical guidelines on the assessment of cardiovascular risk.1 However, several studies have shown limitations for their use,2,3 particularly in patients at intermediate risk, young patients with a definite family history, and women. Among different tools aimed at improving risk...

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related network...