AIMC Topic: Heart Disease Risk Factors

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Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nature communications
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment....

Ischemia and outcome prediction by cardiac CT based machine learning.

The international journal of cardiovascular imaging
Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for card...

From CT to artificial intelligence for complex assessment of plaque-associated risk.

The international journal of cardiovascular imaging
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust inform...

Leisure time physical activity is associated with improved HDL functionality in high cardiovascular risk individuals: a cohort study.

European journal of preventive cardiology
AIMS: Physical activity has consistently been shown to improve cardiovascular health and high-density lipoprotein-cholesterol levels. However, only small and heterogeneous studies have investigated the effect of exercise on high-density lipoprotein f...

Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study.

European journal of preventive cardiology
AIMS: Evidence is limited as to who benefit the most from sodium-glucose cotransporter 2 inhibitors (SGLT2i), especially among people without elevated cardiovascular disease (CVD) risk. To address this knowledge gap, we investigated the heterogeneity...

Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003-2016.

European journal of preventive cardiology
AIMS: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases...

How to measure and model cardiovascular aging.

Cardiovascular research
Most acquired cardiovascular diseases are more common in older people, and the biological mechanisms and manifestations of aging provide insight into cardiovascular pathophysiology. Measuring aging within the cardiovascular system may help to better ...

Development of a machine learning model for predicting cardiovascular risk factors and major adverse cardiovascular events in young adults with glucose metabolism disorders.

Medicina clinica
BACKGROUND: Glucose metabolism disorders (GMDs) are a serious global public health issue, characterized by a high incidence rate and youthfulness. GMDs contribute to the occurrence of major adverse cardiovascular events (MACEs), meanwhile with the re...