# cardiovascular risk stratification

A multi-cohort external validation shows that a previously trained generative adversarial network, applied without retraining, can synthesize mitral annular tissue-Doppler velocities and displacement from a standard 12-lead ECG. Among 1,065 hospitalized acute myocardial infarction patients, synthetic e′ independently predicted all-cause mortality beyond the GRACE score; in the post-discharge subgroup it raised discrimination from a C-index of 0.67 to 0.70 and stayed independent of AI-ECG-predicted and measured ejection fraction. In 219,564 outpatients, all three parameters were associated with MI-specific mortality, with MAPSE showing the largest effect.

A multi-cohort external validation shows that a previously trained generative adversarial network, applied without retraining, can synthesize mitral annular tissue-Doppler velocities and displacement from a standard 12-lead ECG. Among 1,065 hospitalized acute myocardial infarction patients, synthetic e′ independently predicted all-cause mortality beyond the GRACE score; in the post-discharge subgroup it raised discrimination from a C-index of 0.67 to 0.70 and stayed independent of AI-ECG-predicted and measured ejection fraction. In 219,564 outpatients, all three parameters were associated with MI-specific mortality, with MAPSE showing the largest effect.

Cardiac MRI reading no longer rests only on ejection fraction and other predefined measurements: a healthy cardiac digital twin generated from each person's age, sex and body habitus yields a deviation score that still improves atrial fibrillation risk stratification among people with preserved ejection fraction.

Cardiac MRI reading no longer rests only on ejection fraction and other predefined measurements: a healthy cardiac digital twin generated from each person's age, sex and body habitus yields a deviation score that still improves atrial fibrillation risk stratification among people with preserved ejection fraction.

This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.

This review systematically examines the anatomy, physiology, and pathophysiological roles of epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT), compares CT, MRI, and echocardiography for quantifying these depots, notes that deep-learning segmentation (with Dice coefficients reported from 0.82 to 0.98) improves accuracy and reproducibility, and summarizes conflicting imaging evidence on EAT's link to cardiovascular outcomes alongside findings that statins, GLP-1 receptor agonists, SGLT2 inhibitors, and lifestyle interventions can reduce EAT volume.

This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.

This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.