Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

PURPOSE OF REVIEW: Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. RECENT FINDINGS: Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Diastolic Dysfunction Assessed Using Contemporary Guidelines and Prognosis Following Myocardial Infarction.

BACKGROUND: Recent American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines for the assessment of diastolic dysfunction (DD) recommend a simplified approach with four key variables incorporated into a novel diagnostic algorithm. The aim of this study was to assess the prognostic value of significant DD assessed using the algorithm recommended in the 2016 American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines (DD2016) in comparison with the prognostic value of significant DD assessed using the 2009 guidelines (DD2009) as well as the individual parameters incorporated in the 2016 algorithm. METHODS: Retrospective data on 419 consecutive patients with first ever myocardial infarction were included. Doppler echocardiography was performed within 24 hours of admission in all patients. Significant DD was defined as grade 2 or 3 DD. The primary outcome measure was composite major adverse cardiovascular events (MACEs), comprising death, myocardial infarction, and heart failure. RESULTS: At a median follow-up of 24 months, there were 61 MACEs. On Kaplan-Meier analysis, DD2016 showed a better association with MACEs than DD2009 (log-rank χ2 = 21.01 [P < .001] vs 13.13 [P = .001]). On Cox proportional-hazards multivariate analysis incorporating significant clinical predictors and left ventricular ejection fraction, DD2016 (hazard ratio, 2.22; 95% CI, 1.25-3.98; P = .007) was the strongest independent predictor of MACEs, whereas DD2009 (hazard ratio, 1.63; 95% CI, 0.95-2.80; P = .074) was not a significant predictor. Of the four key diastolic parameters, only left atrial volume index was independently associated with MACEs (hazard ratio, 1.79; 95% CI, 1.02-3.14; P = .041) when included in a Cox proportional-hazards multivariate model incorporating significant clinical predictors and left ventricular ejection fraction, although the association was weaker than DD2016. Intermodel comparisons with model χ2 and Harrell's C statistic were satisfactory for DD2016. CONCLUSIONS: Significant DD assessed using the 2016 American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines is a robust independent predictor of clinical outcomes following myocardial infarction and compares favorably with DD2009 as well as the individual parameters incorporated in the novel 2016 algorithm.