Dynamic Edematous Response of the Human Heart to Myocardial Infarction: Implications for Assessing Myocardial Area at Risk and Salvage.

BACKGROUND: Clinical protocols aimed to characterize the post-myocardial infarction (MI) heart by cardiac magnetic resonance (CMR) need to be standardized to take account of dynamic biological phenomena evolving early after the index ischemic event. Here, we evaluated the time course of edema reaction in patients with ST-segment-elevation MI by CMR and assessed its implications for myocardium-at-risk (MaR) quantification both in patients and in a large-animal model. METHODS: A total of 16 patients with anterior ST-segment-elevation MI successfully treated by primary angioplasty and 16 matched controls were prospectively recruited. In total, 94 clinical CMR examinations were performed: patients with ST-segment-elevation MI were serially scanned (within the first 3 hours after reperfusion and at 1, 4, 7, and 40 days), and controls were scanned only once. T2 relaxation time in the myocardium (T2 mapping) and the extent of edema on T2-weighted short-tau triple inversion-recovery (ie, CMR-MaR) were evaluated at all time points. In the experimental study, 20 pigs underwent 40-minute ischemia/reperfusion followed by serial CMR examinations at 120 minutes and 1, 4, and 7 days after reperfusion. Reference MaR was assessed by contrast-multidetector computed tomography during the index coronary occlusion. Generalized linear mixed models were used to take account of repeated measurements. RESULTS: In humans, T2 relaxation time in the ischemic myocardium declines significantly from early after reperfusion to 24 hours, and then increases up to day 4, reaching a plateau from which it decreases from day 7. Consequently, edema extent measured by T2-weighted short-tau triple inversion-recovery (CMR-MaR) varied with the timing of the CMR examination. These findings were confirmed in the experimental model by showing that only CMR-MaR values for day 4 and day 7 postreperfusion, coinciding with the deferred edema wave, were similar to values measured by reference contrast-multidetector computed tomography. CONCLUSIONS: Post-MI edema in patients follows a bimodal pattern that affects CMR estimates of MaR. Dynamic changes in post-ST-segment-elevation MI edema highlight the need for standardization of CMR timing to retrospectively delineate MaR and quantify myocardial salvage. According to the present clinical and experimental data, a time window between days 4 and 7 post-MI seems a good compromise solution for standardization. Further studies are needed to study the effect of other factors on these variables.

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.

INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. METHODS AND ANALYSIS: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. ETHICS AND DISSEMINATION: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease. For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created and will be used as a regional policy to establish effective public health programmes to fight heart disease. At least 10 publications in the first-quartile scientific journals are planned. TRIAL REGISTRATION NUMBER: NCT03429452.