Joan Pou Bordoy , Alfonso Leiva , Maria José Albendín Ariza , Roberto Elosúa Llanos , Fernando Rigo Carratalà , Dora Romaguera , Jordi Salas-Salvadó , Nancy Babio , Miguel Angel Martinez-González , Estefanía Toledo , Montserrat Fitó , Fernando Aros , Ramon Estruch , Miquel Fiol Sala
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引用次数: 0
Abstract
Introduction
Major electrocardiogram abnormalities (MECG) are common in middle-aged and older individuals and could be an important factor in predicting cardiovascular events.
Objective
To analyse the association between MECG (Minnesota classification) and CVE independently of classic cardiovascular risk factors (CVRF), and to assess whether they improve the prediction according to the Spanish Coronary Event Risk Function (FRESCO).
Method
1752 participants included in three nodes of the PREDIMED study aged between 55 and 80 years with medium or high CVRF. Mean follow-up time was 5.1 years. Cumulative CVE incidence was estimated by sex with and without MECG, and hazard ratios by sex were estimated using multivariate Cox regressions adjusted for randomization group and CCRF (FRESCO). Harrel’s C Indices, Nam d’Agostino, Net Reclassification Improvement, and Integrated Discrimination Improvement were calculated.
Results
At baseline, 25% of the participants shows major electrocardiogram abnormalities (AMECG). During follow-up, there were 112 cardiovascular events (16 cardiovascular deaths, 15 acute myocardial infarctions, 38 anginas, 43 strokes). MECG were significantly associated with the onset of CVE. In men, left ventricular hypertrophy (LVH) criteria were associated with T-wave inversion (HR: 17.88, 95% CI: 5.51−58.03, p < 0.001) and QT interval prolongation (HR: 2.41, 95% CI: 1.38−4.21, p = 0.002); in women, atrial fibrillation (HR: 5.7, 95% CI: 1.76−18.72, p = 0.006) and ST-segment depression (HR: 3.24, 95% CI: 1.36−7.71, p < 0.001) were associated. No significant improvement in MECG prediction compared to FRESCO was observed.
Conclusions
MECG are independently associated with the occurrence of CVE, but do not improve risk prediction beyond traditional risk factors.