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Original Articles • Arq. Bras. Cardiol. 104 (2) • Feb 2015 • https://doi.org/10.5935/abc.20140177 linkcopy

Open-access Assessment of Myocardial Infarction by Cardiac Magnetic Resonance Imaging and Long-Term Mortality

AuthorshipSCIMAGO INSTITUTIONS RANKINGS

    Abstracts

    Background:  Cardiac magnetic resonance imaging provides detailed anatomical information on infarction. However, few studies have investigated the association of these data with mortality after acute myocardial infarction.

    Objective:  To study the association between data regarding infarct size and anatomy, as obtained from cardiac magnetic resonance imaging after acute myocardial infarction, and long-term mortality.

    Methods:  A total of 1959 reports of “infarct size” were identified in 7119 cardiac magnetic resonance imaging studies, of which 420 had clinical and laboratory confirmation of previous myocardial infarction. The variables studied were the classic risk factors – left ventricular ejection fraction, categorized ventricular function, and location of acute myocardial infarction. Infarct size and acute myocardial infarction extent and transmurality were analyzed alone and together, using the variable named “MET-AMI”. The statistical analysis was carried out using the elastic net regularization, with the Cox model and survival trees.

    Results:  The mean age was 62.3 ± 12 years, and 77.3% were males. During the mean follow-up of 6.4 ± 2.9 years, there were 76 deaths (18.1%). Serum creatinine, diabetes mellitus and previous myocardial infarction were independently associated with mortality. Age was the main explanatory factor. The cardiac magnetic resonance imaging variables independently associated with mortality were transmurality of acute myocardial infarction (p = 0.047), ventricular dysfunction (p = 0.0005) and infarcted size (p = 0.0005); the latter was the main explanatory variable for ischemic heart disease death. The MET-AMI variable was the most strongly associated with risk of ischemic heart disease death (HR: 16.04; 95%CI: 2.64-97.5; p = 0.003).

    Conclusion:  The anatomical data of infarction, obtained from cardiac magnetic resonance imaging after acute myocardial infarction, were independently associated with long-term mortality, especially for ischemic heart disease death.

    Myocardial Infarction/physiology;; Magnetic Resonance Imaging; Diagnostic Imaging; Mortality; Risk Factor

    Fundamento:  A ressonância magnética cardíaca fornece informações anatômicas detalhadas do infarto, porém poucos estudos investigaram a associação desses dados com mortalidade pós-infarto agudo do miocárdio.

    Objetivo:  Verificar a associação entre os dados de anatomia e magnitude do infarto, obtidos da ressonância magnética cardíaca pós-infarto agudo do miocárdio, e mortalidade em longo prazo.

    Métodos:  Foram identificados 1.959 laudos com “massa infartada” em 7.119 exames de ressonância magnética cardíaca, dos quais 420 possuíam documentação clínica e laboratorial de infarto agudo do miocárdio prévio. As variáveis estudadas foram os fatores de risco clássicos, fração de ejeção do ventrículo esquerdo, função ventricular categorizada e localização do infarto agudo do miocárdio. Massa infartada, extensão e transmuralidade do infarto agudo do miocárdio foram analisadas de maneira isolada e conjuntamente, pela variável denominada “MET-IAM”. A análise estatística foi feita pelo elastic net regularization, pelo modelo de Cox e por árvores de sobrevida.

    Resultados:  A idade média foi 62,3 ± 12 anos, sendo 77,3% de homens. Durante o seguimento de 6,4 ± 2,9 anos, foram identificados 76 óbitos (18,1%). Creatinina sérica, diabetes melito e infarto agudo do miocárdio prévio demonstraram associação independente com mortalidade. A idade foi o principal fator explicativo. As variáveis da ressonância magnética cardíaca que se associaram de forma independente com a mortalidade foram: transmuralidade do infarto agudo do miocárdio (p = 0,047), disfunção ventricular (p = 0,0005) e massa infartada (p = 0,0005) − sendo essa última a principal variável explicativa para morte por doença isquêmica cardíaca. A variável MET-IAM exibiu a maior associação de risco para morte por doença isquêmica cardíaca (HR: 16,04; IC95%: 2,64-97,5; p = 0,003).

    Conclusão:  Os dados anatômicos do infarto obtidos da ressonância magnética cardíaca pós-infarto agudo do miocárdio se associaram de forma independente com mortalidade em longo prazo, sendo, porém, mais relevantes para morte por doença isquêmica cardíaca.

    Infarto do Miocárdio/fisiopatologia;; Imagem por Ressonância Magnética; Diagnóstico por Imagem; Mortalidade; Fator de Risco

    Introduction

    The size and morphology of myocardial fibrosis or necrosis area are potentially associated with the occurrence of ventricular dysfunction and ventricular arrhythmias, which are themselves associated with mortality in patients after acute myocardial infarction (AMI)1,2. The introduction of cardiac magnetic resonance imaging (CMRI) in the assessment of AMI3 was a major advance. It permits thorough documentation of myocardial infarct size and anatomy, improving both the diagnostic ability and the perspective of post-AMI risk assessment. However, some knowledge gaps regarding the association of AMI anatomical data provided by CMRI with long-term mortality still exist, as well as regarding its ability to add relevant prognostic information. The number of studies is limited, and most of them had short follow-up periods and involved North American and European populations 4-12.

    Objective

    To verify the association of data regarding infarct size and anatomy with overall mortality, circulatory system disease mortality, and ischemic heart disease (IHD) mortality in patients undergoing CMRI after AMI.

    Methods

    Study design and population

    Between June 2001 and December 2010, 7119 CMRI records and 1959 reports with the term “infarct size” (IS) were found in a tertiary health care network of imaging laboratories and hospitals. There were 489 patients undergoing CMRI after hospitalization, with documented elevation of troponin levels, of whom 420 were selected for diagnostic confirmation of AMI from their medical records; patients with non-ischemic myocardial injury were excluded. This study was approved by the Research Ethics Committee of University Hospital Clementino Fraga Filho (HUCFF), Federal University of Rio de Janeiro (UFRJ).

    Variables analyzed

    The demographic and clinical variables analyzed were: age; gender; previous AMI; diabetes mellitus; systemic hypertension; smoking habit; dyslipidemia; angiographic classification of obstructive coronary artery disease; treatment with CABG; serum creatinine levels; and peak serum troponin I level. Mortality and the basic cause of death were obtained from probabilistic relationship of identification data of the study population, with 1,485,735 records of the Mortality Information System (Sistema de Informação de Mortalidade -SIM) between 2001 and 2012.

    Cardiac magnetic resonance imaging findings

    CMRI findings were obtained from the medical records of each patient. The resonance techniques routinely used for the assessment of patients with AMI were cine-magnetic resonance, for left ventricular (LV) functional assessment, and late enhancement technique13, for the assessment of myocardial necrosis or fibrosis.

    LV function was obtained from LV ejection fraction (EF) (Simpson’s method) and from the LV function category by subjective visual analysis, and was classified as normal or mild, moderate or severe dysfunction.

    AMI location was based on the 17-segment model used for myocardial segmentation nomenclature14. The presence of transmural AMI was defined by the involvement of > 50% of the segment15,16. The AMI type, regarding transmurality, was thus classified into three categories: transmural (TM), non‑transmural (NTM), and mixed (when both AMI types were present). The percentage of infarcted LV mass was calculated using the semi-quantitative visual score method, as described elsewhere17. The AMI transmurality index (TI) was obtained by calculating the ratio between the number of segments with transmural infarction (SegTM AMI) and the total number of segments with AMI (NSegAMI).

    The MET-AMI variable was elaborated to express AMI size and complexity, combining three categorized primary variables: AMI transmurality, IS, and NSegAMI.

    The definitions of abnormality (negative vs positive) for the variables comprising MET-AMI were: (1) transmurality, if mixed AMI present; (2) categorization of components of the MET-AMI composite variable was obtained from the most accurate cut-off value found in the ROC (Receiver Operating Characteristic) curve, i.e., IS ≥ 14% and NSegAMI ≥ 4.

    The MET-AMI variable was graded according to the number of abnormal components: grade-1 MET-AMI for no abnormality; grade-2 MET-AMI for one abnormal variable; grade-3 MET-AMI for two abnormal variables; grade-4 MET‑AMI for three abnormal variables.

    The presence of microvascular obstruction (no reflow) was documented whenever described in the test report.

    Statistical analysis

    Data analysis was carried out to evaluate the three endpoints: overall mortality, circulatory system disease death, and IHD death, as codified in the Declaration of Death (DD) by the 10th Review of the International Classification of Diseases (ICD-10). Mann-Whitney, chi-square or Fisher’s exact test were used for the univariate analysis; the log rank test was used for the analysis of Kaplan-Meier curves. Multivariate analysis was carried out sequentially by means of an initial selection of variables using elastic net regularization (EM), which were analyzed by the Cox model. The survival tree was used for the identification of the main explanatory variables for each endpoint. The significance level was set at 5%. The Pearson method was used for the analysis of correlations between numeric variables.

    Results

    The mean follow-up period from the day CMRI was performed until the occurrence of death or the end of the observation period was 6.4 ± 2.9 years. The median time between admission for AMI and performance of CMRI was 13 days, and 278 patients (66.1%) underwent the imaging test within the first 30 days after AMI. There were 76 deaths (18.09%), of which 34 (44.7%) were for circulatory system diseases and 22 (29%), for IHD.

    Demographic and clinical data

    Demographic and clinical characteristics of the study population, by subgroup of survivors and of all-cause deaths, are shown in Table 1.

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    Table 1
    Demographic and clinical characteristics of the study population and of the subgroups of survivors and all-cause deaths

    The subgroup of deaths compared to that of survivors showed: higher mean age; higher prevalences of diabetes mellitus, of previous AMI and two-vessel coronary artery disease; higher median peak troponin levels; and higher percentage of cases with serum creatinine levels > 2 mg/dL. A greater number of myocardial revascularization procedures was observed in the subgroup of survivors (73.3% vs. 47.4%). The angiographic profile of single-vessel disease was more frequent among survivors, and only 19.8% did not undergo cardiac catheterization.

    CMRI data in the study population, in the subgroup of survivors and in the all-cause death group are shown in Table 2.

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    Table 2
    Values of variables obtained from cardiac magnetic resonance imaging (CMRI) in the study population, and in the subgroups of survival and all-cause deaths

    Comparing the subgroup of deaths with that of survivors, a higher prevalence of mixed AMI was observed in the group of deaths. Number of segments with TM AMI, total number of segments with AMI, and IS were significantly higher in the subgroup of deaths.

    The Pearson test showed positive linear correlations between IS and SegTM AMI (r = 0.83) and between IS and NSegAMI (r = 0.78). LVEF showed a negative linear association with IS (r = - 0.57).

    Associations of variables with the endpoints studied

    Endpoint all-cause mortality

    The multivariate Cox model was used for the all-cause death endpoint (time elapsed between performance of CMRI and occurrence of death), with the variables selected by EN (Table 3).

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    Table 3
    Cox model for the all-cause mortality endpoint

    Among the CMRI data, the model identified the following independent variables: moderate or severe ventricular dysfunction, IS and NTM AMI. The model also identified the following clinical or demographic variables as independent variables: age, diabetes mellitus and serum creatinine level. An inverse association was also found between treatment with CABG and mortality.

    The MET-AMI variable was tested using the multivariate Cox model again. However, this was proven to be a significant variable for overall mortality (p = 0.25).

    The survival tree identified age at CMRI, with a cut-off point at 69.7 years, as the most relevant explanatory variable for overall mortality.

    Endpoint mortality for circulatory system diseases

    The variables selected by EN for the endpoint mortality for circulatory system diseases were: age, diabetes mellitus, type of treatment, serum creatinine level, IS (%), AMI of anterior location, moderate or severe LV dysfunction, AMI transmurality, previous AMI, AMI-CMDRI time (days), male gender, SegNTM AMI and no reflow.

    The Cox model identified, among the demographic or clinical variables, age (p < 0.0001) and serum creatinine level (p = 0.001) as independent variables. Among CMRI variables, the independent variables identified were ventricular dysfunction (hazard ratio − HR: 3.34; 95% confidence interval − 95% CI: 1.35-8.24; p = 0.008), IS (HR: 1.10; 95%CI: 1.05‑1.15; p = 0.0001) and previous AMI (HR: 2.87; 95%CI: 1.04‑7.89; p = 0.041). The presence of mixed AMI was also a factor identified with significance < 10% (p = 0.055).

    The MET-AMI variable was tested again using the Cox multivariate model. Table 4 shows the independent variables identified in the new model. Moderate/severe LV dysfunction, age, serum creatinine level, and grades 3 and 4 MET-AMI were independent variables related to the endpoint circulatory system disease death.

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    Table 4
    Cox model for the endpoint circulatory disease mortality, with the MET-AMI variable included

    Survival tree for the endpoint circulatory system disease mortality

    The survival tree (Figure 1) identified ventricular function, categorized as moderate or severe dysfunction vs mild dysfunction or normal, as the most relevant explanatory variable for the classification of survivors and of circulatory system disease deaths. The age variable was also relevant in the model.

    Figure 1
    Survival tree for the endpoint circulatory system disease deaths. (1) Normal LV function; (2) Mild LV dysfunction; (3) Moderate LV dysfunction; (4) Severe LV dysfunction. LV: left ventricle.

    Joint assessment of MET-AMI and ventricular function variables

    Figure 2 shows the Kaplan-Meier survival curves for the endpoint circulatory system disease mortality, classified in four groups according to relevant changes of the MET-AMI composite variable (defined by cohort ≥ 3) and of left ventricular function (defined by the presence of moderate or severe ventricular dysfunction).

    Figure 2
    Kaplan-Meier survival curves for the endpoint circulatory system disease mortality, according to the MET-AMI and left ventricular function variables. Group A: dysfunction absent and grades 1 or 2 MET-AMI; Group B: dysfunction absent and grades 3 or 4 MET-AMI; Group C: moderate or severe dysfunction and grades 1 or 2 MET-AMI; Group D: moderate or severe dysfunction and grades 3 or 4 MET-AMI.

    A significant reduction in survival was observed in the group with relevant changes in both variables in comparison to the subgroup with no changes. The subgroups with only one abnormal variable had intermediate survival, however significantly shorter than that of the subgroup with no abnormalities.

    Endpoint ischemic heart disease mortality

    The variables selected by EN for the endpoint IHD mortality were age, diabetes mellitus, serum creatinine level, IS (%), moderate or severe LV dysfunction, AMI transmurality, previous AMI, and SegNTM AMI.

    The Cox model applied to these variables showed that, among CMRI variables, IS was the one that demonstrated independent value (HR: 1.09; 95%CI: 1.03-1.15; p = 0.0015). Among the clinical and demographic variables, independent value was identified for age (HR: 1.08; 95%CI: 1.03-1.13; p = 0.001), serum creatinine level (HR: 2.62; 95%CI: 1.35-5.03; p = 0.004) and previous AMI (HR: 2.87; 95%CI: 1.04‑7.89; p = 0.041).

    Table 5 shows the results of the Cox regression model applied to the MET-AMI variable. The MET-AMI variable showed an increasing risk ratio, according to its grade, showing independent value for grades 3 or 4, and was the variable with the highest risk ratio in the model.

    Thumbnail
    Table 5
    Cox model for the endpoint ischemic heart disease mortality, with inclusion of the MET-AMI variable

    Survival tree

    The survival tree (Figure 3) identified IS as a relevant explanatory variable, with a cut-off point at 21.0% for the endpoint IHD mortality.

    Figure 3
    Survival tree for the endpoint circulatory system disease mortality. IS: infarct size.

    Discussion

    Most of the studies assessing data obtained from CMRI after AMI adopted composite endpoints involving the occurrence of decompensated heart failure, reinfarction, ventricular arrhythmias, and death. Their mean follow-up period was 2 years7,18.

    Kwong et al19 reported a relevant study on the value of ate enhancement documented by CMRI in a population with suspected coronary artery disease, and verified that the presence of subclinical AMI, as detected in CMRI, was the factor more significantly associated with the risk of events and mortality. Differently, the focus of the present study was to evaluate the role of CMRI after clinical documentation of AMI.

    Other studies have evaluated patients with ischemic heart disease in more advanced stages and with longer follow-up periods4,8, however shorter than that of the present study.

    Another differential aspect of this study was the assessment of the basic death cause using DD data. Cheong et al4 similarly obtained mortality data using the social security computed system, however without the definition of death cause.

    The study population showed demographic and clinical characteristics consistent with those found in national studies and registries20-22, as well as in international data23. The mean age of 62 years is similar to that found for patients with AMI of the ACCEPT registry21.

    Bello et al8 investigated the importance of late enhancement provided by CMRI in 100 patients with coronary artery disease in a stable stage and, in corroboration with our findings, observed an independent association of the ventricular function, IS, and diabetes mellitus variables with mortality; also, cigarette smoking was more prevalent in the subgroup of survivors. These findings may be potentially attributed to the high prevalence of more severely ill patients with previous coronary artery disease (49.2% with previous AMI in this study), who had probably quit smoking before the index AMI episode.

    Four clinical variables seemed always significant to predict post-AMI death, whether in ours or in other studies24,25, namely: age, renal dysfunction (as expressed by serum creatinine levels), history of previous AMI, left ventricular dysfunction, and diabetes mellitus.

    The association between the type of AMI and mortality was verified for NTM or mixed AMI, reinforcing the potential anatomical risk for arrhythmic or ischemic phenomena reported for NTM infarction11 or heterogeneous scar26,27.

    IS remained a variable of AMI magnitude, with an independent value in the present study, in corroboration with most of the studies5,6,8,10,11,18. The independent value for ventricular dysfunction was more frequently found in studies that also adopted the endpoint all-cause mortality4,8; however, it was also significant to predict short-term composite cardiovascular events after AMI7,12. This was also verified in the present study, in which age and ventricular function were identified as explanatory variables for the endpoint all-cause mortality.

    The DETERMINE multicenter randomized clinical trial28 evaluated the prophylactic use of implantable cardiac defibrillator in patients after AMI guided by the IS value found on CMRI, adopting a cut-off value of 10% for CMRI in the late phase or 15% for CMRI in the acute phase, based on previous findings29 of the arrhythmogenic risk of an AMI scar.

    In the present study, we observed a 14% cut-off value for IS for the endpoint overall mortality, by the ROC curve; however, for the endpoint IHD mortality, the survival tree identified IS as a relevant variable for a higher cut-off value (21.0%). Isquierdo et al12 studied 440 patients in the acute phase of AMI in a 2-year follow-up. The occurrence of composite endpoints (deaths or severe ventricular arrhythmias) was independently associated with IS; however they occurred predominantly in the presence of ventricular dysfunction. Differently, in the present study, in the subgroup without significant ventricular dysfunction, the presence of ≥ grade-3 MET-AMI was associated with shorter survival. The longer follow-up period and the high risk ratio observed for this variable may justify this finding.

    limitations

    Retrospective study that assessed cases from the partial case series of patients hospitalized for AMI, with a subgroup selected as having a better prognosis, for which CMRI was indicated. A population at different stages after AMI was studied, since CMRI was adopted as the reference.

    Conclusion

    The general findings of this study reinforce the ability of clinical and left ventricular function data to predict the risk after acute myocardial infarction. However, the anatomical data provided by cardiac magnetic resonance imaging regarding acute myocardial infarction showed an independent association with long-term mortality, especially for ischemic heart disease death, and thus can contribute for risk identification in selected cases.

    • Sources of Funding
      This study was partially funded by CAPES.
    • Study Association
      This article is part of the doctoral thesis of the authors João Luiz Fernandes Petriz (doctoral student), Basílio de Bragança Pereira and Nelson Albuquerque de Souza e Silva (counselors) at the Programa de Pós-Graduação em Medicina Cardiologia da Faculdade de Medicina/ Instituto do Coração Edson Saad - Universidade Federal do Rio de Janeiro (UFRJ).

    Acknowledgments

    The authors express your sincere appreciation to following contributors: Ângela Maria Cascão (Secretaria Estadual de Saúde - RJ); Dr. Jorge Moll Filho (Rede D’Or São Luiz); Dr. Fábio Vilas-Boas Pinto (Hospital Espanhol da Bahia); Dr. Carlos Eduardo Rochitte (InCor-USP); Dr. Bernardo Rangel Tura (INC - RJ); Dra. Gláucia M. Moraes de Oliveira e Estat. Rosângela Martins (UFRJ); Eng. Luis Antônio Mendonça (IME); Profa. Maria Lúcia Brandão (Faculdade de Educação - UFF); Dra. Martha Savedra, Dr. Plínio Resende do Carmo Jr, Dra. Andréa C. Parise Fontes, Dra. Mariana M. Canário da Silva, Dra. Daniele Guedes, Dr. André Casarsa Marques, Dr. Rafael Aron Abtibol and Dr. Ricardo G. Gusmão Oliveira (Hospital Barra D’Or-RJ); Dr. Denilson Campos de Albuquerque, Dra. Fernanda Tovar Moll and Dr. Jorge Moll Neto (Instituto D’Or de Ensino e Pesquisa-RJ).

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    Publication Dates

    • Publication in this collection
      21 Nov 2014
    • Date of issue
      Feb 2015

    History

    • Received
      02 Feb 2014
    • Reviewed
      08 Aug 2014
    • Accepted
      11 Aug 2014
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