Author + information
- Received September 30, 2017
- Revision received October 18, 2017
- Accepted October 19, 2017
- Published online January 1, 2018.
- Waqas T. Qureshi, MDa,
- Zhu-Ming Zhang, MD, MPHb,
- Patricia P. Chang, MD, MHSc,
- Wayne D. Rosamond, PhDd,
- Dalane W. Kitzman, MDa,
- Lynne E. Wagenknecht, DrPHe and
- Elsayed Z. Soliman, MD, MSc, MSa,b,∗ ()
- aDepartment of Internal Medicine, Section on Cardiology, Wake Forest School of Medicine, Winston Salem, North Carolina
- bEpidemiological Cardiology Research Center, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- cDivision of Cardiology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- dDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- eDivision of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
- ↵∗Address for correspondence:
Dr. Elsayed Z. Soliman, Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Medical Center Boulevard, Winston Salem, North Carolina 27157.
Background Although silent myocardial infarction (SMI) accounts for about one-half of the total number of myocardial infarctions (MIs), the risk of heart failure (HF) among patients with SMI is not well established.
Objectives The purpose of this study was to examine the association of SMI and clinically manifested myocardial infarction (CMI) with HF, as compared with patients with no MI.
Methods This analysis included 9,243 participants from the ARIC (Atherosclerosis Risk In Communities) study who were free of cardiovascular disease at baseline (ARIC visit 1: 1987 to 1989). SMI was defined as electrocardiographic evidence of MI without CMI after the baseline until ARIC visit 4 (1996 to 1998). HF events were ascertained starting from ARIC visit 4 until 2010 in individuals free of HF before that visit.
Results Between ARIC visits 1 and 4, 305 SMIs and 331 CMIs occurred. After ARIC visit 4 and during a median follow-up of 13.0 years, 976 HF events occurred. The incidence rate of HF was higher in both CMI and SMI participants than in those without MI (incidence rates per 1,000 person-years were 30.4, 16.2, and 7.8, respectively; p < 0.001). In a model adjusted for demographics and HF risk factors, both SMI (hazard ratio [HR]: 1.35; 95% confidence interval [CI]: 1.02 to 1.78) and CMI (HR: 2.85; 95% CI: 2.31 to 3.51) were associated with increased risk of HF compared with no MI. These associations were consistent in subgroups of participants stratified by several HF risk predictors. However, the risk of HF associated with SMI was stronger in those younger than the median age (53 years) (HR: 1.66; 95% CI: 1.00 to 2.75 vs. HR: 1.19; 95% CI: 0.85 to 1.66, respectively; overall interaction p by MI type <0.001).
Conclusions SMI is associated with an increased risk of HF. Future research is needed to examine the cost effectiveness of screening for SMI as part of HF risk assessment, and to identify preventive therapies to improve the risk of HF among patients with SMI.
Heart failure (HF) is the final outcome of up to 15% of the patients who experience acute myocardial infarction (MI) (1–4). The proportion of this segment of the population is likely to increase, as the survival of post-MI patients has significantly improved over the last decade (5). Up to one-third of the 1 million patients who are hospitalized for HF each year in the United States have a history of MI (6). Several factors, such as recurrent MI, ventricular remodeling, mechanical MI complications, and stunned or hibernating myocardium, lead to HF post-MI (7,8). These conditions might be clinically silent and can go unnoticed for a long time.
Silent myocardial infarction (SMI), defined as evidence of MI on the electrocardiogram (ECG) in the absence of history of MI, accounts for about one-half of the total number of MIs (9). Previous reports from different populations have shown that both clinical myocardial infarction (CMI) and SMI are associated with poor prognosis (9,10). However, whether SMI is associated with HF similar to CMI is currently unclear. Furthermore, HF prevalence varies by sex and race, and hence, it is possible that sex and race modify the relationship between SMI and HF (11,12). Therefore, the aims of this study were to examine and compare the associations of SMI and CMI with HF versus those with no MI, and to examine the consistency of these associations in subgroups stratified by sex and race as well as HF risk factors.
Study design and population
The ARIC (Atherosclerosis Risk In Communities) study is a community-based, predominantly biracial prospective cohort study that was designed to study atherosclerosis, its clinical outcomes, and variation in cardiovascular risk factors, medical care, and disease by race, sex, location, and date. Details of the ARIC study have been previously published (13). Briefly, from 1987 to 1989 (ARIC visit 1, baseline), 15,792 adults (age 45 to 64 years) from 4 U.S. communities (Washington County, Maryland; suburbs of Minneapolis, Minnesota; Jackson, Missouri; and Forsyth County, North Carolina) were prospectively enrolled in the ARIC study. They underwent a phone interview and subsequent clinic visit. Additional examinations were performed in 1990 to 1992 (visit 2), 1993 to 1995 (visit 3), 1996 to 1998 (visit 4), and 2011 to 2013 (visit 5). Participants were mostly white in the Washington County and Minneapolis sites, exclusively black in Jackson, and a mix of both in Forsyth County. The study was approved by the institutional review board at each study site. All participants provided written informed consent.
For the purpose of this analysis, all ARIC participants with good quality and complete ECG data at visits 1 through 4 as well as outcome events after visit 4 were considered. The following participants were excluded: 47 with reported race neither African-American nor white; 565 participants with ECG data that were not interpretable for the diagnosis of MI due to poor quality or suppression codes by the Minnesota ECG classification; 3,775 with missing ECG in any of the ARIC first 4 visits, including those who died during this period; 201 with missing baseline cardiovascular disease (CVD) risk factors utilized in the models; and 119 missing HF follow-up data. We also excluded 1,706 participants with a history of prevalent CVD at baseline, which was defined as the presence of ECG evidence of MI or a self-reported history of physician-diagnosed MI, coronary artery bypass surgery, coronary angioplasty, HF, or stroke. Finally, we excluded 136 cases with HF occurring between ARIC visits 1 and 4. After all exclusions (n = 6,549), a total of 9,243 participants remained and were included in the analysis.
Baseline (visit 1) age, sex, race, and smoking status were determined by self-report. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Blood samples were obtained after a 12-h fast and were examined in a central laboratory. Diabetes mellitus was defined as a fasting glucose level ≥126 mg/dl (or nonfasting glucose ≥200 mg/dl), a self-reported physician diagnosis of diabetes mellitus, or the use of antidiabetes medications. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or the use of blood pressure–lowering medications. Medication use was obtained by self-report of medication intake during the last 2 weeks and by a review of medications brought by the participants to their visit. Each medication was coded by trained and certified interviewers with the use of a computerized medication classification system. Heart rate data were obtained from the baseline ECG.
SMI and CMI
SMI was defined as ECG evidence of new MI at ARIC visit 2, 3, or 4 that was not present at the baseline visit (visit 1) in the absence of documented CMI. CMI was adjudicated by physician review based on chest pain, cardiac biomarkers/enzymes from hospitalizations, ECG evidence including a new pathological Q-wave, coronary heart disease history, and other associated information. All hospitalized events were classified into definite, probable, suspect, and no MI. Details of classification and specific criteria for adjudication have been described previously (14). Definite and probable MIs were combined to define CMI in this analysis. Definite hospitalized CMI met ≥1 of the following criteria: evolving diagnostic ECG pattern, diagnostic ECG pattern and abnormal enzymes, or cardiac pain and abnormal enzymes plus evolving ST-T pattern or equivocal ECG pattern. Probable hospitalized MI met ≥1 of the following criteria in the absence of sufficient evidence for definite hospitalized MI: cardiac pain and abnormal enzymes, cardiac pain and equivocal enzymes and either evolving ST-T pattern or diagnostic ECG pattern, or abnormal enzymes and evolving ST-T pattern (14). Participants with both SMI and CMI between ARIC visits 1 and 4 were considered to have CMI.
Resting 10-s standard simultaneous 12-lead ECGs were performed in all participants using identical electrocardiograph machines (MAC PC, Marquette Electronics Inc., Milwaukee, Wisconsin) at all clinical sites by trained personnel. These ECGs were processed in a central ECG laboratory (initially at Dalhousie University, Halifax, Nova Scotia, Canada, and later at the Epidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston-Salem, North Carolina), where all ECGs were visually inspected for quality and technical errors. ECG evidence of MI was defined using the Minnesota Code (MC) ECG classifications as new appearance of a major Q/QS wave abnormality (MC 1.1 or 1.2) or minor Q/QS wave abnormality (MC 1.3) plus major ST-T abnormality (MC 4.1, 4.2, 5.1, or 5.2) (15,16).
Ascertainment of heart failure
Incident HF was defined as the first occurrence of HF hospitalization according to the International Classification of Diseases-9th Revision (ICD-9) code 428 (428.0 to 428.9) as a diagnosis in any position. These diagnostic codes were obtained during retrospective surveillance of hospital discharges, death certificate with death from HF in any position, or death certificate with an ICD-9 code of 428 or an ICD-10 code of I-50 among any of the diagnoses listed or underlying causes of death on the death certificate (17).
Baseline characteristics were compared by MI status (CMI, SMI, and no MI). Statistical significance for categorical variables was tested using the chi-square method and the analysis of variance or Student's t-test for continuous variables.
Cumulative incidence rates of HF per 1,000 person-years occurring after visit 4 were calculated among ARIC participants who had SMI and CMI (vs. no MI) that occurred between visits 1 and 4. Kaplan-Meier estimates were used to compute the cumulative incidence of HF stratified by the MI status, and the difference in estimates was compared using the log-rank procedure. Follow-up time was defined as the time from visit 4 to the diagnosis of HF, death, loss to follow-up, or end of follow-up (December 31, 2010).
After testing for proportional hazard assumptions, Cox proportional hazard analysis was used to examine the association of CMI and SMI (vs. no MI) with HF in models adjusted as follows: model 1 adjusted for demographics (age, sex, and race); and model 2 adjusted for variables in model 1 plus the clinical components of the ARIC study HF risk score (BMI, smoking status, heart rate, systolic blood pressure, use of blood pressure lowering medications, and diabetes mellitus) (18). Because we excluded participants with coronary heart disease, we did not adjust for coronary heart disease, although it is a component of the ARIC HF risk score (18). Individuals were censored at the time of HF, death, or December 31, 2010, whichever occurred earlier.
Subgroup analysis in the study participants stratified by the clinical components of the ARIC study HF risk score were also examined in models adjusted in a similar fashion to model 2, and the p values for interaction were calculated in each subgroup. For the purpose of subgroup analysis, we used hypertension to replace systolic blood pressure and use of blood pressure lowering medications. We also used the median age (53 years), instead of 65 years, which is suggested by the ARIC HF risk score, because of the small number of participants with SMI above the age of 65 years. On the other hand, we used the BMI of 25 kg/m2 and heart rate of 60 beats/min as cut-off points, as suggested by the ARIC HF risk score (18). All analyses were performed with SAS version 9.3 (SAS Institute Inc., Cary, North Carolina). A 2-sided p < 0.05 for main effects and interactions was considered significant.
Overall, 9,243 (mean age 53.7 ± 5.7 years, 57.2% women, 20.4% black) participants were included in the analysis. Table 1 shows the baseline characteristics stratified by MI status. Compared with the CMI group, the SMI group had more women, blacks, and nonsmokers. CMI and SMI had expectedly higher prevalence of coronary heart disease risk factors than the no-MI group.
During a median follow-up of 13.0 years (interquartile range: 12.2 to 13.9 years), there were 976 cases of HF: 104 HF cases among CMI group, 54 among SMI group, and 818 among the no-MI group. The incidence rate of HF was higher in both CMI and SMI than in those without MI (incidence rates per 1,000 person-years were 30.4, 16.2, and 7.8, respectively; p < 0.001). The Central Illustration shows the cumulative incidence of HF stratified by MI status.
In multivariable-adjusted Cox proportional hazard models, both CMI and SMI, compared with no MI, were significantly associated with HF, independent of demographics and clinical risk factors (Table 2). However, the magnitude of risk of HF associated with CMI was larger than the risk associated with SMI.
Figure 1 and Online Table 1 show subgroup analyses stratified by demographics and HF risk factors. As shown, the pattern of associations between MI status and HF was consistent among these subgroups, that is, there was no effect modification of race, diabetes, hypertension, or heart rate on the association between MI by type and HF. However, there was an effect modification by age: the risk of HF associated with SMI was stronger in younger patients than in those at or older than the median age (overall interaction p by MI type <0.001). Also, the risk of HF associated with SMI was slightly stronger for women compared with men (overall interaction p by MI type = 0.093), overweight compared with normal weight (overall interaction p by MI type = 0.093), and in never smokers compared with current and former smokers (overall interaction p by MI type = 0.076).
In this analysis from the ARIC study, we showed that SMI is associated with an increased risk of HF independent of HF risk factors (Central Illustration). CMI also was associated with HF, and its association was stronger than that of SMI. HF has been defined as global pandemic, because it affects around 26 million people worldwide (19). Currently, 5.7 million people in the United States have HF, and it is expected that by 2030, >8 million people will have this condition (20). Therefore, identifying a new potential mechanism contributing to this pandemic is of enormous importance. Although future research is needed to examine the cost effectiveness of screening for SMI as part of HF risk assessment, we believe that our report provides novel insights into an overlooked and potentially addressable contributor to the HF pandemic.
SMI was first described in 1949 and was further characterized in the Framingham Heart Study in 1959 (21,22). Its prevalence in the general population ranges from 0.3% to 4.8% (23–29). Certain subgroups, such as the elderly, persons with diabetes, and women, are known to have a higher prevalence of up to 15% (25,30,31).These subgroups are also uniquely at higher risk of adverse events. Among individuals with MI, SMI constitutes up to one-half of the total number of MIs (30,32), and is associated with increased risk of reinfarction (30), other coronary heart disease, sudden cardiac death, and all-cause mortality (24). To our knowledge, this study is the first to provide evidence that SMI is associated with increased risk of HF as well. Our subgroup analysis shows that the risk of HF associated with SMI is stronger with young age, although they may be less exposed to SMI. Other subgroups of interest that showed borderline effect modification include overweight patients: there was a stronger association of SMI with HF in overweight than in those with normal weight. This could probably be explained by the added risk of obesity to HF. Also, there was a borderline effect modification by smoking, where the association was stronger in never smokers than in current smokers, and was in-between in former smokers. Whether this is due to survival bias or a by-chance finding requires further investigation.
Even though men have higher risk of HF than women, the association of different types of MI with HF did not significantly differ by sex in our study. In fact, there was tendency for women to have more HF risk associated with SMI than men (overall interaction p by MI type = 0.093). In the PREVEND (Prevention of REnal and Vascular END-stage Disease) study, men were more likely to develop HF than women, but women had a higher risk of heart failure with preserved ejection fraction (HFpEF) (33). This finding, and the notion that coronary heart disease (of which SMI is part) is an established risk factor for HFpEF (34,35), suggest that examining HF types along with MI types may shed more light on whether effect modification by sex on the association between SMI and HF exists. Similarly, Hebert et al. (36) showed that black patients with HFpEF tend to have a lower prevalence of ECG-based MI than whites, although blacks are at a higher risk of HF overall. This may partially explain the nonsignificant slightly stronger association between SMI and HF in whites than blacks in our analysis. Further studies examining sex and race differences in the association between MI (clinical and silent) with different types of HF (HFpEF and HFrEF) are needed to examine the effect modification of race and sex on the association of SMI with HF.
Not surprisingly, CMI has a stronger association with HF than SMI. ECG changes reflecting ischemic cascade in the myocardium could precede clinical symptoms (37). Therefore, it is possible that SMI represents milder or earlier changes prior to the development of CMI or HF. Also, it is possible that CMI patients might have a larger infarct size than SMI, and thus, SMI led to a smaller degree of insult to the myocardium. That is to say, MI that remains silent is likely small in size or subclinical, and hence, does not affect the myocardium as significantly as CMI, which could explain the stronger risk of HF with CMI than SMI.
Recently, increased life expectancy and better care of post-MI patients in the United States resulted in an upsurge of HF (6). Early detection of HF prior to overt physiological and structural changes may lead to better outcomes (38). Hence, early detection of risk factors has the potential to minimize the burden of HF-related mortality, morbidity, and health care costs. In this regard, our study provides evidence for a new risk factor that may be otherwise missed in routine care. Because ECG is a readily available tool with high inter-rater reliability, SMI could be identified with ease as a subclinical risk factor. Although guideline-directed therapy has a clear role in preventing future HF among CMI patients and is therefore part of a quality-of-care core measure (39,40), it is not known whether such benefit exists for persons with SMI. Thus, SMI could be considered as a condition for American College of Cardiology/American Heart Association Stage A HF (i.e., at risk for HF) (41). Future studies are needed to study the beneficial effects of screening for SMI and whether guideline-directed therapy will improve outcomes in SMI patients in the same way as CMI patients.
Study limitations and strengths
As in other studies with similar design, residual confounding despite adjustment of several confounders remains a possibility. The inability to detect significant interactions in the subgroup analyses could be related to a lack of power due to the small sample size within the subgroups. Also, including only whites and blacks limits the generalizability of our study to other races/ethnicities. Because the diagnosis of CMI is not reliant on high-sensitivity troponin assays, it is possible that the prevalence of CMI was underestimated in ARIC. However, troponin was not available until 1998, the date our ascertainment of SMI ended. Finally, HF was based on hospitalized cases using ICD-9 and -10 codes and was not validated by physician review for diagnosis of HF, which might have led to misclassification and underestimation of the true incidence of HF; however, the use of inpatient HF events has a high diagnostic specificity in the ARIC study, as shown previously (17).
Strengths of the study include a biracial population with good representation of women, long-term follow-up, and well-ascertained variables and outcomes, including ECG data evaluated at a central reading center.
This is the first report from a large, community-based study showing a link between SMI and HF, which provides an opportunity to identify a new risk factor contributing to a strongly emerging pandemic.
COMPETENCY IN MEDICAL KNOWLEDGE: HF is a frequent complication of MI. About one-half of all MIs are initially clinically silent MI, but these events are associated with an increased risk of HF during long-term follow-up, independent of other clinical predictors of HF.
TRANSLATIONAL OUTLOOK: Future studies should examine the cost-effectiveness of screening for silent MI as part of HF risk assessment and preventive therapies that reduce the risk of HF among patients with silent MI detected by ECG.
The authors thank the staff and participants of the ARIC study for their important contributions.
The ARIC study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Dr. Qureshi has served as a consultant for Medicure; and has traded Medtronic stock shares in the past 12 months (currently does not own any shares). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- body mass index
- clinically manifested myocardial infarction
- heart failure
- heart failure with preserved ejection fraction
- heart failure with reduced ejection fraction
- left ventricular hypertrophy
- silent myocardial infarction
- Received September 30, 2017.
- Revision received October 18, 2017.
- Accepted October 19, 2017.
- 2018 American College of Cardiology Foundation
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