Download Prevalence, predisposing factors, and prognosis of clinically

Journal of the American College of Cardiology
© 1999 by the American College of Cardiology
Published by Elsevier Science Inc.
Vol. 35, No. 1, 2000
ISSN 0735-1097/00/$20.00
PII S0735-1097(99)00524-0
Myocardial Infarction
Prevalence, Predisposing Factors,
and Prognosis of Clinically
Unrecognized Myocardial Infarction in the Elderly
Stuart E. Sheifer, MD,* Bernard J. Gersh, MB, CHB, D.PHIL, FACC,† N. David Yanez III, PHD,‡
Philip A. Ades, MD, FACC,§ Gregory L. Burke, MD,㛳 Teri A. Manolio, MD¶
Washington, DC; Rochester, Minnesota; Seattle, Washington; Burlington, Vermont; Winston-Salem, North
Carolina; Bethesda, Maryland
OBJECTIVES
This study was designed to determine the prevalence of unrecognized myocardial infarction
(UMI), as well as risk factors, and to compare prognosis after detection of previously UMI to
that after recognized myocardial infarction (RMI).
BACKGROUND Past studies revealed that a significant proportion of MIs escape recognition, and that
prognosis after such events is poor, but the epidemiology of UMI has not been reassessed in
the contemporary era.
METHODS
The Cardiovascular Health Study (CHS) database, composed of individuals ⱖ65, was
queried for participants who, at entry, demonstrated electrocardiographic evidence of a prior
Q-wave MI, but who lacked a history of this diagnosis. The features and outcomes of this
group were compared to those of individuals with prevalent RMI.
RESULTS
Of 5,888 participants, 901 evidenced a past MI, and 201 (22.3%) were previously
unrecognized. The independent predictors of UMI were the absence of angina and the
absence of congestive heart failure (CHF). Six-year mortality did not significantly differ
between the two groups.
CONCLUSIONS 1) In the elderly, UMI continues to represent a significant proportion of all MIs; 2)
associations with angina and CHF may reflect complex neurological issues, but they also may
represent diagnosis bias; 3) these individuals can otherwise not be distinguished from those
with recognized infarctions; and 4) mortality rates after UMI and RMI are similar. Future
studies should address screening for UMI, risk stratification after detection of previously
UMI, and the role of standard post-MI therapies. (J Am Coll Cardiol 2000;35:119 –26) ©
1999 by the American College of Cardiology
Past cohort studies have demonstrated that 25% to 40% of
myocardial infarctions (MIs) are clinically unrecognized.
Because these infarctions are accompanied by minimal or no
symptoms, they escape detection until ultimately an electrocardiogram (ECG) is performed for screening or other
clinical purposes. The risk associated with unrecognized MI
(UMI) has been demonstrated to be substantial, with a
long-term total mortality as poor or worse than that of
recognized myocardial infarction (RMI) (1–7).
From the *Division of Cardiology, Georgetown University Medical Center,
Washington, DC; †Cardiovascular Diseases and Internal Medicine, Mayo Clinic,
Rochester, Minnesota; ‡Division of Biostatistics, University of Washington, Seattle,
Washington; §Division of Cardiology, University of Vermont, Burlington, Vermont;
㛳Department of Public Health Sciences, Wake-Forest University School of Medicine,
Winston-Salem, North Carolina; and ¶DECA—Cardiovascular Health Study, Bethesda, Maryland. The research reported in this article was supported by contracts
N01-HC-85079 through HC-85085, HC-95103, and HC-35129 from the National
Heart, Lung, and Blood Institute.
Manuscript received March 4, 1999; revised manuscript received July 30, 1999,
accepted October 5, 1999.
Significant limitations exist to our current understanding
of UMI. In each of the prior cohort studies, either all or
most of the follow-up interval occurred before 1990, and
prevalence and prognosis may have changed over time.
These studies included few women and very few elderly.
Also, it remains uncertain whether the predisposing factors
for UMI are simply the traditional risk factors for coronary
atherosclerosis, or whether UMI patients have unique characteristics that distinguish them from those with recognized
events. In large part, this is due to a paucity of multivariate
analyses designed specifically to predict infarct recognition.
Finally, there is insufficient evidence to determine whether
“recognition status” is independently associated with prognosis after MI, especially in older adults.
The Cardiovascular Health Study (CHS) is an ongoing
population-based investigation of elderly men and women
that employs multiple modalities, including serial ECGs, to
identify novel markers of risk for cardiovascular disease. It
therefore provides an opportunity to address several of the
120
Sheifer et al.
Unrecognized MI
Abbreviations and Acronyms
CHF ⫽ congestive heart failure
CHS ⫽ Cardiovascular Health Study
CV
⫽ cardiovascular
DBP ⫽ diastolic blood pressure
FEV1 ⫽ forced expiratory volume in 1 s by pulmonary
function testing
HTN ⫽ hypertension
MI
⫽ myocardial infarction
RMI ⫽ recognized myocardial infarction
SBP ⫽ systolic blood pressure
UMI ⫽ unrecognized myocardial infarction
unanswered questions about UMI. This analysis was performed to determine the prevalence of UMI and the factors
that independently distinguish individuals with UMI from
those with RMI. In addition, it was designed to compare
the long-term prognoses of these two types of MI.
METHODS
Data collection. The design of the CHS has been fully
described elsewhere (8,9). In brief, this longitudinal cohort
study follows 5,888 individuals aged 65 years or more for
cardiovascular events. Enrollment at four field centers in the
U.S. began in 1989. Participants, the majority of whom
were free of overt cardiovascular (CV) disease at the time of
entry, are tracked annually with questionnaires regarding
medical history, medication use, and CV risk factors. In
addition, they receive annual evaluations, which include
physical examinations, neurologic and psychosocial assessments, laboratory studies, and noninvasive tests, including
ECGs.
Electrocardiogram analysis and diagnostic criteria for
UMI. Standard 12-lead resting ECGs were obtained via
the MAC PC-DT ECG recorder (Marquette Electronics,
Milwaukee, Wisconsin). The results were transmitted to the
CHS Electrocardiographic Reading Center for analysis
using the NOVACODE measurement and classification
system (10). To identify individuals with prior UMI, the
CHS database was queried for subjects who evidenced a
prior myocardial infarction on the entry ECG, but who
answered either “no” or “don’t know” to the entry question
“Has a doctor ever told you that you had a myocardial
infarction or heart attack?” Electrocardiographic criteria for
prior infarction included the presence of Q-waves that were
of sufficient duration and amplitude to meet the leadspecific standards of the Minnesota Code (codes 1-1
through 1-2, except 1-2-8). Alternative criteria included the
presence of smaller Q-waves (code 1-2-8 or 1-3), when
combined with significant ST-segment or T-wave abnormalities (codes 4-1 through 4-3, or codes 5-2 through 5-3)
(11).
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Identification of risk factors for UMI. The SPSS for
Windows Release 8.0.0 software was used for all statistical
analyses in this investigation, and for all hypothesis testing,
a p value of ⱕ0.05 was considered statistically significant
(12). To evaluate factors potentially associated with UMI,
characteristics of those with prevalent UMI, those with
prevalent RMI, and those with no prior MI were compared
(three-way analysis of variance [ANOVA] for continuous
variables; three-way chi-square for categorical variables).
Factors of interest included demographic characteristics,
traditional risk factors for coronary artery disease, the
promising novel risk marker Factor VII, past CV diagnoses
and symptoms, the results of noninvasive tests (such as
FEV1 [forced expiratory volume in 1 s by pulmonary
function testing]), and several psychosocial variables.
Next, to identify factors that predict whether an infarct
will be recognized, bivariate logistic regression analyses were
performed, with each of the characteristics described above
tested as an independent variable, and with infarct type
(UMI vs. RMI) as the dependent variable. Finally, to
identify factors that independently distinguish those with
UMI from those with RMI, a stepwise logistic regression
model predicting infarct recognition was generated. Factors
tested in this model included those that were significant, at
a p value ⱕ0.10, in bivariate analysis.
Outcome assessments. Annual mortality was tabulated
both for subjects with prevalent UMI and for those with
prior RMI. Median follow-up to date in the CHS is 5.4
years (mean: 4.8 years). Cause of death was ascertained
using a standardized review process, which considered death
certificate data, coroner/medical examiner reports, and interviews with next of kin (13). Cardiovascular (CV) death
was defined as that due to coronary artery disease, stroke,
congestive heart failure (CHF), or peripheral vascular disease. Non-CV death was defined as death from any other
cause. Total, CV, and non-CV mortality of the two groups
were compared, using both chi-square and Kaplan-Meier
analyses. Finally, to identify independent predictors of
mortality in the entire MI cohort, a Cox proportional
hazards model was assembled. Factors tested in this model
included age, gender, traditional coronary risk factors,
CHF, and infarct recognition status (UMI vs. RMI). Each
of the bivariate predictors of UMI were also tested for
significance.
Definitions of selected clinical variables. Prevalent RMI
was documented as present when, at entry, a participant did
recall being told by a doctor that he or she had had a MI or
heart attack. Angina was defined as reported symptoms at
baseline that were confirmed by medication use, prior
coronary events, a discharge abstract form, and/or a physician questionnaire. Congestive heart failure was documented as present if there was reported CHF at baseline
and confirmation by the subject’s medication list, a discharge abstract form, or a physician questionnaire. Cerebrovascular disease was defined as subject-reported or
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121
Table 1. Characteristics of Participants with UMI, RMI and no Prior MI
Characteristic
No MI†
UMI§
RMI‡
Significance
(3 Way Analysis)
n
Age (yrs)
Male (%)
White (%)
Total cholesterol (mg/dl)
Hypertension (%)
Systolic blood pressure (mm Hg)
Diastolic blood pressure (mm Hg)
Diabetes mellitus, on insulin (%)
Diabetes mellitus, not on insulin (%)
Current smokers (%)
Former smokers (%)
Family history (%)
Body mass index (kg/m2)
History of angina (%)
Congestive heart failure (%)
Cerebrovascular disease (%)
Claudication (%)
Factor VII (%) activity
FEV1, (liter)*
Reported fair or poor health (%)
4,987
72.1
40.0
83.9
211.5
42.0
136.5
70.9
2.1
8.2
11.8
39.8
28.1
26.6
9.1
2.5
2.6
1.8
124.5
2.05
22.4
201
74.6
48.2
81.2
211.2
54.3
142.2
72.2
1.5
18.8
14.2
39.1
25.9
27.1
18.9
4.0
10.4
3.0
123.1
1.88
33.0
700
73.5
57.6
83.1
207.7
51.9
135.7
69.4
4.0
17.6
10.9
48.7
38.0
27.0
63.3
18.1
12.3
7.3
117.8
2.05
43.9
⬍ 0.01
⬍ 0.01
0.54
0.04
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
0.16
⬍ 0.01
⬍ 0.01
0.06
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
*FEV1 ⫽ forced expiratory volume in 1 s by pulmonary function testing; †MI ⫽ myocardial infarction; ‡RMI ⫽ recognized
myocardial infarction; §UMI ⫽ unrecognized myocardial infarction.
physician-diagnosed stroke or transient ischemic attack.
Claudication was based on symptoms reported at baseline
and/or on baseline examination.
Hypertension (HTN) was defined as systolic blood pressure (SBP) ⱖ160 mm Hg, diastolic blood pressure (DBP)
ⱖ95 mm Hg, or a reported history of HTN that was
confirmed by current use of an antihypertensive medication.
Diabetes mellitus was defined as a reported history of
diabetes, insulin or oral hypoglycemic use, or fasting blood
glucose ⱖ140. Family history of coronary artery disease was
based on reported MI in a sibling.
RESULTS
At baseline, 901 of the 5,888 participants (15.3%) evidenced
a prior MI, and 201 (22.3%) of these were unrecognized.
Table 1 demonstrates the characteristics of the no-MI,
UMI, and RMI subgroups. In unadjusted comparisons,
there were several factors that differed significantly among
these populations. Within the demographic variables, age
was highest in the UMI group and male gender was most
frequent in the RMI group. Conventional risk factors also
differed among the three groups, including hypertension,
which was most frequent in the UMI population, and family
history of coronary artery disease, which was most frequent
in the RMI population. Rates of several clinical syndromes
also differed significantly among these subgroups. Angina,
CHF, cerebrovascular disease, and claudication were all
more frequent in the RMI population, while FEV1 was
lowest on average in the subjects with UMI.
Factors associated with UMI. The results of bivariate
modeling, comparing the UMI and RMI subgroups, are
presented in Table 2. Several factors were found to be
significant bivariate predictors of UMI, including female
gender, increasing age and blood pressure. Also, the absence
of common CV diagnoses, including angina, CHF, and
claudication, predicted that an MI would be unrecognized.
Additional factors that were associated with UMI included
the absence of a family history of coronary artery disease,
low FEV1, increasing Factor VII level, and good or excellent self-assessed health status. Former smoking was associated with recognized infarction.
Results of the stepwise logistic regression analysis are
displayed in Table 3. This revealed that the sole independent predictors of UMI were the absence of angina and the
absence of CHF. There was also a trend toward an
independent association with low FEV1. Given the established impact of gender and height on FEV1, we controlled
for these factors in the regression analysis.
Prognosis. Six-year total, CV, and non-CV mortality rates
of the two MI subgroups, as compared by chi-square
analyses, are presented in Table 4. These analyses demonstrated that the mortality rate for those with prevalent UMI
was significantly higher than that for those with no history
of infarction, and it was not significantly different from that
of individuals with RMI. Additional comparisons of the
recognized and unrecognized infarction groups showed that
subjects with UMI have a significantly lower CV mortality
rate, but also a trend toward more frequent non-CV death.
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Table 2. Bivariate Analyses Directly Comparing Subjects with
Recognized (n ⫽ 700) and Unrecognized (n ⫽ 201) Myocardial
Infarction: Significant Predictors of Unrecognized Infarction
Predictor of
Unrecognized MI‡
Female gender
Age (Increments of 5 years)
SBP (Increments of
5 mm Hg)§
DBP (Increments of
5 mm Hg)*
Absence of angina
Absence of congestive heart
failure
Absence of claudication
Absence of family history
0.5-liter decrease in FEV1†
Prior smoking
Factor VII (% activity)
Good or excellent perceived
health
Odds Ratio
(95% CI)
Significance
1.45 (1.06, 1.99)
1.18 (1.04, 1.35)
1.06 (1.03, 1.10)
0.02
0.01
⬍ 0.01
1.11 (1.04, 1.18)
⬍ 0.01
8.67 (6.01, 12.51)
6.43 (2.33, 17.76)
0.05
⬍ 0.01
2.54 (1.08, 6.01)
1.75 (1.22, 2.50)
1.21 (1.07, 1.38)
0.64 (0.46, 0.88)
1.01 (1.00, 1.01)
1.59 (1.14, 2.21)
0.03
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
⬍ 0.01
*DBP ⫽ diastolic blood pressure; †FEV1 ⫽ forced expiratory volume in 1 s by
pulmonary function testing; ‡MI ⫽ myocardial infarction; §SBP ⫽ systolic blood
pressure.
Kaplan-Meier analyses (Figs. 1 through 3) generated
similar results. Specifically, they showed that mean duration
of survival in the UMI group (5.36 years) was significantly
worse than in the population without MI (5.68 years) (p ⬍
0.001), but not significantly different from that among
subjects with prior recognized infarctions (5.18 years) (p ⫽
0.26). Once again, the similar survival in the two MI
populations appears to represent the combined effect of
significantly lower CV mortality (p ⫽ 0.015) and a nonsignificant trend toward greater non-CV mortality (p ⫽ 0.22)
in subjects whose infarctions were unrecognized (Figs. 2 and
3).
In Cox proportional hazards modeling (Table 5), the
independent predictors of death after MI were increased
age, male gender, CHF, diabetes mellitus, and fair or poor
perception of one’s personal health status. Infarct recognition was not an independent predictor of mortality. However, there was a significant interaction between infarct
recognition status and family history, and there was a trend
Table 3. Logistic Regression Analysis Directly Comparing
Subjects with Unrecognized Myocardial Infarction (n ⫽ 201) to
Those with RMI (n ⫽ 700): Independent Predictors of UMI
Factor
Odds Ratio
(95% CI)
p
Absence of angina
Absence of congestive heart failure
0.5-liter decrease in FEV1*
6.31 (4.25, 9.37)
3.94 (1.84, 8.45)
1.16 (0.99, 1.37)
⬍ 0.01
⬍ 0.01
0.07
Note: Standing height and gender were forced into the model, but neither factor was
a significant independent predictor of infarct recognition.
*FEV1 ⫽ forced expiratory volume in 1 s by pulmonary function testing.
toward a significant interaction between infarct recognition
and hypertension.
DISCUSSION
Primary findings. Among the elderly, UMI represents
22.3% of all MIs. Factors that independently distinguish
individuals with UMI from those with clinically detected
infarction include the absence of angina and the absence of
CHF. Other demographic and clinical features, including
age and traditional risk factors, are not independently
associated with infarct recognition. Long-term mortality
after UMI is not significantly different from that after RMI.
Frequency of UMI. Although greater than one-fifth of all
MIs in the CHS were clinically unrecognized, prior studies
have suggested that the relative frequency of UMI is even
greater. In a 1990 Framingham analysis, UMI represented
30% of all infarctions (6). Similarly, studies of the Reykjavik
cohort demonstrated that 35% of infarctions in men and
33% all infarctions in women escaped clinical detection
(5,14). Also, in the Bronx Aging Study, which evaluated
individuals at least 75 years of age, 43.5% of MIs were
clinically unrecognized (15).
These differences are probably both methodological and
demographic. The ECG criteria for MI varied among
studies, and differences in baseline characteristics may have
contributed as well. Also, improvements over time in
patient education, physician awareness, and diagnostic testing may have reduced the frequency with which infarcts
escape clinical attention. Finally, whereas the Framingham
analysis addressed incident infarctions, our investigation
focused on prevalent MI, and thus prevalence-incidence
bias may have impacted on the findings (6,16). More
specifically, incident UMIs resulting in early mortality
might be underrepresented in our sample, and this might
have led to an underestimate of the frequency of this event.
It should be noted, however, that, due to their reliance on
the ECG to document MI, probably all of the available
studies have underestimated the frequency of UMI. As
Q-wave criteria were employed to make this diagnosis,
many previously unrecognized infarctions were probably
never detected. These would include UMIs resulting in
sudden cardiac death, unrecognized non-Q-wave MIs, and
unrecognized Q-wave infarcts in which, over time, the Q
waves resolved (7,17).
Factors associated with UMI. A key issue in identifying
factors associated with UMI is the comparison group. In
several prior investigations, those with UMI were compared
to all other subjects in the study population, or to all those
free of prior infarction (1,2). In other studies, in which the
intent was to identify factors that distinguish those with
UMI from those with RMI, investigators performed bivariate analyses comparing these two groups only (3,5,6).
However, to determine whether a factor is independently
associated with infarct recognition, multivariate regression,
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123
Table 4. Chi-Square Analyses of Six-Year Mortality in Participants with UMI, RMI, and No
History of MI
Event
No MI*
(n ⴝ 4,987)
UMI†
(n ⴝ 201)
RMI†
(n ⴝ 700)
(UMI-RMI)
p
(UMI-No MI)
p
Death
CV Death
Coronary
Stroke
Other
Non-CV Death
600 (12.0%)
237 (4.8%)
150 (3.0%)
49 (1.0%)
38 (0.8%)
363 (7.3%)
43 (21.4%)
18 (9.0%)
13 (6.5%)
5 (2.5%)
0 (0.0%)
25 (12.4%)
177 (25.4%)
113 (16.1%)
87 (12.4%)
17 (2.4%)
9 (1.3%)
64 (9.1%)
0.24
0.01
⬍ 0.01
⬍ 0.01
0.17
⬍ 0.01
*MI ⫽ myocardial infarction; †RMI ⫽ recognized myocardial infarction; ‡UMI ⫽ unrecognized myocardial infarction.
with recognition status as the dependent variable, is necessary. To our knowledge, this investigation is the only study
to include this type of model.
RESULTS OF BIVARIATE MODELS. Several of the bivariate
findings in CHS merit closer inspection. Consistent with
prior studies, increasing SBP and DBP were significant
bivariate risk factors for UMI (1–3). These findings support
physiologic studies suggesting that mechanisms that control
blood pressure are interrelated with those that determine
pain perception (18 –23).
Age has also been a consistent bivariate predictor of UMI
(6). In the Reykjavik cohort, the odds ratio for UMI, per
year of age, equaled 1.10 (95% confidence interval, 1.07 to
1.12) (5). These findings are consistent with the general
notion that the manifestations of disease are often blunted
in the elderly. Also consistent with previous analyses is the
association of female gender with UMI (6), which may relate
to gender differences in clinical presentation, as well as
diagnosis bias.
Finally, while diabetes is believed to induce cardiac sensory and autonomic neuropathy, and while diabetics have
Figure 1. Survival curve for total mortality by RMI and UMI.
been shown to have a high prevalence of silent ischemia, in
CHS, as in prior studies, diabetes was not associated with
infarct recognition (3,5,6,24). This was true not only for
diabetes in general, but also for the subgroup of diabetics on
insulin or oral hypoglycemic therapy. Confounding factors
that may override the effects of neuropathy include the
intensity of CV screening and counseling provided to
diabetics, as well as the relatively high frequency of emergent complications accompanying infarction in diabetic
patients (25).
In multivariate
modeling, none of these traditional risk factors independently distinguished those with UMI from those with RMI.
Instead, the major independent associations were with CV
symptoms and diagnoses, including angina. Among men in
the Framingham study, 53% of subjects with RMIs had a
history of angina, but only 24% of the individuals with
UMIs reported this symptom. In women, the percentages
were 45 and 33, respectively, and the Reykjavik study
produced similar results (5,6). In the CHS, not only were
the differences in rates of angina more dramatic (63% vs.
RESULTS OF MULTIVARIATE ANALYSIS.
Figure 2. Survival curve for cardiovascular disease deaths by RMI
and UMI.
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Figure 3. Survival curve for non-CV disease deaths by RMI and
UMI.
19%), but also the absence of this symptom independently
predicted that an infarct would be unrecognized.
This finding may simply be due to diagnosis bias.
Individuals with a diagnosis of angina probably receive more
aggressive CV follow-up than do others without this diagnosis. When they develop new or changing symptoms, even
if mild or nonspecific, they are probably more likely to
undergo a thorough CV evaluation to determine whether a
new event has occurred (26). These patients and their
families may receive more aggressive education and counseling regarding coronary artery disease, and they may
consequently be better equipped to recognize the symptoms
of MI and seek medical attention.
Table 5. Independent Predictors of Mortality Post-MI
Factor
Increasing age
Male gender
Congestive heart failure
Diabetes
Worsening health perception
Claudication
MI unrecognized*
Family history
Unrecognized MI–family
history interaction
Hypertension
Unrecognized MI–hypertension
interaction
Hazard Ratio
(95% CI)
p
1.07 (1.05, 1.10)
1.56 (1.15, 2.11)
2.30 (1.66, 3.20)
1.38 (1.02, 1.88)
1.65 (1.23, 2.23)
1.78 (1.16, 2.74)
0.86 (0.49, 1.48)
0.94 (0.69, 1.28)
3.92 (1.86, 8.24)
⬍ 0.01
⬍ 0.01
⬍ 0.01
0.05
⬍ 0.01
⬍ 0.01
0.41
0.72
⬍ 0.01
0.86 (0.63, 1.18)
0.51 (0.24, 1.07)
0.59
0.07
Note: Infarct recognition status was forced into the model.
*MI ⫽ myocardial infarction.
Alternatively, this association may be grounded in a
generalized hyposensitivity to myocardial ischemia, otherwise known as a “defective anginal warning system” (27).
Basic science investigations have identified several possible
neuropsychiatric disruptions that could block normal warning mechanisms. These include insufficient myocardial receptor stimulation, cardiac neuropathy, and a host of complex supratentorial phenomena, including stoicism and
denial (28 –35).
The independent association with CHF and the trend
toward an independent association with low FEV1 are more
difficult to interpret, but they also suggest that diagnosis bias
contributes to UMI. Patients with the diagnosis of CHF,
like those with angina, are typically followed closely for CV
symptoms and events. Conversely, patients with a low
FEV1 typically carry pulmonary diagnoses, and when they
develop chest symptoms, physicians may be predisposed to
attribute them to respiratory problems.
The associations of CHF and FEV1 may also in part be
based on neuropsychiatric phenomena. More specifically,
they may relate to anatomic and physiologic gates in the
afferent nervous system, at which sensory impulses may
collide and abolish each other. It has been suggested that in
some, when these gates are bombarded with respiratory
stimuli, pain or pressure impulses from the heart may be
blocked (36).
Prognosis of UMI. This study also confirms that unrecognized infarctions have significant clinical implications.
Total mortality was significantly greater in the UMI group
than in those with no prior infarction. In addition, while the
association between infarct recognition status and outcome
varied with the presence or absence of coronary risk factors,
total mortality did not significantly differ between the UMI
and RMI groups. These results are similar to those in the
Reykjavik Study, in which 15-year mortality in men with
UMI was 55%, as compared to 52% for those with a
previously recognized infarct (5).
This poor prognosis may relate to non-CV co-morbidity.
In comparison to subjects with prior recognized infarctions,
individuals with prevalent UMI were approximately 30%
more likely to die a non-CV death over the study period.
Perhaps non-CV co-morbidity may impact on individuals’
ability to sense infarction, and therefore also on their
tendency to ascribe symptoms to coronary artery disease.
Subjects with UMI may have also been adversely affected
by delays in diagnosis and treatment. When a CHS examination identifies a previously unrecognized infarction, the
subject’s personal physician is notified. However, even if the
physician chooses to prescribe standard postinfarction medications, their initiation may occur years after the MI. This
may explain why UMI subjects, as compared to participants
with RMI, had more adverse coronary risk profiles, including higher mean cholesterol and blood pressure, and a
greater prevalence of current smoking. This investigation
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was not designed to address differences in treatment between the UMI and RMI populations.
APPENDIX
Study limitations. This study has several limitations. First,
it addressed prevalent infarctions, and not incident events.
This may have had implications for data quality, and it
limited our assessment of risk factors for UMI, as we could
not evaluate temporal associations. However, in the CHS to
date, the number of incident infarcts is insufficient to answer
with adequate statistical power the relevant questions about
UMI. A second important limitation of this investigation
was the use of self-report in the coding of MI, as it created
the potential for recall bias. In particular, using self-report
to document RMI might have led to an overestimate of its
prevalence. In the CHS, each reported infarction prompts a
standardized review process, including evaluation of ECGs,
discharge abstract forms, and physician questionnaires. This
process confirmed only 471 of the 700 prevalent recognized
infarctions. However, if a substantial number of the reported infarctions were erroneous, then the true relative
frequency of UMI would be even greater than reported, and
this would further support our conclusion that a substantial proportion of infarctions are unrecognized. Finally,
given that the CHS cohort has been followed for only six
years, it is possible that, with longer follow-up, significant differences in total mortality between infarct groups
will emerge.
THE CARDIOVASCULAR HEALTH
STUDY: PARTICIPATING
INSTITUTIONS AND PRINCIPAL STAFF
Study implications. Despite these limitations, these results
have important implications for further study, and for
patient care. First, the impact of the absence of angina on
risk for UMI needs to be explored further. Subsequent
investigations should evaluate potential neuropsychiatric
explanations, as they may provide insight into the pathophysiology of UMI. Alternatively though, if they are negative, more emphasis should be placed on the potential role
of diagnosis bias.
While these investigations are ongoing, cost-effective
screening mechanisms should be identified, as they may
promote early diagnosis of prior UMI, early management,
and potentially improved outcomes. More specifically, because it appears that UMI cannot be independently predicted by traditional clinical factors, the potential costs and
benefits of routine screening ECGs in all individuals with
coronary risk factors should be assessed. Also, because the
prognosis after UMI is as poor as that after recognized
infarction, it may be prudent to evaluate coronary risk in
affected individuals thoroughly. Strategies for risk stratification after detection of a previously unrecognized infarct
should be evaluated. It would be particularly valuable to
define tests and patient characteristics that predict outcome
post-UMI, so as to identify patients who may benefit from
more aggressive therapy. Finally, future studies should
investigate the role of standard post-MI therapies in this
interesting subset of patients.
125
Forsyth County, NC—Bowman Gray School of Medicine
of Wake-Forest University: Gregory L. Burke, Sharon
Jackson, Alan Elster, Walter H. Ettinger, Curt D. Furberg,
Gerardo Heiss, Dalane Kitzman, Margie Lamb, David S.
Lefkowitz, Mary F. Lyles, Cathy Nunn, Ward Riley, John
Chen, Beverly Tucker; Forsyth County, NC—Bowman
Gray School of Medicine-ECG Reading Center: Farida
Rautaharju, Pentti Rautaharju.
Sacramento County, CA—University of California,
Davis: William Bommer, Charles Bernick, Andrew Duxbury, Mary Haan, Calvin Hirsch, Lawrence Laslett, Marshall Lee, John Robbins, Richard White.
Washington County, MD—The Johns Hopkins University: M. Jan Busby-Whitehead, Joyce Chabot, George W.
Comstock, Adrian Dobs, Linda P. Fried, Joel G. Hill,
Steven J. Kittner, Shiriki Kumanyika, David Levine, Joao A.
Lima, Neil R. Powe, Thomas R. Price, Jeff Williamson,
Moyses Szklo, Melvyn Tockman.
MRI Reading Center, Washington County, MD—The
Johns Hopkins University: R. Nick Bryan, Norman
Beauchamp, Carolyn C. Meltzer, Naiyer Iman, Douglas
Fellows, Melanie Hawkins, Patrice Holtz, Michael Kraut,
Grace Lee, Larry Schertz, Cynthia Quinn, Earl P. Steinberg, Scott Wells, Linda Wilkins, Nancy C. Yue.
Allegheny County, PA—University of Pittsburgh: Diane
G. Ives, Charles A. Jungreis, Laurie Knepper, Lewis H.
Kuller, Elaine Meilahn, Peg Meyer, Roberta Moyer, Anne
Newman, Richard Schulz, Vivienne E. Smith, Sidney K.
Wolfson.
Echocardiography Reading Center (Baseline)—
University of California, Irvine: Hoda Anton-Culver, Julius
M. Gardin, Margaret Knoll, Tom Kurosaki, Nathan Wong.
Echocardiography Reading Center (Follow-up)—
Georgetown Medical Center: John Gottdiener, Eva Hausner, Stephen Kraus, Judy Gay, Sue Livengood, Mary Ann
Yohe, Retha Webb; Ultrasound Reading Center—
Geisinger Medical Center, Danville, Pennsylvania: Daniel
H. O’Leary, Joseph F. Polak, Laurie Funk.
Central Blood Analysis Laboratory—University of Vermont: Edwin Bovill, Elaine Cornell, Mary Cushman, Russell P. Tracy; Respiratory Sciences—University of Arizona–
Tuscon: Paul Enright; Coordinating Center—University of
Washington, Seattle: Alice Arnold, Annette L. Fitzpatrick,
Bonnie K. Lind, Richard A. Kronmal, Bruce M. Psaty,
David S. Siscovick, Lynn Shemanski, Will Longstreth,
Patricia W. Wahl, David Yanez, Paula Diehr, Maryann
McBurnie, Chuck Spiekerman, Scott Emerson, Cathy Tangen, Priscilla Velentgas; NHLBI Project Office: Robin
Boineau, Teri A. Manolio, Peter J. Savage, Patricia Smith.
126
Sheifer et al.
Unrecognized MI
Reprint requests and correspondence: Stuart E. Sheifer, Fellow,
Division of Cardiology, Georgetown University Medical Center, 4
Main, 3800 Reservoir Road, N.W., Washington, DC 20007.
E-mail: [email protected].
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