ARRHYTHMIAS
Prognostic implications of atrial fibrillation in patients undergoing myocardial perfusion single-photon emission computed tomography
Aiden Abidov, MD, PhD*,
Rory Hachamovitch, MD, MSc, FACC ,
Alan Rozanski, MD ,
Sean W. Hayes, MD*,
Marcia M. Santos, MD*,
Maria G. Sciammarella, MD*,
Ishac Cohen, PhD*,
James Gerlach, CNMT*,
John D. Friedman, MD*,
Guido Germano, PhD, FACC* and
Daniel S. Berman, MD, FACC*,*
Departments of Imaging (Division of Nuclear Medicine) and Medicine (Division of Cardiology), Cedars-Sinai Medical Center, Los Angeles, CaliforniaUSA
Cardiovascular Division, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CaliforniaUSA
Division of Cardiology, St. Luke's Roosevelt Hospital Center, New York, New YorkUSA
Manuscript received February 25, 2004;
revised manuscript received April 22, 2004,
accepted May 25, 2004.
* Reprint requests and correspondence: Dr. Daniel S. Berman, Department of Imaging, Cedars-Sinai Medical Center, Taper Building, Room 1258, 8700 Beverly Boulevard, Los Angeles, California 90048
(Email: daniel.berman{at}cshs.org).
 |
Abstract
|
|---|
OBJECTIVES: The aim of this research was to determine whether presence of atrial fibrillation (AF) provides incremental prognostic information relative to myocardial perfusion single-photon emission computed tomography (MPS) with respect to risk of cardiac death (CD).
BACKGROUND: The prognostic significance of AF in patients undergoing MPS is not known.
METHODS: A total of 16,048 consecutive patients undergoing MPS were followed-up for a mean of 2.21 ± 1.15 years for the development of CD. Of those, 384 patients (2.4%) had AF. Cox proportional hazards method was used to compare clinical and perfusion data for the prediction of CD in patients with and without AF.
RESULTS: Atrial fibrillation was a significant predictor of CD in patients with normal (1.6% per year vs. 0.4% per year in non-AF patients), mildly abnormal (6.3% per year vs. 1.2% per year), and severely abnormal MPS (6.4% per year vs. 3.7% per year) (p < 0.001 for all). By multivariable analysis, AF patients had worse survival (p = 0.001) even after adjustment for the variables most predictive of CD: age, diabetes, shortness of breath, use of vasodilator stress, rest heart rate, and the nuclear variables. In the 4,239 patients with left ventricular ejection fraction evaluated by gated MPS, AF demonstrated incremental prognostic value not only over clinical and nuclear variables, but also over left ventricular ejection in predicting CD (p = 0.014).
CONCLUSIONS: The presence of AF independently increases the risk of cardiac events over perfusion and function variables in patients undergoing MPS. Patients with AF have a high risk of CD, even when MPS is only mildly abnormal. Whether patients with AF and mildly abnormal MPS constitute a group more deserving of early referral to cardiac catheterization is a question warranting further study.
|
Abbreviations and Acronyms
| | AF = atrial fibrillation | | CD = cardiac death | | LVEF = left ventricular ejection fraction | | MPS = myocardial perfusion single-photon emission computed tomography | | SDS = summed difference (stress-rest) perfusion score | | SPECT = single-photon emission computed tomography | | SRS = summed rest score | | SSS = summed stress score |
|
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, accounting for approximately one-third of hospitalizations for rhythm disturbances (1). While the prevalence of AF in modern Western society is <1% in those younger than 60 years of age, it exceeds 6% in those older than 80 years of age (2–4). It has been estimated that 2.2 million Americans have paroxysmal or persistent AF (5), and it is predicted that the prevalence of AF will increase 2.5-fold in the next 50 years (6). The mortality rate of patients with AF is almost double that of patients in normal sinus rhythm; it is linked to the severity of underlying heart disease (2,7–9), and is attributed mainly to an increased cardiac death (CD) rate per se (mostly due to heart failure), rather than to thromboembolism (10). Myocardial perfusion single-photon emission computed tomography (MPS) is effective for risk-stratification of patients with known or suspected coronary artery disease (11–17), but the impact of AF on the prognostic implications of MPS has virtually not been studied. Accordingly, the aim of our study was to assess whether the presence of AF provides incremental prognostic information in patients undergoing MPS with respect to risk of CD.
|
Methods
|
|---|
Study population.
Initial study population was comprised of 18,291 consecutive patients who underwent dual-isotope (rest 201Tl/stress 99mTc sestamibi) MPS with exercise or vasodilator (dipyridamole or adenosine) stress testing between January 1991 and December 1998 at Cedars-Sinai Medical Center. Patients with known valvular heart disease and/or nonischemic cardiomyopathy were excluded based on the results of patient's history, physical examination, and available medical records, including hospital files. Of the initial population, 784 patients (4.2%) were lost to follow-up. In addition, 1,459 patients with early coronary artery bypass grafting or percutaneous coronary intervention <60 days after the index MPS were censored from survival analysis because their referral to revascularization may have been influenced by their scan results (18,19). For the purpose of prognostic assessment, the final population included 16,048 patients, of whom 384 patients (2.4%) had AF and 15,664 did not. We defined the AF population as patients with AF on the baseline resting electrocardiogram (ECG) on the day of MPS. All other patients were considered as the non-AF population. In our study, we compared clinical, nuclear, and prognostic data of AF patients to the non-AF patients (Table 1).
Imaging procedure.
All patients underwent rest 201Tl/stress 99mTc-sestamibi MPS as previously described (20).Whenever possible, beta-blockers and calcium-channel antagonists were terminated 48 h before testing and nitrates at least 6 h before testing. Patients performed a symptom-limited exercise treadmill test (18,19,21) or vasodilator (intravenous dipyridamole or adenosine infusion) stress using standard protocols (17,22). Patients were instructed not to consume coffee or other products containing caffeine for 24 h before the test. The ECG was monitored continuously in three leads (aVF, V1, and V5), and 12-lead ECGs were obtained at rest, during each stage of exercise, and postexercise to assess for arrhythmias or ischemic ST-segment deviation (22). Nondiagnostic ECG response was defined as presence of the significant resting ST-T abnormalities, in which the response to exercise would be considered nondiagnostic. Blood pressure was measured and recorded at rest, at the end of each stress stage, and at peak stress.
Acquisition protocol.
All patients underwent separate acquisition, dual isotope MPS (20). Rest 201Tl MPS was started 10 min after injection of radiotracer. During imaging two energy windows were used: 30% centered over the 68 to 80 keV energy peak and 20% centered over the 167 keV energy peak. 99mTc-sestamibi MPS was initiated 60 min after injection and employed single 15% window, centered on the 140 keV peak; MPS was performed with a circular or elliptical 180° acquisition for 64 projections at 20 to 25 s/projection for 201Tl (3.0 to 4.5 mCi) and at 15 to 25 s/projection for 99mTc-sestamibi (25 to 40 mCi). All images were subject to quality control measures. The projection data were reconstructed into tomographic transaxial images using filtered backprojection and automatic reorientation (23,24). No attenuation or scatter correction was used.
In 4,239 patients (26.4% of the study population), 8-frame gated SPECT imaging (100% acceptance window) was performed to assess left ventricular ejection fraction (LVEF) (25); only technically satisfactory gated LVEF studies were reported and included in the database.
Image interpretation.
Semiquantitative visual interpretation was performed using the 20-segment model (11,20). Each segment was scored using a 5-point scoring system (0 = normal, 1 = equivocal, 2 = moderate, 3 = severe reduction of radioisotope uptake, and 4 = absence of detectable tracer uptake in a segment). Summed stress (SSS), rest (SRS), and rest-stress difference (SDS) scores were obtained by summing the individual scores of the 20 segments (20). These indexes were converted to percent of the total myocardium (% Myo) (26) involved with stress (% Myo stress, from SSS), ischemic (% Myo ischemic, from SDS), or fixed defects (% Myo fixed, from SRS) by dividing the summed scores by 80, the maximum potential score in the 20-segment model (4 x 20), and multiplying by 100. Myocardial perfusion SPECT results were categorized using % Myo stress; % Myo stress <5 was considered as normal, % Myo stress = 5 to 10 as mildly abnormal, and % Myo stress >10 as moderately to severely abnormal MPS (27).
In those patients who had gated SPECT performed, gated short-axis images were processed using quantitative gated SPECT software (QGS, Cedars-Sinai Medical Center, Los Angeles, California), and the LVEF was automatically calculated (28).
Patient follow-up.
Specifically dedicated research personnel, blinded to the clinical and MPS test results, performed all follow-up-related procedures (11). The follow-up duration was >1 year for all study population patients. Patients were followed-up for CD or nonfatal myocardial infarction. Cardiac death was defined as death attributable to any cardiac cause (e.g., lethal arrhythmia, myocardial infarction, or congestive heart failure) as confirmed by review of death certificate and medical records. All other death was considered noncardiac. Known stroke-related death was not considered CD. Non-fatal myocardial infarction was documented by appropriate ECG and cardiac enzyme level changes. If both events were found in a patient, only CD was considered as follow-up event. We also collected information regarding the non-cardiac mortality in the study population.
Statistical analysis.
All continuous variables are expressed as means ± SD. The mean differences for continuous variables were compared using the Student t test (two-tailed) or analysis of variance (in case of multiple comparisons). Categorical variables were compared using a chi-square statistic. A p value <0.05 was considered significant; where appropriate, 95% confidence intervals were employed.
Unadjusted as well as adjusted CD rates were assessed. For the latter, Cox proportional hazards analysis was applied to determine the independent prognostic value of clinical, historical, and nuclear parameters. Selection of variables for consideration for entry was based on both univariate statistical significance and clinical judgment (17). The threshold for entry of variables into the final model was p < 0.05. A statistically significant increase in the global chi-square of the model after the addition of the tested variables defined incremental prognostic value. Model assumptions of proportional hazards, linearity, and additivity were examined, and risk-adjusted survival and predicted CD rates were determined on the basis of the final model. Kaplan-Meier analysis was used to depict risk-adjusted cumulative CD-free survival curves comparing patients among the following clinical subsets: 1) non-AF patients with normal MPS; 2) AF patients with normal MPS; 3) non-AF patients with abnormal MPS; and 4) AF patients with abnormal MPS. Statistical analysis used SPSS for Windows (version 11.0, SPSS Inc., Chicago, Illinois).
|
Results
|
|---|
Patient characteristics.
Within our patient population, the prevalence of AF rose significantly with age (Fig. 1). As shown in Table 1, the AF patient cohort was significantly older than our non-AF cohort, by a mean of approximately eight years. Compared with non-AF cohort, our AF cohort also underwent more vasodilator than exercise stress, had an 8% greater mean incidence of hypertension, and a higher frequency of dyspnea. In addition, the frequency of non-diagnostic ECGs was two-fold higher in the AF cohort. Atrial fibrillation patients more frequently had resting tachycardia (rest heart rate >100 beats/min), and almost half of the AF cohort was under the influence of digoxin during MPS. Only a small percentage of patients in both cohorts was under the influence of beta-blockers or calcium-channel blockers during stress testing.
View larger version (14K):
[in this window]
[in a new window]
|
Figure 1 Prevalence of atrial fibrillation (AF) in the study population as a function of age. *p < 0.001 across groups. Solid bars = percentage of AF patients.
|
|
Follow-up events.
The unadjusted CD rate was more than three-fold higher among the AF versus non-AF patients, during a mean follow-up period spanning 2.21 ± 1.15 years (Table 2). By contrast, there were no differences in the non-fatal MI rate between the two groups. Patients with AF had almost three-fold higher rate of noncardiac death compared with non-AF patients (Table 2). Furthering the multivariable analysis of cardiac events, we compared only the CD rate in the AF and non-AF groups.
MPS results in patients with and without AF.
Compared with the non-AF patients, the patients with AF had greater % Myo fixed values but comparable % Myo ischemic values (Fig. 2). Accordingly, the higher % Myo stress values noted in the AF patients were due almost entirely to greater % Myo fixed values.
View larger version (18K):
[in this window]
[in a new window]
|
Figure 2 Myocardial perfusion single-photon emission computed tomography results in the study population. *p = 0.007; **p = 0.002 compared with non-atrial fibrillation (AF) patients. % Myo fixed = % myocardium hypoperfused at rest (from summed rest score); % Myo ischemic = % myocardium ischemic (from summed difference perfusion score); % Myo stress = % myocardium hypoperfused at stress (from summed stress score). Solid bars = AF; open bars = non-AF.
|
|
CD rate as function of MPS results.
Atrial fibrillation remained a significant predictor of outcome after dividing our patient population into normal, mildly abnormal, and moderately to severely abnormal MPS subgroups, as illustrated in Figure 3. Those with AF had a substantially greater event rate in all MPS categories.
View larger version (23K):
[in this window]
[in a new window]
|
Figure 3 Unadjusted cardiac death (CD) rates in patients with atrial fibrillation (AF) compared with non-AF patients as a function of myocardial perfusion single-photon emission computed tomography results. *p < 0.001. abnl = abnormal; Mod-Sev = moderately to severely; MPS = myocardial perfusion single-photon emission computed tomography; % Myo Stress = % myocardium hypoperfused at stress (from summed stress score). Solid bars = AF patients; open bars = non-AF patients.
|
|
CD rate in different clinical subsets of AF and non-AF patients.
When patients were divided on the basis of various clinical variables, such as age, the type of stress, and the presence or absence of diabetes, of shortness of breath, or of resting tachycardia, AF patients had a higher incidence of cardiac events in all subgroups (Table 3). Of note, 18% among AF cohort had rest HR >100 beats/min, compared with only 2.5% of patients without AF; in both patients with and without AF, this HR elevation was associated with a relatively high cardiac event rate. By contrast, among patients without such rest HR elevation, only the AF subgroup had a significant increase in cardiac events.
Univariable and multivariable analysis.
The most powerful univariable predictor of adverse outcome in the study population was an abnormal MPS, associated with a seven-fold increase in the risk of CD compared with patients who had a normal perfusion study (Table 4). Vasodilator stress, presence of shortness of breath, AF, and age >75 years were also associated with a strikingly high (>3-fold) increase in risk of CD. By Cox proportional hazards analysis, the multivariable model most predictive of CD in the overall study population included AF as well as age, diabetes, shortness of breath, use of vasodilator stress, rest heart rate, and the nuclear variables (% Myo fixed and ischemic) (Table 5). By far, the most potent multivariable predictors of CD were % Myo fixed and ischemic. When added to the multivariable model in place of % Myo fixed and ischemic, % Myo stress also was a strong, independent predictor of CD (p < 0.001), and the global chi-square for the overall model with this substitution was 1,119 (p < 0.001). Among the clinical variables, age and vasodilator stress were the most powerful independent predictors of CD, followed by rest heart rate, shortness of breath, diabetes, and presence of AF. This model included a nonlinear term for age. Of note, despite the higher prevalence of resting tachycardia in AF patients compared with non-AF group, mentioned in the preceding text, there was no prognostic interaction between these two variables by multivariable analysis.
View this table:
[in this window]
[in a new window]
|
Table 5. Final Multivariable Cox Proportional Hazards Model (Global Chi-Square of the Model = 1,146.1, p < 0.001)
|
|
Comparison of risk-adjusted CD rates.
To further assess the impact of clinical factors on outcome among AF and non-AF cohorts, we performed risk-adjusted Kaplan-Meier survival curves by MPS results (Fig. 4). Predicted survival of AF patients was significantly worse compared with non-AF patients (p = 0.001) either in normal and abnormal MPS, even after adjustment for all independent predictors that were identified in the final Cox model, including age, % Myo fixed and ischemic, shortness of breath, rest heart rate, diabetes, and type of stress.
View larger version (17K):
[in this window]
[in a new window]
|
Figure 4 Risk-adjusted cardiac death (CD)-free survival curves of patients with atrial fibrillation (AF) compared with non-AF patients. Solid lines = AF patients; dotted lines = non-AF patients. 1 = non-AF patients with normal myocardial perfusion single-photon emission computed tomography (MPS); 2 = AF patients with normal MPS; 3 = non-AF patients with abnormal MPS; 4 = AF patients with abnormal MPS.
|
|
Prognostic significance AF in patients with normal and impaired left ventricular function.
Among the 4,239 patients who had LVEF assessment performed by gated SPECT imaging, there were 86 AF patients (22.2% of AF group) and 4,153 non-AF patients (26.5% of non-AF group). While patients with AF in this cohort were older and generally sicker than their non-AF counterparts (as previously noted in our larger overall patient group), the AF and non-AF patients in this cohort did not differ significantly from the larger sample of AF and non-AF patients within our study (Table 6). Although the percentage of patients with a history of prior myocardial infarction was the same in both the AF and non-AF cohorts, the AF patients had a lower mean resting LVEF value and a higher percentage of patients with an LVEF  35%.
View this table:
[in this window]
[in a new window]
|
Table 6. Clinical Characteristics of the Study Population Patients Having Left Ventricular Function Assessed by Gated SPECT
|
|
Notably, as indicated in Figure 5, when LVEF was dichotomized (as >35% or 35%), the frequency of CD was significantly increased in the AF patients in both LVEF subgroups, but the relative risk of CD was more prominent in AF patients compared with non-AF patients with preserved LV function.
View larger version (13K):
[in this window]
[in a new window]
|
Figure 5 Unadjusted cardiac death (CD) rates in patients with atrial fibrillation (AF) compared with non-AF patients as a function of left ventricular ejection fraction. *p = 0.001. EF = ejection fraction. Solid bars = AF patients; open bars = non-AF patients.
|
|
Further, when added in stepwise fashion to our multivariate model (i.e., only after considering all of the significant clinical variables, nuclear perfusion variables [SRS and SDS], and the LVEF), the presence or absence of AF added significantly (p = 0.014) to the global chi-square value for prediction of CD (Fig. 6). This was true even though LVEF, entered as a continuous variable, was a significant independent predictor of CD (p = 0.001) among the subgroup of patients who underwent gated SPECT assessment. Adding other variables (history of hypertension, prior myocardial infarction, or revascularization) to the model that already included AF did not reveal any significant increase in predictive value of the model.
View larger version (21K):
[in this window]
[in a new window]
|
Figure 6 Incremental prognostic value of chronic atrial fibrillation (AF) in predicting cardiac death over clinical variables (*age, type of stress, resting heart rate, diabetes, and shortness of breath), nuclear variables (% myocardium hypoperfused at rest [% Myo fixed] and % myocardium ischemic [% Myo ischemic]), left ventricular ejection function ([LVEF] *including the interaction LVEF x shortness of breath). Added to all these most significant variables, AF provided additional significant gain in global chi-square, compared with the previous step.
|
|
|
Discussion
|
|---|
This is the first study to examine the interaction between AF and the results of stress-rest MPS for predicting cardiac outcomes. The reason why such studies have been lacking previously is evident from the fact that our follow-up of over 16,000 subjects contained only 384 patients with AF. On the other hand, while AF in the overall population was uncommon, the incidence of AF neared 5% among our patients, who were >75 years old, a subgroup that is increasingly being referred for cardiac stress testing.
Our principal finding was the observation that AF was associated with a significantly increased CD rate among each of our relevant SPECT subgroups. Among patients with an abnormal SPECT study, the CD rate was more than doubled if AF was present. When these abnormal SPECT patients were further divided into those with mild and relatively more severely abnormal SPECT studies, the relationship between AF and elevated CD rates persisted. Among patients with a normal SPECT study, the observed (unadjusted) CD rate rose from only 0.4% per year in those without AF to 1.6% per year among those with AF. The latter rate places patients into the intermediate risk category, both according to Framingham risk score analysis (29,30) and conventional practice (11–17). By multivariable analysis in the subgroup having gated SPECT, AF demonstrated incremental prognostic value not only over clinical and nuclear variables, but also over LVEF in predicting CD. Of note, observed CD rates were substantially higher in AF patients compared with non-AF patients, regardless of the presence or absence of severe LV dysfunction.
Prior studies.
Chronic AF has generally been associated with substantial morbidity and decreased survival (31), although it is not clear if AF, per se, results in excess mortality.
Previous studies have shown that the overall mortality rate in patients with AF is at least doubled (2,7,9). Whether mortality is directly influenced by AF or AF is a consequence of underlying disease is unclear, including subsets of patients who were admitted for the first time with AF (32), elderly subjects (33), postmyocardial infarction patients (34), patients with advanced heart failure (35), patients with asymptomatic and symptomatic left ventricular dysfunction (10), with pacemakers (36), and even those with implantable cardioverter-defibrillators (37). In some subsets, AF, however, has not been associated with a poor prognosis. For instance, there was no AF-associated increase in mortality noted in the Vasodilator Heart Failure Trial (V-HeFT) (38) and two other relatively small studies of heart failure patients (39,40). Still, among recent large trials, such as a random Swedish population sample of 7,495 men age 47 to 55 years (41), AF was associated with a 3.3 increase in mortality. Also, data from the Framingham Heart Study demonstrated that AF was associated with excess mortality among men and women independent of associated cardiac conditions and risk factors (42). In this study, the risk of mortality conferred by AF did not vary by age or gender and was present even in the absence of valvular disease or preexisting cardiovascular condition.
Notably, none of prior studies attempted to relate MPS findings to outcome among AF patients. Our study demonstrates that AF is independently predictive of worse outcome even after taking into account consideration of both the magnitude of myocardial scar and ischemia and a variety of powerful clinical variables.
Of note, in the current manuscript we reported MPS results as percentage of myocardium hypoperfused (summed perfusion score normalized by the maximum possible score in the model of the left ventricle). The benefits of this approach include that the percentage of myocardium hypoperfused provides a validated measure with intuitive clinical implications and that can easily be applied with scoring systems using varying numbers of segments (27,28).
Potential mechanisms.
The association between AF and increased cardiac events among our various MPS subgroups could be due either to a direct mechanism or an indirect association. With respect to the latter, AF could simply be serving as a marker for severe disease, where the disease parameters, per se, and not AF, are the direct contributors to the heightened death rate in our AF cohort. Along these lines, the AF patients in our study were older, had more hypertension, more dyspnea, more resting ECG abnormalities, and a higher frequency of resting tachycardia (43) compared with the patients without AF, with a number of these factors having figured prominently as prior risk modifiers. The AF patients also had a higher noncardiac death rate. Moreover, compared with the patients without AF, AF patients had a higher prevalence of severe LV dysfunction (LVEF <35%), and more resting perfusion abnormalities, despite the same percentage of patients with history of prior myocardial infarction. Thus, both clinically and functionally, patients with AF represented a population that was intrinsically sicker compared with non-AF group.
On the other hand, several observations in our study raise the possibility that AF may be contributing to a heightened CD rate through a more direct mechanism. First, we divided our patients into subgroups based on the most significant multivariate predictors of CD, including age, history of dyspnea, diabetes, presence of resting tachycardia, type of stress patients were able to tolerate (exercise vs. vasodilator). In each subgroup, the patients with AF had a significantly higher CD rate than the corresponding non-AF patients. Second, we examined the possibility that, whereas no single clinical factor accounted for the heightened CD rate among patients with AF, a combination of clinical factors could explain the difference. To that end, we performed a risk-adjusted Kaplan-Meier survival curve analysis in which we compared CD rates after adjusting for all of the independent Cox model predictors of CD. This risk-adjusted analysis still resulted in a significantly greater CD rate in AF versus non-AF patients both in normal and abnormal MPS subgroups. Thus, AF remained a significant independent prognostic predictor even after adjustment for all other known prognostic modifiers. Finally, even when the most powerful prognostic variables (perfusion scores and LVEF) were entered into the model in the group who had gated SPECT, AF demonstrated additional significant incremental value in predicting CD.
However, the direct pathophysiologic mechanism through which AF might be contributing to an increased CD rate remains to be determined. Although prior observations suggest that the presence of AF may be associated with the development of an occult or clinically unrecognized cardiomyopathy (44) and/or heart failure (10), our findings that AF was of prognostic importance even after LVEF was considered suggest that this mechanism alone does not explain the expanded risk. Many other possible explanations besides AF-induced tachycardia could potentially help explain the heightened cardiovascular risk in such patients, including enhanced neurohumoral stimulation (45), hypercoaguability (46), the propensity of AF patients to have a higher frequency of serious ventricular arrhythmias (47), increased inflammation (48), a direct AF-induced reduction in coronary flow reserve (49), and AF-induced endothelial dysfunction (50).
Clinical implications.
Our findings indicate that the presence or absence of AF should be considered when interpreting the results of MPS. Previous studies have demonstrated that patients with normal MPS have a <1% annual risk of cardiac events (16); however, the intermediate risk observed in our AF patients with normal MPS studies suggests that such patients should be followed more closely than the average normal MPS patient, including scrutiny for all modifiable coronary artery disease risk factors and/or consideration of measures that may maximally preserve left ventricular function. In a similar vein, there are other normal MPS patients with intermediate risk for events, including diabetics (51), patients with a low resting LVEF (52), and patients in whom a normal MPS study is discordant with clinical data strongly suggestive of ischemia (53). These are also clinical subsets that deserve more scrutiny compared with the average patient with a normal MPS study.
Our findings apply similarly for the management of AF patients with mildly abnormal MPS studies. While we have previously observed that patients with mildly abnormal MPS (2–8) are at low risk for CD (14), this was not the case in our AF cohort with these findings; the CD rate was 6.3% per year in the AF patients with mildly abnormal MPS compared with 1.2% per year in the non-AF group with these MPS findings. Whether this means that patients with AF and mild ischemic abnormalities constitute a group more deserving of early referral to cardiac catheterization is a question warranting further study.
Study limitations.
Our results are based on a population referred for nuclear testing and may not be applicable to a broader population. Although all data were collected and entered prospectively, the study is retrospective. Ventricular function was assessed in only 26.4% of the study population, as gated MPS was not routinely performed in our laboratory until 1995. However, there was no significant difference in any observed variables in the patients studied with gated SPECT compared with the overall study population.
In our study, we presume that the majority of our patients with left ventricular dysfunction had either hypertensive or ischemic cardiomyopathy, because we intentionally excluded patients with clinically apparent cardiomyopathies or valvular disease, as done in other studies (54). However, we cannot exclude that some patients had undetected valvular disease. Regardless, our findings indicate that the presence of AF predicted worse outcomes among patients stratified by summed rest defect score, a measure of the degree of left ventricular scarring, and according to LVEF levels.
While the accuracy of gated SPECT LVEF is not as high in AF as in patients without AF, good correlations with LVEF and other modalities has been demonstrated (55,56).
Although our total sample size was quite large, the overall percentage of patients with AF was relatively low, constituting only 2.4% of our patient population. Thus, additional study involving more AF patients in the follow-up would be useful, to assess the robustness of observations noted in our study. Nevertheless, prevalence of AF in our population by age was pretty similar to this in general population.
Finally, this study is based on data from a single nuclear cardiology center. Future study in this area may, thus, benefit from pooling the data from multiple medical centers, to increase the sample size of AF patients.
Conclusions.
Our findings indicate that the patients with AF warrant special consideration with respect to prognostic assessment using MPS. The presence of AF independently increases the risk of cardiac events over perfusion and function variables. Patients with AF have a high risk of CD, even when MPS is only mildly abnormal. Whether patients with AF and mildly abnormal MPS constitute a group more deserving of early referral to cardiac catherization is a question warranting further study.
|
Footnotes
|
|---|
Supported, in part, by a grant from Bristol-Myers Squibb Medical Imaging and a grant from Fujisawa Heathcare. Dr. Abidov is a Save a Heart Foundation Research Fellow at Cedars-Sinai Medical Center, Los Angeles, California. Dr. Kim Williams acted as the guest editor for this paper.
|
References
|
|---|
1. Fuster V, Ryden LE, Asinger RW, et al. ACC/AHA/ESC guidelines for the management of patients with atrial fibrillation: executive summaryA report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines and Policy Conferences (Committee to Develop Guidelines for the Management of Patients with Atrial Fibrillation): developed in collaboration with the North American Society of Pacing and Electrophysiology. J Am Coll Cardiol 2001;38:1231-1266.[Free Full Text]
2. Flegel KM, Shipley MJ, Rose G. Risk of stroke in non-rheumatic atrial fibrillation Lancet 1987;1:526-529.[CrossRef][Medline]
3. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham study Stroke 1991;22:983-988.[Abstract/Free Full Text]
4. Furberg CD, Psaty BM, Manolio TA, Gardin JM, Smith VE, Rautaharju PM. Prevalence of atrial fibrillation in elderly subjects (the Cardiovascular Health study) Am J Cardiol 1994;74:236-241.[CrossRef][Medline]
5. Feinberg WM, Cornell ES, Nightingale SD, et al. Relationship between prothrombin activation fragment F1.2 and international normalized ratio in patients with atrial fibrillation: Stroke Prevention in Atrial Fibrillation investigators Stroke 1997;28:1101-1106.[Abstract/Free Full Text]
6. Tsang TS, Petty GW, Barnes ME, et al. The prevalence of atrial fibrillation in incident stroke cases and matched population controls in Rochester, Minnesota: changes over three decades J Am Coll Cardiol 2003;42:93-9100.[Abstract/Free Full Text]
7. Kannel WB, Abbott RD, Savage DD, McNamara PM. Coronary heart disease and atrial fibrillation: the Framingham study Am Heart J 1983;106:389-396.[CrossRef][Medline]
8. Psaty BM, Manolio TA, Kuller LH, et al. Incidence of and risk factors for atrial fibrillation in older adults Circulation 1997;96:2455-2461.[Abstract/Free Full Text]
9. Krahn AD, Manfreda J, Tate RB, Mathewson FA, Cuddy TE. The natural history of atrial fibrillation: incidence, risk factors, and prognosis in the Manitoba follow-up study Am J Med 1995;98:476-484.[CrossRef][Medline]
10. Dries DL, Exner DV, Gersh BJ, Domanski MJ, Waclawiw MA, Stevenson LW. Atrial fibrillation is associated with an increased risk for mortality and heart failure progression in patients with asymptomatic and symptomatic left ventricular systolic dysfunction: a retrospective analysis of the SOLVD trials: Studies Of Left Ventricular Dysfunction J Am Coll Cardiol 1998;32:695-703.[Abstract/Free Full Text]
11. Berman DS, Hachamovitch R, Kiat H, et al. Incremental value of prognostic testing in patients with known or suspected ischemic heart disease: a basis for optimal utilization of exercise technetium-99m sestamibi myocardial perfusion single-photon emission computed tomography J Am Coll Cardiol 1995;26:639-647.[Abstract]
12. Berman DS, Hachamovitch R. Risk assessment in patients with stable coronary artery disease: incremental value of nuclear imaging J Nucl Cardiol 1996;3:S41-9.[CrossRef][Medline]
13. Berman D, Hachamovitch R, Lewin H, Friedman J, Shaw L, Germano G. Risk stratification in coronary artery disease: implications for stabilization and prevention Am J Cardiol 1997;79:10-16.[Medline]
14. Hachamovitch R, Berman DS, Shaw LJ, et al. Incremental prognostic value of myocardial perfusion single photon emission computed tomography for the prediction of cardiac death: differential stratification for risk of cardiac death and myocardial infarction Circulation 1998;97:535-543.[Abstract/Free Full Text]
15. Shaw LJ, Hachamovitch R, Berman DS, et al. The economic consequences of available diagnostic and prognostic strategies for the evaluation of stable angina patients: an observational assessment of the value of precatheterization ischemia J Am Coll Cardiol 1999;33:661-669.[Abstract/Free Full Text]
16. Gibbons RJ, Chatterjee K, Daley J, et al. ACC/AHA/ACP-ASIM guidelines for the management of patients with chronic stable angina: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Management of Patients With Chronic Stable Angina) J Am Coll Cardiol 1999;33:2092-2197.[Free Full Text]
17. Berman DS, Kang X, Hayes SW, et al. Adenosine myocardial perdusion SPECT in women compared to men: impact of diabetes mellitus on incremental prognostic value and effect on patient management J Am Coll Cardiol 2003;41:1125-1133.[Abstract/Free Full Text]
18. Ladenheim ML, Pollock BH, Rozanski A, et al. Extent and severity of myocardial hypoperfusion as predictors of prognosis in patients with suspected coronary artery disease J Am Coll Cardiol 1986;7:464-471.[Abstract]
19. Staniloff HM, Forrester JS, Berman DS, Swan HJ. Prediction of death, myocardial infarction, and worsening chest pain using thallium scintigraphy and exercise electrocardiography J Nucl Med 1986;27:1842-1848.[Abstract/Free Full Text]
20. Berman DS, Kiat H, Friedman JD, et al. Separate acquisition rest thallium-201/stress technetium-99m sestamibi dual-isotope myocardial perfusion single-photon emission computed tomography: a clinical validation study J Am Coll Cardiol 1993;22:1455-1464.[Abstract]
21. Matzer L, Kiat H, Van Train K, et al. Quantitative severity of stress thallium-201 myocardial perfusion single-photon emission computed tomography defects in one-vessel coronary artery disease Am J Cardiol 1993;72:273-279.[CrossRef][Medline]
22. Amanullah AM, Berman DS, Kiat H, Friedman JD. Usefulness of hemodynamic changes during adenosine infusion in predicting the diagnostic accuracy of adenosine technetium-99m sestamibi single-photon emission computed tomography (SPECT) Am J Cardiol 1997;79:1319-1322.[CrossRef][Medline]
23. Van Train KF, Areeda J, Garcia EV, et al. Quantitative same-day rest-stress technetium-99m-sestamibi SPECT: definition and validation of stress normal limits and criteria for abnormality J Nucl Med 1993;34:1494-1502.[Abstract/Free Full Text]
24. Germano G, Kavanagh PB, Su HT, et al. Automatic reorientation of three-dimensional, transaxial myocardial perfusion SPECT images (see comments) J Nucl Med 1995;36:1107-1114.[Abstract/Free Full Text]
25. Sharir T, Germano G, Kang X, et al. Prediction of myocardial infarction versus cardiac death by gated myocardial perfusion SPECT: risk stratification by the amount of stress-induced ischemia and the poststress ejection fraction J Nucl Med 2001;42:831-837.[Abstract/Free Full Text]
26. Hachamovitch R, Hayes SW, Friedman JD, Cohen I, Berman DS. Comparison of the short-term survival benefit associated with revascularization compared with medical therapy in patients with no prior coronary artery disease undergoing stress myocardial perfusion single photon emission computed tomography Circulation 2003;107:2900-2907.[Abstract/Free Full Text]
27. Berman DS, Abidov A, Kang X, et al. Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation. J Nucl Cardiol. In Press..
28. Germano G, Kiat H, Kavanagh PB, et al. Automatic quantification of ejection fraction from gated myocardial perfusion SPECT J Nucl Med 1995;36:2138-2147.[Abstract/Free Full Text]
29. Multiple Risk Factor Intervention Trial Research Group Relationship between baseline risk factors and coronary heart disease and total mortality in the Multiple Risk Factor Intervention Trial Prev Med 1986;15:254-273.[CrossRef][Medline]
30. Knuiman MW, Vu HT. Prediction of coronary heart disease mortality in Busselton, Western Australia: an evaluation of the Framingham, national health epidemiologic follow up study, and WHO ERICA risk scores J Epidemiol Community Health 1997;51:515-519.[Abstract/Free Full Text]
31. Saksena S, Domanski MJ, Benjamin EJ, et al. Report of the NASPE/NHLBI round table on future research directions in atrial fibrillation Pacing Clin Electrophysiol 2001;24:1435-1451.[CrossRef][Medline]
32. Stewart S, MacIntyre K, Chalmers JW, et al. Trends in case-fatality in 22,968 patients admitted for the first time with atrial fibrillation in Scotland, 1986 to 1995 Int J Cardiol 2002;82:229-236.[CrossRef][Medline]
33. Lake FR, Thompson PL. Prevention of embolic complications in nonvalvular atrial fibrillation in the elderly Drugs Aging 1991;1:458-466.[Medline]
34. Crenshaw BS, Ward SR, Granger CB, Stebbins AL, Topol EJ, Califf RM. Atrial fibrillation in the setting of acute myocardial infarction: the GUSTO-I experience: Global Utilization of Streptokinase and TPA for Occluded Coronary Arteries J Am Coll Cardiol 1997;30:406-413.[Abstract]
35. Middlekauff HR, Stevenson WG, Stevenson LW. Prognostic significance of atrial fibrillation in advanced heart failure: a study of 390 patients Circulation 1991;84:40-48.[Abstract/Free Full Text]
36. Nielsen JC. Mortality and incidence of atrial fibrillation in paced patients J Cardiovasc Electrophysiol 2002;13:S17-22.[Medline]
37. Ryan S, Siemon G, Drogemuller A, et al. [2 year follow-up of 321 patients with an implantable cardioverter/defibrillator: comparison of patients with and without atrial fibrillation] Z Kardiol 2001;90:906-915.[CrossRef][Medline]
38. Carson PE, Johnson GR, Dunkman WB, Fletcher RD, Farrell L, Cohn JN. The influence of atrial fibrillation on prognosis in mild to moderate heart failure: the V-HeFT studies: the V-HeFT VA cooperative studies group Circulation 1993;87:VI102-10.[Medline]
39. Crijns HJ, Van den Berg MP, Van Gelder IC, Van Veldhuisen DJ. Management of atrial fibrillation in the setting of heart failure Eur Heart J 1997;18(Suppl C):C45-9.[Medline]
40. Mahoney P, Kimmel S, DeNofrio D, Wahl P, Loh E. Prognostic significance of atrial fibrillation in patients at a tertiary medical center referred for heart transplantation because of severe heart failure Am J Cardiol 1999;83:1544-1547.[CrossRef][Medline]
41. Wilhelmsen L, Rosengren A, Lappas G. Hospitalizations for atrial fibrillation in the general male population: morbidity and risk factors J Intern Med 2001;250:382-389.[CrossRef][Medline]
42. Benjamin EJ, Wolf PA, D'Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart study Circulation 1998;98:946-952.[Abstract/Free Full Text]
43. Abidov A, Hachamovitch R, Hayes SW, et al. Prognostic impact of hemodynamic response to adenosine in patients older than age 55 years undergoing vasodilator stress myocardial perfusion study Circulation 2003;107:2894-2899.[Abstract/Free Full Text]
44. Shinbane JS, Wood MA, Jensen DN, Ellenbogen KA, Fitzpatrick AP, Scheinman MM. Tachycardia-induced cardiomyopathy: a review of animal models and clinical studies J Am Coll Cardiol 1997;29:709-715.[Abstract]
45. Ertl G, Wichmann J, Kaufmann M, Kochsiek K. Alpha-receptor constriction induced by atrial fibrillation during maximal coronary dilatation Basic Res Cardiol 1986;81:29-39.[CrossRef][Medline]
46. Feng D, D'Agostino RB, Silbershatz H, et al. Hemostatic state and atrial fibrillation (the Framingham offspring study) Am J Cardiol 2001;87:168-171.[CrossRef][Medline]
47. Ehrlich JR, Nattel S, Hohnloser SH. Atrial fibrillation and congestive heart failure: specific considerations at the intersection of two common and important cardiac disease sets J Cardiovasc Electrophysiol 2002;13:399-405.[CrossRef][Medline]
48. Frustaci A, Chimenti C, Bellocci F, Morgante E, Russo MA, Maseri A. Histological substrate of atrial biopsies in patients with lone atrial fibrillation Circulation 1997;96:1180-1184.[Abstract/Free Full Text]
49. Kochiadakis GE, Skalidis EI, Kalebubas MD, et al. Effect of acute atrial fibrillation on phasic coronary blood flow pattern and flow reserve in humans Eur Heart J 2002;23:734-741.[Abstract/Free Full Text]
50. Takahashi N, Ishibashi Y, Shimada T, et al. Impaired exercise-induced vasodilatation in chronic atrial fibrillation—role of endothelium-derived nitric oxide Circ J 2002;66:583-588.[CrossRef][Medline]
51. Kang X, Berman DS, Lewin HC, et al. Incremental prognostic value of myocardial perfusion single photon emission computed tomography in patients with diabetes mellitus. Am Heart J 1999;138:1025–32..
52. Sharir T, Germano G, Kavanagh PB, et al. Incremental prognostic value of post-stress left ventricular ejection fraction and volume by gated myocardial perfusion single photon emission computed tomography Circulation 1999;100:1035-1042.[Abstract/Free Full Text]
53. Hachamovitch R, Berman DS, Kiat H, Cohen I, Friedman JD, Shaw LJ. Value of stress myocardial perfusion single photon emission computed tomography in patients with normal resting electrocardiograms: an evaluation of incremental prognostic value and cost-effectiveness Circulation 2002;105:823-829.[Abstract/Free Full Text]
54. Poornima IG, Miller TD, Christian TF, Hodge DO, Bailey KR, Gibbons RJ. Utility of myocardial perfusion imaging in patients with low-risk treadmill scores J Am Coll Cardiol 2004;43:194-199.[Abstract/Free Full Text]
55. Nichols K, Dorbala S, DePuey EG, Yao SS, Sharma A, Rozanski A. Influence of arrhythmias on gated SPECT myocardial perfusion and function quantification J Nucl Med 1999;40:924-934.[Abstract/Free Full Text]
56. Watkowska J, Gewirtz H, Tawakol A, Fishman AJ, Scott JA, Yasuda T. Assessment of left ventricular ejection fraction by Tc-99m-MIBI gated SPECT in patients with atrial fibrillation: comparison with two-dimensional echocardiography J Nucl Cardiol 2003;10:S24-S25.
This article has been cited by other articles:
|
|
|
|
 
A. Goette, A. Bukowska, D. Dobrev, J. Pfeiffenberger, H. Morawietz, D. Strugala, I. Wiswedel, F.-W. Rohl, C. Wolke, S. Bergmann, et al.
Acute atrial tachyarrhythmia induces angiotensin II type 1 receptor-mediated oxidative stress and microvascular flow abnormalities in the ventricles
Eur. Heart J.,
June 1, 2009;
30(11):
1411 - 1420.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. Nucifora, J. D. Schuijf, L. F. Tops, J. M. van Werkhoven, S. Kajander, J. W. Jukema, J. H.M. Schreur, M. W. Heijenbrok, S. A. Trines, O. Gaemperli, et al.
Prevalence of Coronary Artery Disease Assessed by Multislice Computed Tomography Coronary Angiography in Patients With Paroxysmal or Persistent Atrial Fibrillation
Circ Cardiovasc Imaging,
March 1, 2009;
2(2):
100 - 106.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. Sciagra, B. Sotgia, N. Boni, and A. Pupi
Assessment of the Influence of Atrial Fibrillation on Gated SPECT Perfusion Data by Comparison with Simultaneously Acquired Nongated SPECT Data
J. Nucl. Med.,
August 1, 2008;
49(8):
1283 - 1287.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Kirchhof, A. Auricchio, J. Bax, H. Crijns, J. Camm, H.-C. Diener, A. Goette, G. Hindricks, S. Hohnloser, L. Kappenberger, et al.
Outcome parameters for trials in atrial fibrillation: executive summary: Recommendations from a consensus conference organized by the German Atrial Fibrillation Competence NETwork (AFNET) and the European Heart Rhythm Association (EHRA)
Eur. Heart J.,
November 2, 2007;
28(22):
2803 - 2817.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Kirchhof, A. Auricchio, J. Bax, H. Crijns, J. Camm, H.-C. Diener, A. Goette, G. Hindricks, S. Hohnloser, L. Kappenberger, et al.
Outcome parameters for trials in atrial fibrillation: Recommendations from a consensus conference organized by the German Atrial Fibrillation Competence NETwork and the European Heart Rhythm Association
Europace,
November 1, 2007;
9(11):
1006 - 1023.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. W. Askew, T. D. Miller, D. O. Hodge, and R. J. Gibbons
The Value of Myocardial Perfusion Single-Photon Emission Computed Tomography in Screening Asymptomatic Patients With Atrial Fibrillation for Coronary Artery Disease
J. Am. Coll. Cardiol.,
September 11, 2007;
50(11):
1080 - 1085.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. C. Haigney
Reduced myocardial perfusion in atrial fibrillation: when the egg comes before the chicken
Eur. Heart J.,
September 2, 2007;
28(18):
2181 - 2182.
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Rozanski, H. Gransar, N. D. Wong, L. J. Shaw, R. Miranda-Peats, D. Polk, S. W. Hayes, J. D. Friedman, and D. S. Berman
Clinical Outcomes After Both Coronary Calcium Scanning and Exercise Myocardial Perfusion Scintigraphy
J. Am. Coll. Cardiol.,
March 27, 2007;
49(12):
1352 - 1361.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. S. Berman, R. Hachamovitch, L. J. Shaw, J. D. Friedman, S. W. Hayes, L. E.J. Thomson, D. S. Fieno, G. Germano, N. D. Wong, X. Kang, et al.
Roles of Nuclear Cardiology, Cardiac Computed Tomography, and Cardiac Magnetic Resonance: Noninvasive Risk Stratification and a Conceptual Framework for the Selection of Noninvasive Imaging Tests in Patients with Known or Suspected Coronary Artery Disease
J. Nucl. Med.,
July 1, 2006;
47(7):
1107 - 1118.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. Kang, L. J. Shaw, S. W. Hayes, R. Hachamovitch, A. Abidov, I. Cohen, J. D. Friedman, L. E.J. Thomson, D. Polk, G. Germano, et al.
Impact of Body Mass Index on Cardiac Mortality in Patients With Known or Suspected Coronary Artery Disease Undergoing Myocardial Perfusion Single-Photon Emission Computed Tomography
J. Am. Coll. Cardiol.,
April 4, 2006;
47(7):
1418 - 1426.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
Top
Abstract
Methods