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J Am Coll Cardiol, 2002; 40:1735-1743 © 2002 by the American College of Cardiology Foundation |
* Cardiac PET Centre, Divisions of Cardiology and Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
Divisions of Cardiology and Cardiac Surgery, Toronto Hospital Western and General Divisions and St. Michael’s Hospital, University of Toronto Health Sciences Network, Toronto, Ontario, Canada
E. S. Garnett Memorial PET Centre, Department of Radiology, McMaster University and Department of Nuclear Medicine, Hamilton Health Sciences, Hamilton, Ontario, Canada
Divisions of Cardiology and Cardiac Surgery, McMaster University, Hamilton, Ontario, Canada
|| Division of Cardiology, London Health Sciences Centre, London, Ontario, Canada
¶ Cardiovascular Outcomes Related to Economics Group, Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
Manuscript received February 15, 2002; revised manuscript received May 24, 2002, accepted June 24, 2002.
* Reprint requests and correspondence: Dr. Rob S. B. Beanlands, University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, Ontario K1Y 4W7, Canada.
rbeanlands{at}ottawaheart.ca
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Abstract |
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BACKGROUND: F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to define viable myocardium in patients with coronary artery disease (CAD) and severe LV dysfunction and to guide revascularization decisions. Whether this approach improves clinical outcomes has not been tested in a randomized trial. Before doing so, an objective model for prediction of recovery is required.
METHODS: A total of 82 patients with CAD and an ejection fraction (EF) 
35% had FDG PET perfusion imaging before revascularization. Complete follow-up was available on 70 patients (86%). Patients had radionuclide angiograms at baseline and three months post-revascularization.
RESULTS: Diabetes (p = 0.029), time to operation (p = 0.008), and scar score (p = 0.001) were significant independent predictors of the change in EF. Previous coronary artery bypass graft confounded the effect of age. There was a significant interaction between the perfusion tracer used and mismatch score (p = 0.02). The multivariable prediction model incorporating PET and clinical variables had a goodness of fit with p = 0.001. Across tertiles of scar scores (I, small: 0% to 16%; II, moderate: 16% to 27.5%; III, large: 27.5% to 47%), the changes in EFs were 9.0 ± 1.9%, 3.7 ± 1.6%, and 1.3 ± 1.5% (p = 0.003: I vs. III), respectively.
CONCLUSIONS: In patients with severe LV dysfunction, the amount of scar was a significant independent predictor of LV function recovery after revascularization. A combination of PET and clinical parameters predicts the degree of recovery. This model is being applied in a large randomized controlled trial to determine the effectiveness of therapy guided by FDG PET.
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Retrospective and cohort studies suggest that the use of FDG positron emission tomography (PET) imaging may have impact on patient outcome (2,5,7,20). However, there are no large prospective randomized controlled trials that focus on patients with severe LV dysfunction, the group of patients where FDG PET may have the greatest benefit. Recent data have shown that FDG PET imaging can impact clinical decision making (6,20–25). However, the integration of both viability and clinical parameters may be helpful in optimizing therapy decisions for patients with severe LV dysfunction. Before undertaking a randomized controlled trial of FDG PET on patient outcome, it is necessary to understand what degree of viability will predict what level of LV function recovery, and how integration with other clinical parameters will affect the prediction of recovery.
The aim of this study was to determine whether the extent of viable or scarred myocardium is important in the level of recovery of LV function among patients with severe coronary disease and severe LV dysfunction, and to develop a model that incorporates viability and clinical parameters for predicting the degree of recovery after revascularization that could be tested in a similar patient population in a prospective randomized controlled trial.
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35% by any quantitative technique, who were being scheduled for revascularization. Excluded were those patients with myocardial infarction within the preceding six weeks, severe valve disease requiring valve replacement, requirement for aneurysm resection, and inability to obtain informed consent. Patients had evaluation of myocardial viability at the Cardiac PET Centre of the University of Ottawa Heart Institute (64 patients enrolled from eastern Ontario, northern Ontario, and western Quebec) or the E. S. Garnett Memorial PET Centre of McMaster University (18 patients enrolled from southwestern Ontario at McMaster University Medical Centre, University of Toronto Health Sciences Network, and London Health Sciences Centre). After providing informed consent, patients underwent baseline perfusion imaging and FDG PET. Clinical assessment of functional class, radionuclide angiography (RNA), quality of life questionnaire, and 6-min walk test were done within two weeks of the FDG PET scan, before revascularization. The RNA was repeated after three months of follow-up. The absolute change in EF between baseline and follow-up was then determined.
Evaluation of functional capacity and quality of life
Canadian Cardiovascular Society angina classification and New York Heart Association dyspnea classification were recorded based on patient self-report. The Minnesota Living with Heart Failure questionnaire was used to assess health-related quality of life (26,27). Functional score using this approach ranges from 0 (good) to 105 (poor). Submaximal exercise capacity was determined using the 6-min walk test as a safe and objective means for evaluating the functional status of patients (28,29).
RNA
The RNAs were acquired using a standard electrocardiogram-gated equilibrium blood pool imaging protocol, using Tc-99m-labelled red blood cells (6,13–15,17,22). The EF was measured from the left anterior oblique 45° acquisition. The RNA analysis was performed in a central core laboratory by observers (R.M. Iwanochko and R. J. Burns, Toronto Hospital) who were blind to any clinical or imaging data (30,31).
FDG PET
A standardized protocol for static FDG PET imaging was used. Patients without diabetes were studied in the postprandial state after a 50-g oral glucose load. Those patients with diabetes or glucose intolerance also received insulin according to a standardized protocol (2,4–6,14,15,22,32,33).
Initial PET transmission scanning was performed to allow for attenuation correction. A 30-min emission scan was acquired starting 40 min after injection of 75 to 370 MBq of FDG in a whole-body PET camera (CTI/Siemens ECAT ART, Knoxville, Tennessee). The FDG uptake images were reconstructed using a Hann window cutoff at 0.2 cycles/pixel.
Perfusion imaging
Where PET perfusion imaging was available, this was performed using either N-13 ammonia or rubidium-82. Both of these have been used routinely in previous studies for perfusion imaging, often interchangeably (2,4,5,7,13–15,32,34,35). A dose of 370 to 740 MBq of rubidium-82 or 300 to 370 MBq of N-13 ammonia was administered intravenously. Standard perfusion imaging acquisition protocols were used (32,36–41). Sites without the capability of PET perfusion imaging performed Tc-99m sestamibi single photon emission computed tomography (SPECT) to assess perfusion (4,6,17,22,42). A dose of 740 MBq of Tc-99m sestamibi was administered intravenously, followed 1 h later by standard 30-min 180° SPECT imaging protocol. All patients were studied under resting conditions. The SPECT images were reconstructed with the same resolution as the PET images and transferred to the PET computer system for analysis.
Perfusion/FDG image data analysis
The heart was divided into 460 sectors by an automated analysis program (41,43). For each sector, tracer activity was expressed as a percentage of the maximum uptake for the patient’s myocardium. The sum of the percentages of all sectors, corrected for the maximum possible score (maximum score: 46,000 = 460 sectors x 100%), defined the total "raw perfusion" score and the total "raw FDG" score, respectively. To normalize the FDG to perfusion, sectors that were 
80% of the maximum perfusion were defined as normal. FDG uptake was then normalized by scaling the percent FDG uptake to be equal to perfusion in the "maximum zone" sectors. After this normalization, sectors with a value >100% FDG were assigned a value of 100%. The sums of all sectors were calculated again to define the total "normalized perfusion" and the total "normalized FDG" uptake, respectively.
Tissue characterization
In the "abnormal" sectors with <80% perfusion, the extent and severity of hibernating myocardium ("mismatch" score) and "scar" score could be defined as a percentage of the total LV myocardium. Thus, for abnormal sectors:
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In sectors with FDG < perfusion, the perfusion score was substituted for the FDG score in the above equation.
In sectors with FDG > perfusion:
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The score parameters consider the combination of extent and severity. Using this method, sectors with reduced perfusion can have a mixture of both mismatch and scar scores. The sum of perfusion, mismatch, and scar scores for any abnormal sector equals 100%. To evaluate size alone, the number of sectors defined as scar or mismatch were considered. Figure 1 shows examples of the polar maps to determine scar and mismatch scores in three patients.
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Multivariable analyses were performed by using stepwise multiple regression methods. The criterion for possible inclusion in the multivariable model were univariable p value <0.2. The criterion for remaining in the multivariable model was an adjusted p value <0.05. Goodness of fit was based on assessment of overall model fit and graphic comparison of observed versus adjusted values residual plots to identify outlying or influential observations or systematic patterns (46). To illustrate the association between scar score and the absolute change in EF on RNA between baseline and post-revascularization follow-up, the scar score was compared across tertiles. Interaction terms were considered. Thus, an indicator variable was included in the model to account for the potential differences in perfusion tracer, as the use of PET or SPECT tracers could potentially affect the degree of scar or mismatch defined (8,47,48) (tracer variable = 1 for PET, 0 for SPECT perfusion).
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Discussion |
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Scar, mismatch, and LV function recovery. In the current study, quantification of the amount of scar was an independent predictor of improvement in LV function following revascularization in patients with severe LV dysfunction. Previous studies have focused on the importance of mismatch (or hibernating myocardium) in predicting recovery of function and improved outcome (2,4,5,7,10,14,18). The current study indicates that scar should also be considered and that PET parameters should be considered in the clinical context.
Recent data from Pagano et al. and reports from Bax et al. suggest that the number of viable dysfunctional segments and the amount of mismatch are important for predicting symptom and LV function recovery (19,49,50). In the study by Pagano et al. (19) the number of viable segments, defined by the rate of myocardial glucose utilization, was associated with the change in EF (r = 0.65). Unlike many previous studies, Pagano et al. (19) defined viability independent of perfusion. The current study showed similar results in that the degree of scar (the corollary of the Pagano et al. (19) glucose utilization) was a significant independent predictor of LV function recovery. However, it also showed that a prediction model that includes PET parameters (scar and mismatch) and clinical parameters can be used to estimate the degree of recovery of LV function following revascularization.
Others have also recently focused on the extent of scar. In a cohort study, Haas et al. (20) used 50% FDG uptake as cutoff for viability and defined an extent of scar >40% as unlikely to recover with revascularization. Using FDG PET to risk stratify with these cutoffs yielded improved postoperative and long-term outcomes compared to an approach that did not utilize FDG PET (20). Further evidence on the importance of the total extent of viability comes from subsequent studies by Haas et al. (51,52). These data indicate that dysfunctional regions with normal perfusion (repetitively stunned myocardium) are more common than mismatch (70% vs. 24% of dysfunctional segments). In addition such "normal perfusion zones" have less associated tissue injury and are more likely to demonstrate complete recovery than mismatch segments (31% vs. 18%, respectively) (51,52).
Retrospective studies evaluating FDG PET and long-term patient outcome (2,5–7,12,35) suggest that FDG PET potentially identifies high-risk patients that may benefit from revascularization. Other studies have shown that FDG PET is helpful in decision making for revascularization (22–24), but it is difficult, without prospective randomized controlled trials, to know whether therapy decisions are indeed appropriate and impact on outcome. One randomized controlled trial has compared FDG PET guided therapy to Tc-99m-sestamibi guided therapy (53). Although there were minor trends, this study showed no significant difference in outcome. However, the study focused on a lower risk group of patients (with mostly moderate LV dysfunction), used only FDG mismatch to define viability in hypoperfused segments, and was too small to detect important differences (54). One of the aims of the current study was the identification of a prediction model that incorporates PET and clinical parameters and could be applied in a prospective randomized controlled trial of patients with severe LV dysfunction—the patient population who will most likely benefit from viability detection.
Utility of the prediction model
In this study, several clinical parameters had impact on LV function recovery. The presence of higher risk parameters, including diabetes, age, and previous revascularization, increased the degree of recovery. At first glance, this may appear paradoxical. However, other studies (55) have shown that the patients at greatest risk are often those who gain the most benefit from treatment. It is possible that older patients and those with previous bypass have better developed collateral flow, which could facilitate recovery. Alternatively, a selection bias may have occurred whereby subjective criteria for revascularization decisions are stricter with the higher risk patients. In the case of diabetes, it may also indicate that the level of viable myocardium is greater than apparent on imaging because patients with diabetes tend to be more difficult to image (56–60). Examples of applying the model to three patients in the current study who had small, moderate, and large scar areas are shown in Figure 1.
Study limitations
Some limitations of this approach are worth considering. These primarily relate to the patient population in this study, which included patients who were predominantly men, predominately between 53 and 71 years of age (1 standard deviation from the mean), had multivessel disease, and had bypassable vessels. The generalizability of the prediction model to a wider patient population is being tested in a large randomized controlled trial (PET and Recovery following Revascularization–Phase 2 [PARR-2]).
The current study was intended as a pilot study to establish the prediction model for subsequent trials. The sample size was larger than many PET studies evaluating recovery of function. However, the sample size limited the parameters that could be included in the prediction model. Thus, some potentially important parameters, such as stress-induced ischemia and ventricular volumes, were not included. The number of parameters used simplifies the model for research and clinical use but also means that prediction rule for a given patient must be taken in the context of clinical judgment and image interpretation. The impact of some of these variables on the model will be tested in the larger ongoing randomized control trial.
The use of both PET and SPECT perfusion agents in the current study represents the clinical reality, as many institutions do not have easy access to PET perfusion agents. This may account for the lack of significance of the independent mismatch term. To account for potential differences in mismatch due to the type of perfusion tracer (PET or SPECT), an interaction variable (tracer variable) was used in the model. When PET perfusion was used, the overall effect of mismatch was for a predicted increase in EF. When SPECT perfusion was used, the mismatch effect was not significant and the confidence limits of the point estimate overlap 0. Thus, interpretation of the effect of mismatch with SPECT perfusion must be done cautiously. This limitation of the model likely reflects the small sample size. Ongoing clinical trials will allow evaluation of each PET variable with greater accuracy.
Although improvement in LV function has been noted at three months of follow-up in many previous studies, recent data suggest that more recovery may be observed with longer follow-up time (51,52,61). The impact of follow-up time on the model could not be addressed in the current study but will be considered in the larger ongoing randomized control trial that will follow up patients up to two years.
It could be argued the angiographic data should be included in the model. Clearly this is an important parameter in decision making. However, patients in this study did have suitable anatomy for revascularization. If they did not, they were not generally considered for revascularization and the model would not be applicable. Second, many patients referred for PET (about 50% in our institution) have not had a recent angiogram. Therefore, coronary anatomy data would not always be available to incorporate into the model. The prospective randomized controlled trial (PARR-2) includes patients who have as well as those who have not had recent angiography before the PET study: so the model can be validated in this patient population.
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These data set the framework for larger scale future studies evaluating the application of FDG PET. The prediction model is now being applied in a large multicenter randomized controlled trial (PARR-2) to determine the clinical outcome and cost-effectiveness of therapy guided by FDG PET. Appendix
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APPENDIX |
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Core labs. PET and perfusion imaging: University of Ottawa Heart Institute (R. deKemp, M. Aung); RA (R. Burns, R. M. Iwanochko, R. Burke).
Statistical analysis. CORE Group, Department of Epidemiology and Community Medicine, University of Ottawa (G. Nichol, J. Blackburn).
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Footnotes |
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