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Circulation. 1995;91:54-65

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(Circulation. 1995;91:54-65.)
© 1995 American Heart Association, Inc.

Comparison of Cost-Effectiveness and Utility of Exercise ECG, Single Photon Emission Computed Tomography, Positron Emission Tomography, and Coronary Angiography for Diagnosis of Coronary Artery Disease

Randolph E. Patterson, MD; Robert L. Eisner, PhD; Steven F. Horowitz, MD

From the Carlyle Fraser Heart Center, Emory:Crawford Long Hospital, Department of Medicine, Division of Cardiology and Department of Radiology, Emory University School of Medicine, Atlanta, Ga, and Thomas Killip Division of Cardiology, Department of Medicine, Beth Israel Medical Center, Albert Einstein College of Medicine, New York, NY (S.F.H.).

Correspondence to Randolph E. Patterson, MD, Professor of Medicine (Cardiology) and Radiology, Emory University School of Medicine, Carlyle Fraser Heart Center, Emory:Crawford Long Hospital, 550 Peachtree St, NE, Atlanta, GA 30365.


*    Abstract
up arrowTop
 *Abstract
down arrowIntroduction
 down arrowMethods
down arrowResults
down arrowDiscussion
down arrowAppendix B
 down arrowReferences
 
Background To compare cost-effectiveness and utility of four clinical algorithms to diagnose obstructive coronary atherosclerotic heart disease (CAD), we compared exercise ECG (ExECG), stress single photon emission computed tomography (SPECT), positron emission tomography (PET), and coronary angiography.

Methods and Results Published data and a straightforward mathematical model based on Bayes' theorem were used to compare strategies. Effectiveness was defined as the number of patients with diagnosed CAD, and utility was defined as the clinical outcome, ie, the number of quality-adjusted life years (QALY) extended by therapy after the diagnosis of CAD. Our model used published values for costs, accuracy, and complication rates of tests. Analysis of the model indicates the following results. (1) The direct cost (fee) for each test differs considerably from total cost per {Delta}QALY. (2) As pretest likelihood of CAD (pCAD) in the population increases, there is a linear increase in cost per patient tested but a hyperbolic decrease in cost per effect and cost per utility unit, ie, increased cost-effectiveness and decreased cost per utility unit. (3) At pCAD<0.70, analysis of the model indicates that stress PET is the most cost-effective test, with the lowest cost per utility, followed by SPECT, ExECG, and angiography, in that order. (4) Above a threshold value of pCAD of 0.70 (for example, middle-aged men with typical angina), proceeding directly to angiography as the first test showed the lowest cost per effect or utility. This quantitative model has the advantage of estimating a threshold value of pCAD (0.70) at which the rank order of cost-effectiveness and cost per utility unit change. The model also allows substitution of different values for any variable as a way to account for the uncertainties of clinical data, ie, changing costs, test accuracy and risk, etc. This procedure, called sensitivity analysis, showed that the rank order of cost-effectiveness did not change despite changes in several variables.

Conclusions (1) Estimation of total costs of diagnostic tests for CAD requires consideration not only of the direct cost of the test per se (eg, test fees) but also of the indirect and induced costs of management algorithms based on the test (eg, cost/{Delta}QALY). (2) It is essential to consider the clinical history (pCAD) when selecting the clinical algorithm to make a diagnosis with the lowest cost per effect or cost per utility unit. (3) Stress PET shows the lowest cost per effect or cost per utility unit in patients with pCAD<0.70. (4) Angiography shows the lowest cost per effect or cost per utility unit in patients with pCAD>0.70.


Key Words: cost-effectiveness • electrocardiography • imaging • angiography • coronary artery disease


*    Introduction
up arrowTop
 up arrowAbstract
 *Introduction
down arrowMethods
down arrowResults
down arrowDiscussion
down arrowAppendix B
 down arrowReferences
 
Positron emission tomography (PET) has greater sensitivity and specificity to diagnose obstructive coronary artery disease (CAD) than does single photon emission computed tomographic (SPECT) imaging or exercise ECG (ExECG).1 2 Despite the demonstration of greater accuracy of PET, it has not yet replaced other noninvasive approaches to detect CAD.

The limitation of PET is that it is more expensive than other noninvasive tests for CAD. One of the most difficult jobs facing medicine today is to objectively assess the cost relative to effectiveness or utility of health care.3 4 There is an especially urgent need to develop the lowest cost per effect for diagnostic approaches to CAD because the spiraling cost of cardiac care is estimated to be between 1% and 2% of the gross domestic product.5 Analysis of the utility of clinical approaches accounts for the impact of medical care on the quality as well as quantity of life.6 7 We previously reported a mathematical model to compare the cost-effectiveness of exercise ECG, planar thallium, and coronary angiography to diagnose CAD.8 The goal of this study is to use an updated version of that model to compare the cost per effect or cost per utility unit of PET and SPECT myocardial perfusion imaging with ExECG and angiography in different populations of patients. Gould et al9 also proposed a model to assess the cost-effectiveness of PET, and Gleason and Frick10 suggested improvements in that model.

Comparison of cost-effectiveness or cost per utility unit is complex because it needs to account for a wide variety of factors.3 4 5 6 7 8 9 10 The model must be able to account for the cost of diagnostic and therapeutic measures, including those that yield false-positive results and lead to unnecessary further testing, as well as those that yield false-negative results and lead to complications due to inadequate treatment of the disease. The present study used actual data that have been published in the literature about the tests, their complication rates, and their accuracy. The objective of the study was not to assess the impact of diagnostic tests or treatment of CAD on the welfare of society. This more complicated task represents cost-benefit analysis.11 12 Rather, the limited goal of this study is to compare the costs of different clinical diagnostic algorithms to achieve the same effect, outcome, or utility, eg, increasing the quantity and/or quality of life by the same amount. Our model addresses only the problem facing a physician who sees a patient with symptoms or a clinical situation that indicates a need for testing for possible CAD.


*    Methods
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 *Methods
down arrowResults
down arrowDiscussion
down arrowAppendix B
 down arrowReferences
 
A mathematical model8 was developed based on real data to estimate comparative cost-effectiveness of cost per utility unit of four different diagnostic approaches to CAD (Table 1Down) based on literature values for clinical variables (Table 2Down).


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  		Table 1. Clinical Algorithms to Use Diagnostic Tests for Coronary Artery Disease


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  		Table 2. Standard Parameters Used in Calculations

Effectiveness of Tests: Criteria
The most difficult issue in the assessment of cost-effectiveness is to develop criteria to define effectiveness of health care.5 6 7 8 9 10 13 For the purpose of this study, our first criterion for effectiveness of diagnostic tests was the ability to identify accurately a patient who has CAD when patients are selected for testing from a group presenting to a physician because of possible CAD.

The second criterion for the effectiveness of care attempts to account for several of the clinical variables that influence the outcome of management of CAD, ie, utility.4 5 11 Here, we developed criteria for utility of tests for CAD in terms of the clinical outcome for patients undergoing the tests, ie, an increase ({Delta}) in the number of quality-adjusted life years (QALY) for a patient over a 10-year follow-up period (Appendix A and Table 3Down).11 We multiplied the number of years of life extended by therapy (over a 10-year follow-up period) by the adjusted quality of life, expressed as a fraction of full quality of life (1.0). This criterion is imperfect, but it does account for most important clinical variables.12 13 It seemed reasonable for the purpose of this study because it must be emphasized that {Delta}QALY is used only as a common denominator to compare cost per utility unit of different diagnostic tests. Briefly, we assumed that the diagnosis of CAD increased the number of QALYs by 3 years over a 10-year follow-up period, based on available data.14 15 16 17 18 19 20 21 22 23 24 25 26 We limited this variable to a 10-year follow-up period because the natural history of patients treated with modern medical, percutaneous transluminal coronary angioplasty (PTCA), or coronary artery bypass graft surgery (CABG) therapies is not well known beyond 10 years. If therapies have a sustained long-term effect on the natural history of CAD, then the outcome of therapy ({Delta}QALY) might be more favorable than our analysis indicates.


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  		Table 3. Improvement in QALY' for Patients With Versus Without the Diagnosis of CAD: Calculations

Calculations of Cost Per Effect and Cost Per Utility Unit (Appendix B)
Total costs were calculated as direct costs (fees for tests) multiplied by the number of patients tested (as decided by physician's approach) plus the induced costs (the number of patients who had additional tests, eg, angiography), multiplied by the costs of complications produced by test procedures or by CAD per se.8 It was not necessary to estimate the dollar value of a human life lost to CAD or to complications of test procedures. Instead, mortalities were considered separately and were incorporated into the calculation of QALYs. We calculated (cost-effectiveness)-1 as cost (in dollars) per effect:


Effectiveness was defined as a patient with CAD diagnosed.8 11 Utility was defined as an increment in the number of QALYs added over a 10-year follow-up period.11 Although calculation of ratios of cost to effect can lead to erroneous conclusions, as pointed out by McKean,13 our analysis avoids these pitfalls by focusing on the cost-to-effect ratio in a defined population and by accounting for all patients whether or not they were diagnosed accurately. Calculation of direct and induced costs is outlined briefly here but with detailed equations below (Appendix B, using variables in Table 2Up). Total direct costs, therefore, were calculated as the fee for each test multiplied by the number of patients having the test and summed for all tests.8 The costs for each test were derived from literature values for fees rather than attempts to estimate incremental costs, with all of the uncertainties involved in that procedure.12 Furthermore, the number of patients who undergo a test is determined by the diagnostic approach, ie, strategy selected by the physician to decide indications for each test (Table 1Up), and thus becomes the most relevant consideration for physicians.

Cost Estimates
The induced costs of exercise tests arise from the complications and mortalities associated with each test, including subsequent testing indicated by the results of the first test, for example, angiography after a positive noninvasive test.8 Induced costs also include the complications and mortality associated with CAD that is not treated because of false-negative test results.27 28 Costs of complications are not easy to estimate, and it was necessary to combine actual data for this purpose. We assumed that the usual complication of each test or of CAD would be nonfatal myocardial (or cerebral, for angiography) infarction, requiring 1 week in the hospital and 2 months away from employment at a conservative average cost of $40 000 per complication. We adjusted the annual rate of nonfatal myocardial infarction in patients with CAD to estimate the rate of myocardial infarction in the subgroup of patients with false-negative exercise test results.27 28 Because we required that the patient achieve 85% of age-predicted maximal heart rate or a good hemodynamic response to pharmacological vasodilator stress to interpret the test as negative, many patients with false-negative tests would have good exercise capacity and therefore a low risk of nonfatal myocardial infarction or death.27 On the other hand, patients who had pharmacological rather than exercise stress would have no assessment of exercise capacity, which would have helped predict prognosis. Furthermore, only patients with mild to moderate symptoms are likely to avoid angiography despite negative exercise test results. Thus, we assumed a 20% rate of nonfatal myocardial infarction over 10 years in patients with CAD missed by false-negative exercise test results after adjustment for the good exercise tolerance or response to pharmacological dilation, mild to moderate symptoms, and low likelihood of left main or three-vessel CAD in patients with false-negative exercise test results.27 28

A modification of the equations of Bayes' theorem was used to calculate the number of patients having each test or experiencing the complications (Appendix B).29 30 31 These calculations were based on literature values of the sensitivities and specificities of each test1 2 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 (Table 2Up for values used in this study) and the rates of complications47 48 over the full range of pCAD.

Clinical Algorithms for Use of Diagnostic Tests
Using this model, we tested four straightforward diagnostic algorithms for CAD. The four algorithms represent simplified diagnostic approaches or algorithms for CAD (Table 1Up). For the first three algorithms, one of the three noninvasive stress tests is performed first, and the patients are referred for angiography only if the noninvasive test is positive or nondiagnostic. The three tests are ExECG, stress SPECT, and stress PET myocardial perfusion imaging. In the other approach, angiography is the first and only diagnostic test.

Data Analysis
Many assumptions are required in any model of cost-effectiveness (Tables 2Up and 3Up); therefore, we performed sensitivity analysis of the model by repeating calculations after changing the values of sensitivity, specificity, fees, and rates and costs of complications or mortalities (Tables 2Up and 4Down).8 In particular, the most difficult data to estimate accurately are the clinical outcomes, including rates of complications, and effects of different therapies on prognosis (Table 3Up).14 15 16 17 18 19 20 21 22 23 24 25 26 Fortunately, the impact of these uncertainties on the calculations in this analysis can be easily tested by changing {Delta}QALY. It must be emphasized that our analysis focused on comparing different clinical approaches to detection of CAD, and these hard-to-measure clinical variables can be accounted for by changing the value of {Delta}QALY to assess whether it changed the rank order of different approaches to diagnose CAD.


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  		Table 4. Changes in Parameters From Standard (S) to Test Their Impact on Cost Per Utility Unit*


*    Results
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
*Results
down arrowDiscussion
down arrowAppendix B
 down arrowReferences
 
Pretest Likelihood of CAD: Effect on Costs
First, these results show that the fee for a diagnostic test represents only one small component of the total costs incurred by using a management algorithm based on that diagnostic test (Tables 2Up and 3Up and Fig 1Down). Next, when the pretest likelihood of CAD (pCAD) increased in the population, the cost and mortality rate over a 10-year follow-up period also increased linearly (Fig 1Down). As the pCAD increased, there were hyperbolic decreases in cost per patient with CAD diagnosed and in cost per increased number of QALYs (Fig 1Down). Although the absolute cost and mortality rates increased with increasing pCAD, cost per effect and cost per utility unit improved. The hyperbolic relation between pCAD and cost per effect or cost per utility unit reveals the very high cost per effect or cost per utility unit at low pCAD and the dramatic differences among the different diagnostic algorithms at lower pCAD (Fig 1Down).



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  		Figure 1. Each of these four graphs shows the pretest likelihood (p) of coronary artery disease (CAD) (pCAD) increasing along the horizontal axis. The top left graph shows cost per patient (Pt) tested on the vertical axis for each diagnostic approach: exercise ECG (ExECG), single photon emission computed tomography (SPECT), positron emission tomography (PET), and angiography (Angio). Cost increases with pCAD for the noninvasive tests but not significantly for angiography. Noninvasive tests showed lower cost than angiography, with pCAD<0.5 but higher costs with pCAD>0.7. Cost in all cases is in $1000 units. The top right graph shows mortality over 10 years on the vertical axis for each diagnostic approach. The mortality rate increases as pCAD increases for the noninvasive tests but not significantly for angiography. Mortality increases most for ExECG and least for PET because more patients with CAD were missed by false-negative ExECG. The lower left graph shows cost per effect (cost per patient with CAD diagnosed [Dx] accurately) on the vertical axis. Note the hyperbolic decrease in cost per effect (inverse of cost-effectiveness) as pCAD increases. Cost per effect at low pCAD is highest for angiography and lowest for PET. ExECG shows higher cost per effect than SPECT. At higher pCAD, the noninvasive tests continue their same rank order of cost per effect: lowest for PET, followed by SPECT, and highest for ExECG. Angiography, however, shows the lowest cost per effect when pCAD is >0.70. The lower right graph shows cost per utility unit ({Delta}QALY, improvement in quality-adjusted life years due to diagnosis and treatment of CAD) on the vertical axis. The rank order of diagnostic approaches for cost per {Delta}QALY are quite similar to cost per effect (patient with CAD diagnosed). This similarity indicates that the model is robust and does not depend critically on the assumptions about the impact of care on the outcomes of patients with CAD.

Comparing Cost Per Effect and Cost Per Utility Unit for Clinical Algorithms
Comparing cost per effect and cost per utility unit of different clinical algorithms may be easiest to understand by using individual patient examples selected to represent varied pCAD (Fig 2Down). A population of 50-year-old men with no chest discomfort but with two risk factors would have a 10% pCAD, based on clinical data alone (pretest likelihood).30 31 In such patients, PET imaging shows the lowest cost per effect and cost per utility unit, followed by SPECT imaging and ExECG. Performing angiography first is the least cost-effective algorithm at the 10% pCAD. The rank order of cost per effect or cost per utility unit is the same for 50-year-old women with atypical chest discomfort (a 30% pCAD).30 31 For 40-year-old men with atypical chest discomfort (with a 50% pCAD),30 31 PET still maintains the lowest cost per effect or cost per utility unit of any algorithm. Performing angiography first has the highest cost per effect, but differences among policies are less dramatic. On the other hand, at 90% pCAD (typical angina in a 65-year-old woman),30 31 performing angiography first has the lowest cost per effect or cost per utility unit, and ExECG has the greatest cost per effect. Thus, not only the relative differences but, more importantly, the rank order of cost per effect or cost per utility unit of the four different algorithms changes as pCAD increases (Fig 2Down).



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  		Figure 2. Bar graphs showing cost per utility unit ({Delta}QALY) for the four different diagnostic approaches for four different pCADs, illustrated by four patient examples. Cost is in units of $1000. For pCAD of 0.1 to 0.5, cost per utility unit was lowest for PET; and for pCAD=0.9 (bottom right), cost per utility unit was lowest for angiography. For noninvasive stress tests at all pCAD, cost per utility unit was lowest for PET and highest for ExECG. ATCD indicates atypical chest discomfort; TAP, typical angina pectoris; and y/o, year old. Other abbreviations as in Fig 1Up.

Fig 3Down plots cost versus the mortality rate over 10 years for the same four patient examples cited above. For the 50-year-old man with no chest discomfort but with two risk factors (10% pCAD), PET has the lowest cost and has virtually the same low mortality as the approach based on angiography. The mortality rate of ExECG is highest. For the 50-year-old woman with atypical chest discomfort (30% pCAD), the rank order is the same, and angiography remains the most costly, followed by PET and SPECT in these patients. Finally, in 65-year-old women with typical angina pectoris (90% pCAD), performing coronary angiography first becomes least costly and has the lowest mortality. PET has the second lowest mortality rate and approaches that of performing angiography first, but it has the second lowest cost in these patients.



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  		Figure 3. Graphs showing total cost on each horizontal axis and mortality rate over a 10-year period on each vertical axis. The four graphs show four different values of pCAD, illustrated by patient examples. Different symbols represent the four different diagnostic approaches in each of the four graphs. The top left graph (pCAD=0.1) shows lowest mortality and lowest cost for PET, highest cost for angiography, and highest mortality for ExECG. The bottom left graph (pCAD=0.3) shows that angiography has the lowest mortality, followed closely by PET. PET has the lowest cost, and angiography has the highest cost. The top right graph (pCAD=0.5) shows that angiography has the lowest mortality and ExECG the highest. PET has the lowest cost and ExECG the highest. The bottom right graph (pCAD=0.9) shows that angiography has the lowest cost and the lowest mortality. ExECG has the highest cost and the highest mortality. PET has the lowest cost and lowest mortality of any of the noninvasive stress tests. Abbreviations as in previous figures.

Sensitivity Analysis: Changes in Assumptions Influence Cost Per Effect or Cost Per Utility Unit
Because valid data are not easy to acquire and several assumptions are involved in these calculations, we systematically changed parameters in the equations to test their impact on cost per effect or cost per utility unit (Table 4Up; Figs 4 and 5).8 We analyzed the effects of changes in the variables shown in Table 4Up on the basis of available literature.12 14 15 16 17 18 19 20 21 22 23 24 25 26 Fig 4Down shows graphs of cost per utility unit for standard parameters and changes A through H defined in Table 4Up. All changes are shown for each diagnostic approach considered separately, eg, ExECG, SPECT, PET, and angiography. In general, lowering the fee for a test decreases cost per utility unit ({Delta}QALY), and raising the fee for a test increases cost per utility unit. Lowering the accuracy of tests increases their cost per utility unit, and lowering the risk because of false-negative diagnostic tests decreases cost per utility unit slightly. As the pCAD rises from P=.2 to P=.8, overall cost per utility unit decreases, but there is little effect on the rank order of cost per utility unit due to various changes in parameters (A through H). More details are indicated in the legend to Fig 4Down. The greatest change occurred when the benefit of treatment ({Delta}QALY) was reduced.



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  		Figure 4. Bar graphs showing sensitivity analysis: comparisons of effects of changing parameters on cost per utility unit. Effects of changing parameters from standard (S) to A through H on calculated cost-effectiveness of each diagnostic approach. Each graph shows cost per utility unit (change in quality-adjusted life years, {Delta}QALY) on the vertical axis. The three graphs from left to right represent patients with probabilities of CAD that increase from left (P=.2) to right (P=.8). The four sets of graphs from top to bottom show data for ExECG, stress SPECT myocardial perfusion imaging, stress PET myocardial perfusion imaging, and coronary angiography. The standard parameters (S) are shown as the left, dark bar in each graph. Parameters are changed from A through H as shown in Table 4Up. If there was no change from the standard parameters, they are not shown in the table. Note that for ExECG, (A) lowering the fee for ExECG causes negligible change, but (C) lowering the fee for angiography decreases cost per {Delta}QALY (good effect). (F) Lowering the accuracy of ExECG increases the cost per {Delta}QALY (bad effect). (H) Reducing the benefit of therapy ({Delta}QALY) also increases cost per {Delta}QALY (bad effect). For SPECT, (B) lowering the fee for SPECT decreases cost per {Delta}QALY, but (C) lowering the fee for angiography has an even greater effect. (F) Lowering the specificity of SPECT to 0.54 increased cost per {Delta}QALY, and (H) lowering the benefit of treatment of CAD also increased cost per {Delta}QALY. For PET, (C) lowering the fee for angiography decreased the cost per {Delta}QALY, while (D) raising the fee for PET increased the cost per {Delta}QALY. (E,F) Lowering the accuracy of PET also increased the cost per {Delta}QALY, and (H) reducing the benefit of treatment ({Delta}QALY) also increased cost per {Delta}QALY. For angiography, (C) lowering the fee for angiography decreased cost per {Delta}QALY, and (H) reducing the benefit of treatment also increased cost per {Delta}QALY. Abbreviations as in previous figures.

Fig 5Down shows graphs of cost per utility unit for standard parameters and changes (A through H). All changes (A through H) are grouped to emphasize comparison of the rank order of different diagnostic approaches (ExECG, SPECT, PET, and angiography). In general, there was little impact of changes (A through H) on the rank order of different diagnostic approaches. Greatest impact on rank order occurred with selective changes in the fees or accuracy of a particular test, eg, SPECT and PET. The rank order of diagnostic approaches changed when the pCAD increased from 0.2 (noninvasive tests perform best) to 0.5 (less dramatic differences) to 0.8 (angiography performs best). Sensitivity analysis indicates that the model is robust because it shows that relatively small changes occur in rank order of cost per utility unit despite relatively large changes in test fees, test accuracy, complication rates, and benefits of treatments (A through H).



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  		Figure 5. Bar graphs showing cost per utility unit ({Delta}QALY) on the vertical axis for the four diagnostic approaches. Parameters are varied as described in Fig 4Up from the standard (S) to A through H. Calculations are made for patients with different pCAD from 0.2 (top) to 0.8 (bottom). Note that the rank order of different diagnostic approaches changes very little. (A) Lowering the fee for ExECG has negligible effect, but (B) lowering the fee for SPECT allows it to approach PET in cost per {Delta}QALY. (C) Lowering the fee for angiography does not change the rank order substantially. (D) Raising the fee for PET allows PET to approach SPECT, as does lowering the accuracy of PET (E). Lowering the accuracy of all noninvasive stress tests (F) decreases cost per {Delta}QALY for angiography relative to other approaches. (G) Lowering the risk of false-negative tests decreases all cost per {Delta}QALY, and (H) lowering the benefit of treatment ({Delta}QALY) increased all cost per {Delta}QALY. Abbreviations as in previous figures.


*    Discussion
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
up arrowResults
*Discussion
down arrowAppendix B
 down arrowReferences
 
Estimation of Cost-effectiveness
Measurement of the cost-effectiveness of health care is difficult,4 5 6 7 but current spiraling increases in the cost of cardiac care demand further analyses.8 9 The present study was designed to achieve a specific limited objective, that is, to compare the relative cost per effect and cost per utility unit of four diagnostic algorithms for the patient suspected of having CAD. We focused on one aspect of cost-effectiveness, ie, test selection, which is currently decided primarily by the physician. To compare various clinical policies, a conceptual model was constructed based on real data and the equations of Bayes' theorem.8 The conclusions were derived strictly from analysis of this model and should not be considered clinical practice guidelines unless they can be confirmed in clinical trials.

The most difficult aspect of assessing cost-effectiveness has been an adequate definition of effectiveness.5 7 To minimize this problem, we used one definition of effectiveness but also calculated cost per utility unit.10 Expression of effectiveness and utility yielded concordant results when the rank order of cost per effect and cost per utility unit of different clinical algorithms were compared and did not require placing a dollar value on human life. It must be emphasized that the agreement of results using effectiveness (cost per patient diagnosed) and utility (cost per {Delta}QALY) supports the validity of the model. This agreement means that the difficult job of estimating improvement in QALY due to diagnosis and treatment of CAD had little or no effect on the comparison of PET versus other modalities. The model is robust and shows only minimal dependence on the assumptions used to calculate the impact of care on the patient outcome.

The present analysis addressed the use of tests to diagnose the presence or absence of CAD, because this is the indication for which the most information exists. There are other indications for tests that were not addressed by the present study, for example, predicting prognosis, functional significance of a coronary lesion, or myocardial viability.31 The present study did not try to assess cost-effectiveness or utility of the tests when used for these indications.

Controversy continues regarding the comparative cost-effectiveness or utility of medical, angioplasty, and surgical therapies for CAD.12 14 15 16 17 18 19 20 21 22 23 24 25 26 For the purpose of this study, the most important result was that the choices among these therapeutic measures produced little change in the rank order of different clinical diagnostic algorithms but rather that they produced similar changes in the absolute cost of each algorithm. Again, the validity of the model is supported by the concordance of rank orders of cost per {Delta}QALY and cost per CAD patient diagnosed (Fig 1Up). When {Delta}QALY decreased from 3.0 to 1.5, there was less utility for diagnosing CAD, so sensitivity of tests to detect CAD became less important than the test fees (Fig 5HUp). Finally, we used a simplified approach to calculating costs by using the test fees as the cost of the test instead of a more elaborate and controversial cost-accounting analysis of all the factors that influence incremental costs. Because our analysis of the model depends on the relative rather than the absolute costs of different tests, our use of published fees seems reasonable for the limited objective of this study.

pCAD in the Population
Both absolute costs and mortality rates increased linearly with pCAD, but cost per effect and cost per utility unit decreased. Thus, decreased pCAD leads to increased cost per effect and cost per utility unit. The cost per effect and cost per utility unit were particularly high in populations in which pCAD was below 10% because there were too few patients with CAD to benefit from therapy (Fig 1Up). This analysis shows that diagnostic testing for CAD in asymptomatic populations is not generally cost-effective and offers little utility for the cost. An exception may be the asymptomatic middle-aged people with multiple risk factors for CAD30 31 (who have pCAD of 0.1 to 0.2, similar to the low-pCAD people in Figs 2 through 5UpUpUpUp). Thus, some asymptomatic patients have considerable risk for CAD, and testing might have lower cost per effect or cost per utility unit than in lower-risk patients with nonanginal chest discomfort.

Cost-effectiveness increased with increasing pCAD for all clinical algorithms involving noninvasive tests, because more patients with CAD were diagnosed and could benefit from therapy. At low pCAD, negative results of the noninvasive tests reduced the number of coronary angiograms because at low pCAD, most negative tests will be correct to exclude CAD (lower predictive error at lower pCAD). Thus, it is entirely reasonable that the noninvasive tests show lower cost per effect and cost per utility unit than angiography only in populations with low to intermediate pCAD.

In contrast, at higher pCAD (above a threshold value of 70%), the noninvasive tests miss so many patients with CAD that increasing complications of CAD increase cost per effect and cost per utility unit. Decreased cost-effectiveness of noninvasive tests at high pCAD results from the increasing percent of patients with false-negative test results who actually have CAD (high predictive error at high pCAD). Thus, at high pCAD (>70%), performing angiography as the first and only test to diagnose CAD showed the lowest cost per effect or cost per utility unit of any clinical algorithm, according to this model. It should be noted that the accuracy of PET allows it to be used at a somewhat higher threshold than the other noninvasive tests; ie, angiography becomes more cost-effective than SPECT at a lower pCAD than it does for PET (Fig 1Up).

ExECG Versus Stress SPECT or PET Myocardial Perfusion Imaging
SPECT imaging shows lower cost per effect or cost per utility unit than ExECG to diagnose CAD in populations with a wider range of pCAD, but PET is most cost-effective over this same range of pCAD (Figs 1Up and 2Up). It should be noted that our values of sensitivity and specificity were only slightly higher for SPECT imaging1 2 than for ExECG.32 Part of the advantage of SPECT imaging in the present study seems to result from its lower rate of nondiagnostic tests (P<.05) despite its greater cost. PET showed lower cost per effect or cost per utility unit than the other noninvasive tests over a range of pCAD because of its greater accuracy.

Sensitivity Analysis: How Changes in Assumptions Influence Cost Per Effect and Cost Per Utility Unit
Using a model to evaluate cost-effectiveness offers the advantage that one can substitute any variable into any equation to test its impact (Table 4Up, Figs 4Up and 5Up). This approach, called sensitivity analysis, is one way to account for the uncertainties of available data for costs, risks, and clinical outcomes. For example, the differences in cost per effect or cost per utility unit of ExECG, SPECT, and PET result from changes in test fees, sensitivity, specificity, and the frequency of nondiagnostic test results when one compares the different algorithms. The most dramatic changes in cost per utility unit, however, resulted from changes in the benefit of treatment on patient outcome ({Delta}QALY).

The physician whose major concern is the risk of missing CAD in a patient would incur the lowest cost per effect or cost per utility unit if he or she recommended a more sensitive test, eg, angiography or PET rather than ExECG. Similarly, if the physician takes a pessimistic view of the impact of therapy on the natural history of CAD, he or she might assume a decrease in the number of QALYs extended from 3 to 1.5 years. This decrease would increase the cost per utility unit for all tests (Fig 5HUp). Thus, physicians who have the most aggressive and optimistic view of the results of therapy would be expected to be most aggressive about recommending the more sensitive tests for CAD, because they would yield a more favorable cost per utility unit. In contrast, a physician with a more conservative view of the benefits of therapy for CAD would be expected to recommend fewer tests and would require lower sensitivity.

Implications for Patient Care
The results of our analysis of this model suggest cost-effective algorithms for the clinical use of tests to diagnose CAD. The critical step is for the physician to select a diagnostic approach based on the pCAD in the patient, estimated by symptoms and risk factors. It is possible to estimate the pCAD clinically before and after noninvasive testing by use of any of several sources.30 31 In patients with no symptoms or risk factors, in particular, the pCAD is so low that it is difficult for any testing to yield favorable balances between cost and effect or utility. This analysis deserves consideration by physicians who include exercise stress testing as part of a routine examination, regardless of the patient's history or risk factors. It is also important for the physician to base indications for test ordering on his or her view of the impact of therapy on CAD. The higher the physician's estimate of the utility ({Delta}QALY) of treatment of CAD, the more aggressive he or she should be about ordering tests to detect the disease.

Atypical Chest Discomfort
If the patient has symptoms that are not typical of angina pectoris, then the patient has an intermediate pCAD.30 31 This higher pCAD (compared with asymptomatic people) makes all testing approaches more cost-effective and yields greater utility because it reduces the dramatic cost differences among different clinical algorithms for the use of diagnostic tests. PET imaging remains the most cost-effective first test, but the initial use of angiography becomes more competitive in terms of cost per effect or cost per utility unit as pCAD increases.

Typical Angina Pectoris
If the patient has typical angina pectoris, then the pCAD is high, unless the patient is a young woman or very young man.30 31 In middle-aged men and older women, the most cost-effective approach is to perform angiography as the initial test. Angiography provides the most reliable prognostic information for CAD and is necessary before PTCA or CABG is considered.14 Since functional aerobic capacity for exercise27 28 or evidence of functional significance of lesions or myocardial viability may add useful information about prognosis, noninvasive exercise tests might be indicated after angiography in selected patients to determine indications for invasive therapies.30 To incorporate these indications for tests into the model would require many additional assumptions and use of data that have been less widely tested. Thus, we did not test some potentially important and clinically relevant hypotheses so as to make the conclusion more reliable.

Calculation of the Increase in {Delta}QALY' for Patients Based on Whether or Not They Have an Accurate Diagnosis of CAD
The absolute value of the utility unit was the increase in quality-adjusted life years ({Delta}QALY') due to diagnosis and treatment of CAD (Table 3Up). This value is obviously difficult to calculate, but it is used in the present study only as a common denominator to modify the benefit of diagnosing CAD. The goal of the present study was to compare rank order of cost per utility unit of four different clinical algorithms to diagnose CAD rather than computing the exact cost of each individual algorithm. The difference in utility ({Delta}QALY') over a 10-year follow-up period is shown in Table 3Up for patients in whom CAD was diagnosed accurately ("With Dx," Table 3AUp) as well as for the same patients if they had not had an accurate diagnosis of CAD ("Without Dx," Table 3BUp). The average length of life over a 10-year follow-up period is shown, as is the subjective quality of life, expressed as a fraction of life at full health (Q/y), to calculate utility. Making the diagnosis of CAD leads to higher {Delta}QALY': 7.09-4.09=3.00 years (Table 3AUp minus 3B). This calculation is based on a synthesis of available data, as indicated below.

Thirty percent of patients were treated medically (FP=0.3), and 60% of these medical patients were living at 10 years after diagnosis (Table 3AUp) (FL=0.6). For patients who survived 10 years (FL), multiplying the fraction of patients treated medically (FP=0.3) times the fraction of patients alive at 10 years (0.6) times the average length of life after angiography (10 years for survivors) times Q/y (0.8 in survivors) yielded utility (QALY') values of 1.44 for survivors. Similar calculations for patients who had died within 10 years yielded QALY' of 0.36 years. The overall QALY' for all patients treated medically after diagnosis (1.80 years) was calculated as the sum of QALY' for 10-year survivors (1.44) plus those who died (0.36). The QALY' for survivors shown in Table 3AUp (1.80) is 20% higher than that estimated for the same group of patients if they had not had angiography to prove the diagnosis of CAD (1.54 years, Table 3BUp).

Another 30% of patients were treated by PTCA (FP=0.3), and 75% of these PTCA patients were living at 10 years after angiography (Table 3AUp). Multiplying the fraction of patients who had PTCA (0.3) times the fraction who were alive at 10 years (0.75) times the average length of life after angiography (10 years for survivors) times y (0.9) yielded QALY' values of 2.02 QALY' for survivors. Similar calculations yielded QALY'=0.23 years for those who died. The overall {Delta}QALY' for all patients treated by PTCA (2.25 QALY', Table 3AUp) was calculated as the sum of QALY' for 10-year survivors (2.02) plus those who died (0.23 years). The overall QALY' for patients with the diagnosis of CAD and PTCA treatment was 40% higher than that estimated for the same group of patients if they had not had angiography to prove the diagnosis of CAD and begin treatment (1.35).

Finally, 40% of patients with CAD diagnosed would have CABG (FP=0.4) for left main or three-vessel CAD or intractable symptoms (Table 3AUp).12 These surgical patients would have an 80% survival over 10 years compared with a 33% survival estimated for the same patients treated without surgery ("without diagnosis and treatment of CAD," Table 3BUp).12 27 28 29 30 31 Patients who died at some time after surgery had a 4-year average life span after surgery and a lower quality of life (Q/y=0.5) than did patients who lived at least 10 years after surgery (Q/y=0.9). The number of QALYs for surgical patients is calculated by multiplying the fractions of patients having surgery (0.4) by the fraction of patients surviving 10 years (0.8)=0.32 (Table 3AUp). This figure is multiplied by the number of years survived (10) and the Q/y (0.9) to yield QALY' as an index of utility. Similar computations for patients who died after surgery yield 0.16 years, which is added to yield {Delta}QALY' of 3.04 years for surgical patients versus 1.19 years for the same patients treated without CABG (Table 3BUp).

For sensitivity analysis, this slight improvement in {Delta}QALY' (3.0 years) over a 10-year follow-up period was decreased to 1.5 years at full quality of life as an index of utility. Such a large variation in {Delta}QALY' can account for a wide range of variability in outcomes of different types of therapies, which represents the major source of uncertainty in this type of utility analysis. Thus, the particular value of improved utility ({Delta}QALY') due to diagnosis of CAD changed the absolute dollar cost but not the relative ranking of different clinical policies. This value of improved {Delta}QALY' due to diagnosis of CAD estimated in Table 3Up was then applied to each of the four clinical policies by use of the equations in Appendix B. We calculated utility as net QALYs ({Delta}QALY) for each algorithm, and this reflects not only the effect of diagnosis and therapy ({Delta}QALY') but also the outcomes of diagnostic tests per se, the effects of complications and mortality rates due to tests and CAD missed by false-negative tests.


*    Appendix B
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
up arrowResults
up arrowDiscussion
*Appendix B
down arrowReferences
 
Equations to Calculate Cost-effectiveness and Cost Per Utility Unit for Each Clinical Algorithm
Table 2Up shows variables used in calculations. For the first algorithm, ExECG was performed first in all patients, and angiography was performed only if ExECG was positive (+) or nondiagnostic (NonDx).

Cost=NE(FE+REC)+NA(FA+RAC)+NF(RFC) and mortalities=NEME+NAMA+NFMF, where P=pCAD in population.

NE=number of patients having initial test; ExECG=1.0.

NA=number of patients having angiography because of (+) or NonDx ExECG=NEx(1-NDxE)x[PxSnE+(1-P)x (1-SpE)]+NExNDxE.

NF=number of patients with false-negative (-) ExECG who do not have angiography for CAD Dx=NEx (1-NDxE)xPx(1-SnE).

CAD Dx=patients with CAD diagnosed correctly by the test (first definition of "effectiveness" of algorithms)=NEx (1-NDxE)xPxSnE + NExPxNDxE.

{Delta}QALY'=quality-adjusted life years extended by therapy due to the diagnosis of CAD by the algorithm (Appendix A) (definition of "utility" of policy) and Table 3Up=3.0 years over a 10-year follow-up period.

{Delta}QALY=net quality-adjusted life years extended by therapy for a particular algorithm, taking into account not only the favorable effect of CAD diagnosis ({Delta}QALY') but also the deaths and complications of tests and CAD missed that result from application of the particular algorithm=(CAD Dx)x ({Delta}QALY')-10x(NExME+NAxMA)-5(NFxMF)-10(0.1) (NExRE+NAxRA+NFxRF), where deaths due to diagnostic tests subtract 10 years, and deaths due to CAD missed by false-negative tests subtract an average of 5 years. Complications due to tests or CAD missed reduce the quality of life per year (Q/y) by 1/10 per year.

In the next algorithm, exercise or pharmacological SPECT or PET imaging was performed first and angiography was performed only if SPECT or PET was positive or nondiagnostic (equations are identical to the first algorithm, substituting values for fees, test sensitivity, specificity, and rates of nondiagnostic tests of SPECT or PET for ExECG).

In the final algorithm, angiography is the first and only test to diagnose CAD.

1. Costs=NAxFA+RAxC and mortality=NAxMA, where NA=NE from policy 1=1.0 and NF=0.

2. CAD Dx=NAxP and {Delta}QALY=NAx{Delta}QALY'xP-10x NAxMA-NAxRA.

Received March 4, 1994; accepted August 15, 1994.


*    References
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
up arrowResults
up arrowDiscussion
up arrowAppendix B
 *References
 

  1. Schelbert HR, Wisenberg G, Phelps ME, Gould KL, Herze E, Hoffman EJ, Gomes A, Kuhl DE. Noninvasive assessment of coronary stenoses by myocardial imaging during pharmacologic coronary vasodilation, VI: detection of coronary artery disease in human beings with intravenous N-13 ammonia and positron computed tomography. Am J Cardiol. 1982;49:1197-1207. [Medline] [Order article via Infotrieve]
  2. Gould KL. PET perfusion imaging and nuclear cardiology. J Nucl Med. 1991;32:579-606. [Free Full Text]
  3. McNeil BJ, Varady PD, Burrows BA, Adelstein SJ. Measures of clinical efficacy: cost-effectiveness calculations in the diagnosis and treatment of hypertensive renovascular disease. N Engl J Med. 1975;293:216-221. [Abstract]
  4. Enthoven AC. Shattuck Lecture: Cutting costs without cutting the quality of care. N Engl J Med. 1978;298:1229-1238. [Medline] [Order article via Infotrieve]
  5. Roper WL, Winkenwerder W, Hackbarth GM, Krakauer H. Effectiveness in health care: an initiation to evaluate and improve medical practice. N Engl J Med. 1988;319:1197-1202. [Medline] [Order article via Infotrieve]
  6. Ellwood P. Special Report: Shattuck Guest Lecture: Outcome management, a technology of patient experience. N Engl J Med. 1988;18:1549-1556.
  7. Bell RS. Efficacy...what's that? Semin Nucl Med. 1978;8:316-323. [Medline] [Order article via Infotrieve]
  8. Patterson RE, Eng C, Horowitz SF, Gorlin R, Goldstein SR. Bayesian comparison of cost-effectiveness of different clinical approaches to diagnose coronary artery disease. J Am Coll Cardiol. 1984;4:278-289. [Abstract]
  9. Gould KL, Goldstein RA, Mullani NA. Economic analysis of clinical positron emission tomography of the heart with rubidium-82. J Nucl Med. 1989;30:707-717. [Abstract/Free Full Text]
  10. Gleason JM, Frick M. The expected value of cash flows in the economic analysis of clinical positron emission tomography of the heart. J Nucl Med. 1990;31:1893-1894. Letter. [Free Full Text]
  11. Weinstein MC, Fineberg HV, Elstein AS, Frazier HS, Heuhauser D, Neutra RR, McNeil BJ. Clinical Decisions and Limited Resources: Clinical Decision Analysis. Philadelphia, Pa: WB Saunders; 1980:228-265.
  12. Sox HC Jr, Blatt MA, Higgins MC, Martin KI. Medical Decision Making. Boston, Mass: Butterworths; 1988:147-201 and 317-336.
  13. McKean R. Efficiency in Government Through Systems Analysis. New York, NY: John Wiley & Son; 1958:21-96.
  14. Proudfit WL, Bruschke AVG, Sones FM Jr. Natural history of obstructive coronary artery disease: ten-year study of 601 nonsurgical cases. Prog Cardiovasc Dis. 1978;21:53-78. [Medline] [Order article via Infotrieve]
  15. Kent KM, Bentivoglio LG, Block PS, Bourassa MG, Cowley MJ, Dorros G, Detre KM, Gosselin AJ, Gruentzig AR, Kelsey SF, Mock MB, Mullin SM, Passamani ER, Myler RK, Simpson J, Stertzer SH, Van Rader MJ, Williams DO. Long term efficacy of percutaneous transluminal angioplasty (PTCA): report from the NHLBI registry. Am J Cardiol. 1984;53(suppl C):27C-31C.
  16. Talley JD, Hurst JW, King SB III, Douglas JS Jr, Roubin GS, Gruentzig AR, Anderson HV, Weintraub WS. Clinical outcome five years after attempted percutaneous coronary angioplasty in 427 patients. Circulation. 1988;77:820-829. [Abstract/Free Full Text]
  17. Frye RL, Frommer PL, McCallum BD. Consensus development conference on coronary artery bypass: introduction. Circulation. 1982;65(suppl II):II-1-II-2.
  18. Hurst JW, King SB III. The prolongation of life by coronary bypass surgery: the Emory University Hospital experience. In: Hurst JW, ed. The Heart: Bypass Surgery for Obstructive Coronary Disease Update II. New York, NY: McGraw-Hill; 1980:141-150.
  19. Cosgrove DM, Loop FD, Sheldon WC. Results of myocardial revascularization: a 12-year experience. Circulation. 1982;65(suppl II):II-37-II-43.
  20. Hammermeister KE, DeRouen TA, Dodge HT. Comparison of survival of medically and surgically treated coronary disease patients in Seattle Heart Watch: a non-randomized study. Circulation. 1982;65(suppl II):II-53-II-59.
  21. Hall RJ, Elayda MA, Gray A, Mathur VS, Garcia E, DeCastro CM, Massumi A, Cooley DA. Coronary artery bypass: long-term follow-up of 22,284 consecutive patients. Circulation. 1983;68(suppl II):II-20-II-26.
  22. The Veterans Administration Coronary Artery Bypass Surgery Cooperative Study Group. Eleven-year survival in the Veterans' Administration randomized trial of coronary bypass surgery for stable angina. N Engl J Med. 1984;311:1333-1339. [Abstract]
  23. Peduzzi P, Hultgren H, Thomsen J, Detre K. Ten-year effect of medical and surgical therapy on quality of life: Veterans' Administration Cooperative Study of Coronary Artery Surgery. Am J Cardiol. 1987;59:1017-1023. [Medline] [Order article via Infotrieve]
  24. Varnauskas E, European Coronary Surgery Study Group. Survival, myocardial infarction, and employment status in a prospective randomized study of coronary bypass surgery. Circulation. 1985;72(suppl V):V-90-V-101.
  25. Killip T, Passamani E, Davis K, the CASS Principal Investigators and Their Associates. The Coronary Artery Surgery Study (CASS): a randomized trial of coronary bypass surgery: eight year follow-up and survival in patients with reduced ejection fraction. Circulation. 1985;72(suppl V):V-102-V-109.
  26. Califf RM, Pryor DB, Greenfield JC Jr. Beyond randomized clinical trials: applying clinical experience in the treatment of patients with coronary artery disease. Circulation. 1986;74:1191-1194. [Free Full Text]
  27. McNeer JR, Margolis JR, Lee KL, Kisslo JA, Peter RH, Kong Y, Behar VS, Wallace AG, McCarts CB, Rosati RA. The role of the exercise test in the evaluation of patients for ischemic heart disease. Circulation. 1978;57:64-70. [Abstract/Free Full Text]
  28. Weiner DA, Ryan TJ, McCabe CH, Chaitman BR, Sheffield LT, Fisher LD, Tristani F. The role of exercise testing in identifying patients with improved survival after coronary artery bypass surgery. J Am Coll Cardiol. 1986;8:741-748. [Abstract]
  29. Lusted LB. Introduction to Medical Decision-Making. Springfield, Ill: Charles C Thomas; 1968:1-46.
  30. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. N Engl J Med. 1979;30:1350-1358.
  31. Patterson RE, Horowitz SF. Importance of epidemiology and biostatistics in deciding clinical strategies for using diagnostic tests: a simplified approach using examples from coronary artery disease. J Am Coll Cardiol. 1989;13:1653-1665. [Abstract]
  32. Gianrossi R, Detrano R, Mulvihill D, Lehmann K, Dubach P, Colombo A, McArthur D, Froelicher V. Exercise-induced ST depression in the diagnosis of coronary artery disease: a meta-analysis. Circulation. 1989;80:87-98.[Abstract/Free Full Text]
  33. Beller GA. Diagnostic accuracy of thallium-201 myocardial perfusion imaging. Circulation. 1991;84(suppl I):I-1-I-6.
  34. Patterson RE, Horowitz SF, Eng C, Rudin A, Meller J, Halgash DA, Pichard AD, Goldsmith SJ, Herman MV, Gorlin R. Can exercise electrocardiography and thallium-201 myocardial imaging exclude the diagnosis of coronary artery disease? Am J Cardiol. 1982;49:1127-1135. [Medline] [Order article via Infotrieve]
  35. Bungo MW, Leland OS. Discordance of exercise thallium testing with coronary arteriography in patients with atypical presentations. Chest. 1983;83:112-116. [Abstract/Free Full Text]
  36. DePasquale EE, Nody AC, DePuey EG, Garcia EV, Pilcher G, Bredlau C, Rosbin G, Gober A, Gruentzig A, D'Amato P, Berger HJ. Quantitative rotational thallium-201 tomography for identifying and localizing coronary artery disease. Circulation. 1988;77:316-327. [Abstract/Free Full Text]
  37. Iskandrian AS, Heo J, Kong B, Lyons E. Effectiveness of exercise level on the ability of thallium-201 tomographic imaging in detecting coronary artery disease: analysis of 461 patients. J Am Coll Cardiol. 1989;14:1477-1468. [Abstract]
  38. Van Train KF, Maddahi J, Berman DS, Kiat H, Areeda J, Prigent F, Friedman J, and the Participants of the Multicenter Trial. Quantitative analysis of tomographic stress thallium-201 myocardial scintigrams: a multicenter trial. J Nucl Med. 1990;31:1168-1179. [Abstract/Free Full Text]
  39. Mahmarian JJ, Boyce TM, Goldberg RK, Cocanougher MK, Roberts R, Verani MS. Quantitative exercise thallium-201 single photon emission computed tomography for the enhanced diagnosis of ischemic heart disease. J Am Coll Cardiol. 1990;15:318-329. [Abstract]
  40. Schwartz RS, Jackson WG, Celio PV, Richardson LA, Hickman JR Jr. Accuracy of exercise 201Tl myocardial scintigraphy in asymptomatic young men. Circulation. 1993;87:165-172. [Abstract/Free Full Text]
  41. Tamaki N, Yonekura Y, Senda M, Yamashita K, Koide H, Saji H, Hashimoto T, Fudo T, Kambara H, Kawai C, Konishi J. Value and limitation of stress thallium-201 single photon emission computed tomography: comparison with nitrogen-13 ammonia positron tomography. J Nucl Med. 1988;20:1181-1188. [Abstract/Free Full Text]
  42. Demer LL, Gould KL, Goldstein RA, Kirkeeide RL, Mullani NA, Smalling RW, Nishikawa A, Merhige ME. Assessment of coronary artery disease severity by positron emission tomography: comparison with quantitative arteriography in 193 patients. Circulation. 1989;79:825-835. [Abstract/Free Full Text]
  43. Williams BR, Jansen DE, Wong LF, Fiedotin AF, Knopf WD, Toporoff SJ. Positron emission tomography for the diagnosis of coronary artery disease: a non-university experience and correlation with coronary angiography. J Nucl Med. 1989;30:845. Abstract.
  44. Go RT, Marwick TH, MacIntyre WJ, Saha GB, Neumann DR, Underwood DA, Simpferdorfer CC. A prospective compari- son of rubidium-82 PET and thallium-201 SPECT myocardial perfusion imaging utilizing a single dipyridamole stress in the diagnosis of coronary artery disease. J Nucl Med. 1990;31:1899-1905. [Abstract/Free Full Text]
  45. Stewart RE, Schwaiger M, Molina E, Popma J, Gacioch GM, Kalus M, Squicciarini S, Al-Aouar ZR, Schork A, Kuhl DE. Comparison of rubidium-82 positron emission tomography and thallium-201 SPECT imaging for detection of coronary artery disease. Am J Cardiol. 1991;67:1303-1310. [Medline] [Order article via Infotrieve]
  46. Garcia EV, Eisner RL, Patterson RE. What should we expect from cardiac PET? J Nucl Med. 1993;34:978-980. [Free Full Text]
  47. Bruce RA, Irving JB. Exercise electrocardiography. In: Hurst JW, Logue RB, Schlant RC, Wenger NK, eds. The Heart, Arteries and Veins. 4th ed. New York, NY: McGraw-Hill; 1978:336-348.
  48. Adams DF, Abrams HL. Complications of coronary arteriography: a follow-up report. Cardiovasc Radiol. 1979;2:89-97.[Medline] [Order article via Infotrieve]



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