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C HAPTER 1 9 / Exercise Testing 427
DISPLAY 19-2 Example of an Automated Exercise Test Summary Report with Diagnostic and Prognostic Probabilities
Generated from A Computer Program (continued)
Angiographic CAD Prediction
The patient has no recorded history of coronary disease. Pretest probabilities for any significant coronary disease are 50%
(CASS, 1981 [chest pain, age, gender]), 71% (Morise, 1992), and 51% (Do/Froelicher, 1997,1998). Pretest probabilities for severe
coronary disease are 22% (Pryor, 1993), 52% (Morise, 1992), and 17% (Do/Froelicher, 1997,1998).
The posttest probabilities for any clinically significant CAD are 99% (Detrano, 1992), 98% (Morise, 1992), and 94%
(Do/Froelicher, 1997,1998) due to age, diabetes mellitus, symptoms, and abnormal ST depression.
The probabilities of having severe CAD are 75% (Detrano, 1992) due to abnormal ST depression, 91% (Morise, 1992) due to
age abnormal ST depression, and 74% (Do/Froelicher, 1997,1998) due to abnormal ST depression.
Operative Mortality Prediction
If the patient would be selected for nonemergent bypass surgery and no renal dysfunction was present, the estimated
operative morality rates are 9% (Parsonnet, 1989), 2% (NY State Department of Health, 1992), and 3% (VA, 1993). This is
partially based on an estimated EF of 45%, so compare with measured EF.
Disclaimer: This report was computer generated and the results are dependent on rules and correct data entry. It must be
overread by a physician.
EF, ejection fraction; ECG, electrocardiographic; METs, metabolic equivalents; SBP, systolic blood pressure.
From Froelicher, V. F. (1996). Exercise test reporting aid (EXTRA) software. St. Louis, MO: Mosby-Year Book.
Morise, A. P., Detrano, R., Bobbio, M., et al. (1993). Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary
artery disease before and after exercise testing. Journal of the American College of Cardiology 22(1), 340–341.
Do, D., West, J. A., Morise, A., et al. (1997). An agreement approach to predict severe angiographic coronary artery disease with clinical and exercise test data. American Heart Jour-
nal 134(4),672–679.
Do, D., Marcus, R., Froelicher, V., et al. (1998). Predicting severe angiographic coronary artery disease using computerization of clinical and exercise test data. Chest 114(5),1437–1445.
Pryor, D. B., Shaw, L., McCants, C.B., et al. (1993). Value of the history and physical in identifying patients at increased risk for coronary artery disease. Annals of Internal Medicine
118(2),81–90.
Detrano, R., Janosi, A., Steinbrunn, W., et al. (1991). Algorithm to predict triple-vessel/left main coronary artery disease in patients without myocardial infarction. An international
cross validation. Circulation 83(5 Suppl),III89–96.
Parsonnet, V., Dean, D., Bernstein A. D. (1989). A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation 79(6 Pt 2),I3–I12.
exchange techniques, but this requires specialized equipment and an MET value can be ascribed to any speed and grade on a tread-
is not available in many clinical laboratories. Exercise capacity is mill or workload achieved on a cycle ergometer; therefore, exercise
therefore usually expressed as exercise duration, watts achieved capacity can be compared uniformly between protocols.
(on a bicycle ergometer), maximal exercise stage, or METs. In the As mentioned previously in the discussion on protocols, there
absence of gas exchange techniques, it is preferable to express ex- can be a great deal of uncertainty in predicting a person’s energy cost
ercise capacity in METs rather than exercise time. This is because from the treadmill or cycle ergometer workload. How accurately an
MET level predicts a person’s true oxygen uptake depends on several
factors. For most patients with cardiovascular or pulmonary disease,
250 there is a substantial overprediction of the MET level. 8,25,29,60 The
error associated with this prediction is accentuated when rapidly
220 incremented protocols are used, when patients are unaccustomed to
walking on a treadmill or pedaling a cycle ergometer, and when
8,29,60
patients are allowed to use handrail support.
Maximal heart rate 160 and as a relative percentage of normal reference values for age and
190
Exercise capacity should be expressed as both an absolute value
gender. The latter can be important because exercise capacity de-
clines with increasing age and higher values are observed in men.
Thus, when measuring or estimating oxygen uptake or MET levels,
130
it is useful to have reference values for comparison. Normal refer-
ence values can facilitate communication with patients and between
100
physicians regarding levels of exercise capacity in relation to a given
patient’s peers. Figures 19-4 and 19-5 are illustrations of nomo-
70 grams for male patients referred for exercise testing. Expressing rel-
20 40 60 80 100 ative exercise capacity using a nomogram is advantageous because it
offers a simple visual method of classifying a patient’s response,
Age
without having to make cumbersome calculations from a particular
■ Figure 19-3 The relationship between maximal heart rate and regression equation. However, there are numerous available regres-
age among patients referred for exercise testing. Inner lines represent
the standard error; outer lines represent 95% confidence limits. sion equations for “normal.” All are population-specific, and nu-
(With permission from Morris, C. K., Myers, J., Froelicher, V. F., et al. merous factors affect a person’s exercise tolerance other than age and
[1994]. Nomogram based on metabolic equivalents and age for gender, including height, weight, body composition, activity status,
assessing aerobic exercise capacity in men. Journal of the American and exercise test mode used, in addition to many clinical factors
College of Cardiology, 22, 175–182.) such as smoking history, heart disease, and medications. 10,29