Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing.
OBJECTIVE
Our goals were to develop and validate a multivariate algorithm for estimating the incremental probability of the presence of coronary artery disease.


BACKGROUND
Multivariate methods, including logistic regression analysis, have been extensively applied to diagnostic exercise testing. However, few previous studies have included both an incremental design and external validation.


METHODS
A retrospective collection of clinical, exercise test and catheterization data was performed involving four U.S. referral medical centers. All patients had no prior history of coronary disease and had undergone coronary angiography < or = 3 months after exercise stress testing. An algorithm was developed in one center (590 patients with a 41% prevalence of coronary artery disease) with the use of logistic regression analysis and was validated in the other three centers (1,234 patients, 70% prevalence). The algorithm incorporated pretest variables (age, gender, symptoms, diabetes, cholesterol), exercise electrocardiographic (ECG) variables (mm of ST segment depression, ST slope, peak heart rate, metabolic equivalents [METs], exercise angina) and one thallium variable. Discrimination was measured with receiver operating characteristic curve analysis. Calibration (that is, reliability) was assessed from a comparison of probability estimates and the actual prevalence of disease.


RESULTS
The overall incremental receiver operating characteristic curve areas for the validation group were pretest, -0.738 +/- 0.016; postexercise ECG, 0.78 (SE 0.017); and postthallium, 0.82 (SE 0.016); p < 0.01 for both increments. Within the three validation institutions, the institution with a disease prevalence closest to that of the derivation institution had the best incremental receiver operating characteristic curve areas. There was a stepwise incremental improvement in calibration especially from exercise ECG to thallium testing.


CONCLUSIONS
An incremental multivariate algorithm derived in one center reliably estimated disease probability in patients from three other centers. The incremental value of testing was best demonstrated when the derivation and validation groups had a similar disease prevalence. This algorithm may be useful in decision making that relates to the diagnosis of coronary disease.
Morise
A P
AP
Department of Medicine, West Virginia University School of Medicine, Morgantown 26506.
Detrano
R
R
Bobbio
M
M
Diamond
G A
GA
D000465:Algorithms; D017023:Coronary Angiography; D003327:Coronary Disease / Q000175:diagnosis / Q000453:epidemiology*; D016002:Discriminant Analysis; D004562:Electrocardiography; D005069:Evaluation Studies as Topic; D005080:Exercise Test / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D015995:Prevalence; D011336:Probability