Use of a prognostic treadmill score in identifying diagnostic coronary disease subgroups

Leslee J. Shaw, Eric D. Peterson, Linda K. Shaw, Karen L. Kesler, Elizabeth R. DeLong, Frank E. Harrell, Lawrence H. Muhlbaier, Daniel B. Mark

Research output: Contribution to journalArticlepeer-review

139 Scopus citations


Background - Exercise testing is useful in the assessment of symptomatic patients for diagnosis of significant or extensive coronary disease and to predict their future risk of cardiac events. The Duke treadmill score (DTS) is a composite index that was designed to provide survival estimates based on results from the exercise test, including ST-segment depression, chest pain, and exercise duration. However, its usefulness for providing diagnostic estimates has yet to be determined. Methods and Results - A logistic regression model was used to predict significant (≥75% stenosis) and severe (3-vessel or left main) coronary artery disease, and a Cox regression analysis was used to predict cardiac survival. After adjustment for baseline clinical risk, the DTS was effectively diagnostic for significant (P<0.0001) and severe (P<0.0001) coronary artery disease. For low-risk patients (score ≥+5), 60% had no coronary stenosis ≥75% and 16% had single-vessel ≥75% stenosis. By comparison, 74% of high-risk patients (score < -11) had 3- vessel or left main coronary disease. Five-year mortality was 3%, 10%, and 35% for low-, moderate-, and high-risk DTS groups (P<0.0001). Conclusions - The composite DTS provides accurate diagnostic and prognostic information for the evaluation of symptomatic patients evaluated for clinically suspected ischemic heart disease.

Original languageEnglish (US)
Pages (from-to)1622-1630
Number of pages9
Issue number16
StatePublished - Oct 20 1998
Externally publishedYes


  • Coronary artery disease
  • Exercise
  • Prognosis
  • Tests

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)


Dive into the research topics of 'Use of a prognostic treadmill score in identifying diagnostic coronary disease subgroups'. Together they form a unique fingerprint.

Cite this