Prognostic value of the Duke Treadmill Score in diabetic patients

Dhanunjaya R Lakkireddy1, Jyothi Bhakkad, Hema L Korlakunta

  • 1Creighton University Cardiac Center, Omaha, Nebraska 68131, USA.

American Heart Journal
|September 20, 2005
PubMed

Insights

The Duke Treadmill Score (DTS) effectively predicts cardiac events in both diabetic and nondiabetic patients. This tool offers similar prognostic value for coronary artery disease risk stratification across both groups.

Area of Science:

  • Cardiology
  • Diabetology
  • Risk Stratification

Background:

  • The Duke Treadmill Score (DTS) is a validated tool for assessing coronary artery disease (CAD) risk.
  • Its prognostic utility in diabetic populations compared to nondiabetics requires further investigation.

Purpose of the Study:

  • To evaluate and compare the prognostic value of the Duke Treadmill Score (DTS) in patients with diabetes mellitus versus age- and sex-matched nondiabetic controls.
  • To determine if DTS risk stratification for coronary artery disease differs between these groups.

Main Methods:

  • A cohort study involving 100 diabetic patients and 202 matched nondiabetic controls without known CAD.
  • Risk stratification using DTS, followed by a median 6.6-year follow-up for primary and secondary cardiac events, composite outcomes, and coronary angiography rates.

Main Results:

  • The DTS demonstrated significant prognostic value for composite events in both diabetic (P < .001) and nondiabetic (P < .001) groups.
  • Diabetic patients experienced higher rates of secondary events (P = .011) and coronary angiography (P < .001).
  • Survival free from major adverse cardiac events differed significantly across DTS risk groups for diabetics (P = .002) but not controls (P = .07).

Conclusions:

  • The Duke Treadmill Score (DTS) is an equally effective predictor of survival free from major adverse cardiac events and composite events in both diabetic and nondiabetic individuals.
  • DTS provides consistent prognostic information for CAD risk stratification across diverse patient populations.
Abstract