Format

Send to

Choose Destination
See comment in PubMed Commons below
Am J Cardiol. 2004 May 15;93(10):1223-8.

Use of a scoring model combining clinical, exercise test, and echocardiographic data to predict mortality in patients with known or suspected coronary artery disease.

Author information

1
Division of Cardiovascular Diseases and Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

Abstract

The aim of this study was to derive and validate a model for predicting mortality by combining clinical, exercise testing, and echocardiographic data in patients with known or suspected coronary artery disease. We studied 5,679 patients (aged 62 +/- 12 years; 3,231 men) who were followed for a mean of 3 years after treadmill exercise echocardiography. Patients were randomly divided into 2 groups of equal size. (1) The modeling group underwent multivariate analysis to define independent predictors of mortality. Three hundred bootstrap resamplings were performed to determine parameter coefficients. Patients were divided into 5 risk categories according to their composite score and survival rate in each category was estimated by the Kaplan-Meier method. (2) The validation group comprised patients for whom the risk model was applied. Patients were divided into 5 risk categories based on data obtained from the modeling group. During follow-up, 315 patients died (151 in the modeling group). Independent predictors of mortality were exercise wall motion score index (chi-square 22.4, p <0.0001), workload (chi-square 17.1, p <0.0001), male gender (chi-square 15.4, p <0.0001), and age (chi-square 5.5, RR 1.02, 95% confidence interval 1 to 1.04; p = 0.02). Application of the composite score in the validation group resulted in an effective stratification of patients for mortality and cardiac events. This study provides a model for assessing risk of death by combining clinical, exercise testing, and echocardiographic data using a single composite score.

PMID:
15135693
DOI:
10.1016/j.amjcard.2004.01.064
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center