Quantity of viable myocardium required to improve survival with revascularization in patients with ischemic cardiomyopathy: A meta-analysis

J Nucl Cardiol. 2010 Aug;17(4):646-54. doi: 10.1007/s12350-010-9226-2. Epub 2010 Apr 9.

Abstract

Background: This meta-analysis was conducted to determine optimal cutoff values for the assessment of viability using various imaging techniques for which revascularization would offer a survival benefit in patients with ischemic cardiomyopathy (ICM).

Methods and results: We searched five electronic databases to identify relevant studies through December 2008. Relative risks of cardiac death, both in patients with and without viability, were calculated in each study. In order to estimate the optimal threshold for the presence of viability, we assumed a linear relationship between the amount of viable myocardium and survival benefit of revascularization. Twenty-nine studies (4,167 patients) met the inclusion criteria. The optimal threshold for the presence of viability was estimated to be 25.8% (95% CI: 16.6-35.0%) by positron emission tomography using 18F-fluorodeoxyglucose-perfusion mismatch, 35.9% (95% CI: 31.6-40.3%) by stress echocardiography using contractile reserve or ischemic responses, and 38.7% (95% CI: 27.7-49.7%) by single photon emission computed tomography using thallium-201 or technetium-99m MIBI myocardial perfusion.

Conclusions: The calculated amount of viable myocardium determined to lead to improved survival was different among imaging techniques. Thus, separate cutoff values for imaging modalities may be helpful in determining which patients with ICM benefit from revascularization.

Publication types

  • Meta-Analysis

MeSH terms

  • Cardiomyopathies / diagnosis*
  • Diagnostic Imaging / statistics & numerical data*
  • Female
  • Humans
  • Incidence
  • Male
  • Myocardial Ischemia / diagnosis*
  • Myocardial Ischemia / mortality*
  • Myocardial Revascularization / mortality*
  • Prognosis
  • Reproducibility of Results
  • Risk Assessment / methods
  • Risk Factors
  • Sensitivity and Specificity
  • Survival Analysis
  • Survival Rate