Predictors of Mortality in Patients With an Implantable Cardiac Defibrillator: A Systematic Review and Meta-analysis
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
BACKGROUND: Many current predictors of mortality in heart failure (HF) were evaluated before the use of implantable cardioverter defibrillators (ICDs). We conducted a meta-analysis to identify factors associated with mortality in ICD-HF patients. METHODS: We searched in MedLine, EMBASE, and CINAHL in May 2012. Two reviewers selected citations that included ambulatory ICD patients and addressed the association between any predictor and mortality using multivariable regression. We meta-analyzed mortality using random-effects models. RESULTS: Of 10,420 studies reviewed, 72 studies evaluating 63 predictors on 257,692 ICD patients proved eligible. High confidence in estimates was found for age (hazard ratio [HR], 1.45 for 10-year increase; 95% confidence interval [CI], 1.35-1.56), baseline glomerular filtration rate (HR, 1.25 for 15-mL/min decrease; 95% CI, 1.15-1.35), chronic obstructive pulmonary disease (HR, 1.54; 95% CI, 1.38-1.71), diabetes (HR, 1.56; 95% CI, 1.37-1.79), peripheral vascular disease (HR, 1.43; 95% CI, 1.2-1.72), left ventricular ejection fraction (HR, 0.77 for 10% increase; 95% CI, 0.73-0.83), and appropriate or inappropriate ICD shocks (HR, 2.34; 95% CI 1.59-3.44) New York Heart Association class, atrial fibrillation, and congestive HF were strongly associated with mortality but the confidence in estimates was low. Ischemic cardiomyopathy and male sex were not independent predictors of mortality. CONCLUSIONS: This meta-analysis identified strong reliable mortality predictors in ICD-HF patients. Age, renal dysfunction, chronic obstructive pulmonary disease, diabetes, peripheral vascular disease, decreased left ventricular ejection fraction, and ICD shocks during follow-up were strong predictors of mortality; ischemic cardiomyopathy and male sex were not. Further research is needed to study other potential predictors, particularly biomarkers.
