Limitations of Ejection Fraction for Prediction of Sudden Death Risk
Limitations of Ejection Fraction for Prediction of Sudden Death Risk
Objectives: We determined the contribution of multiple variables to predict arrhythmic death and total mortality risk in patients with coronary disease and left ventricular dysfunction. We then constructed an algorithm to predict risk of mortality and sudden death.
Background: Many factors in addition to ejection fraction (EF) influence the prognosis of patients with coronary disease. However, there are few tools to use this information to guide clinical decisions.
Methods: We evaluated the relationship between 25 variables and total mortality and arrhythmic death in 674 patients enrolled in the MUSTT (Multicenter Unsustained Tachycardia Trial) study that did not receive antiarrhythmic therapy. We then constructed risk-stratification algorithms to weight the prognostic impact of each variable on arrhythmic death and total mortality risk.
Results: The variables having the greatest prognostic impact in multivariable analysis were functional class, history of heart failure, nonsustained ventricular tachycardia not related to bypass surgery, EF, age, left ventricular conduction abnormalities, inducible sustained ventricular tachycardia, enrollment as an inpatient, and atrial fibrillation. The model demonstrates that patients whose only risk factor is EF ≤30% have a predicted 2-year arrhythmic death risk <5%.
Conclusions: Multiple variables influence arrhythmic death and total mortality risk. Patients with EF ≤30% but no other risk factor have low predicted mortality risk. Patients with EF >30% and other risk factors may have higher mortality and a higher risk of sudden death than some patients with EF ≤30%. Thus, risk of sudden death in patients with coronary disease depends on multiple variables in addition to EF.
Sudden cardiac death accounts for 450,000 deaths yearly in the U.S.. Furthermore, the proportion of all cardiac deaths accounted for by sudden death is increasing. Multiple clinical trials completed over the past decade have documented the effectiveness of the implantable cardioverter-defibrillator (ICD) to reduce the risk of sudden death and overall mortality in patients at high risk for sudden death. However, no recent study to date has examined the most effective means of deploying this technology to make it available to the greatest number of people in a cost-effective manner.
A number of variables have been demonstrated to identify patients at increased risk for sudden death. Recent trials have focused on left ventricular ejection fraction (EF), because of its demonstrated association with mortality risk in patients with recent myocardial infarction. However, EF lacks sensitivity for prediction of sudden death; less than 50% of patients with prior infarction who die suddenly have EF ≤30%. Additionally, many factors besides EF affect the prognosis of patients with coronary artery disease, and several lines of evidence suggest that reduced EF is a risk factor only when it exists in combination with other risk factors. Given earlier studies pointing to factors other than EF that influence prognosis after MI, the purpose of the present study is to evaluate the relative importance of multiple factors and to compare their relative contribution to risk of arrhythmic death as well as total mortality using the MUSTT (Multicenter Unsustained Tachycardia Trial) database. We then constructed a risk stratification tool that could be used in clinical practice. We demonstrate that use of such a model may enable more precise risk stratification of patients with coronary disease considered for ICD implantation for primary prevention of sudden death.
Abstract and Introduction
Abstract
Objectives: We determined the contribution of multiple variables to predict arrhythmic death and total mortality risk in patients with coronary disease and left ventricular dysfunction. We then constructed an algorithm to predict risk of mortality and sudden death.
Background: Many factors in addition to ejection fraction (EF) influence the prognosis of patients with coronary disease. However, there are few tools to use this information to guide clinical decisions.
Methods: We evaluated the relationship between 25 variables and total mortality and arrhythmic death in 674 patients enrolled in the MUSTT (Multicenter Unsustained Tachycardia Trial) study that did not receive antiarrhythmic therapy. We then constructed risk-stratification algorithms to weight the prognostic impact of each variable on arrhythmic death and total mortality risk.
Results: The variables having the greatest prognostic impact in multivariable analysis were functional class, history of heart failure, nonsustained ventricular tachycardia not related to bypass surgery, EF, age, left ventricular conduction abnormalities, inducible sustained ventricular tachycardia, enrollment as an inpatient, and atrial fibrillation. The model demonstrates that patients whose only risk factor is EF ≤30% have a predicted 2-year arrhythmic death risk <5%.
Conclusions: Multiple variables influence arrhythmic death and total mortality risk. Patients with EF ≤30% but no other risk factor have low predicted mortality risk. Patients with EF >30% and other risk factors may have higher mortality and a higher risk of sudden death than some patients with EF ≤30%. Thus, risk of sudden death in patients with coronary disease depends on multiple variables in addition to EF.
Introduction
Sudden cardiac death accounts for 450,000 deaths yearly in the U.S.. Furthermore, the proportion of all cardiac deaths accounted for by sudden death is increasing. Multiple clinical trials completed over the past decade have documented the effectiveness of the implantable cardioverter-defibrillator (ICD) to reduce the risk of sudden death and overall mortality in patients at high risk for sudden death. However, no recent study to date has examined the most effective means of deploying this technology to make it available to the greatest number of people in a cost-effective manner.
A number of variables have been demonstrated to identify patients at increased risk for sudden death. Recent trials have focused on left ventricular ejection fraction (EF), because of its demonstrated association with mortality risk in patients with recent myocardial infarction. However, EF lacks sensitivity for prediction of sudden death; less than 50% of patients with prior infarction who die suddenly have EF ≤30%. Additionally, many factors besides EF affect the prognosis of patients with coronary artery disease, and several lines of evidence suggest that reduced EF is a risk factor only when it exists in combination with other risk factors. Given earlier studies pointing to factors other than EF that influence prognosis after MI, the purpose of the present study is to evaluate the relative importance of multiple factors and to compare their relative contribution to risk of arrhythmic death as well as total mortality using the MUSTT (Multicenter Unsustained Tachycardia Trial) database. We then constructed a risk stratification tool that could be used in clinical practice. We demonstrate that use of such a model may enable more precise risk stratification of patients with coronary disease considered for ICD implantation for primary prevention of sudden death.
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