Biomarkers of myocardial stress and fibrosis as predictors of mode of death in patients with chronic heart failure

Abstract

Objectives: The aim of this study was to determine whether biomarkers of myocardial stress and fibrosis improve prediction of the mode of death in patients with chronic heart failure.

Background: The 2 most common modes of death in patients with chronic heart failure are pump failure and sudden cardiac death. Prediction of the mode of death may facilitate treatment decisions. The relationship between amino-terminal pro-brain natriuretic peptide (NT-proBNP), galectin-3, and ST2, biomarkers that reflect different pathogenic pathways in heart failure (myocardial stress and fibrosis), and mode of death is unknown.

Methods: HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) was a randomized controlled trial of exercise training versus usual care in patients with chronic heart failure due to left ventricular systolic dysfunction (left ventricular ejection fraction ≤35%). An independent clinical events committee prospectively adjudicated mode of death. NT-proBNP, galectin-3, and ST2 levels were assessed at baseline in 813 subjects. Associations between biomarkers and mode of death were assessed using cause-specific Cox proportional hazards modeling, and interaction testing was used to measure differential associations between biomarkers and pump failure versus sudden cardiac death. Discrimination and risk reclassification metrics were used to assess the added value of galectin-3 and ST2 in predicting mode of death risk beyond a clinical model that included NT-proBNP.

Results: After a median follow-up period of 2.5 years, there were 155 deaths: 49 from pump failure, 42 from sudden cardiac death, and 64 from other causes. Elevations in all biomarkers were associated with increased risk for both pump failure and sudden cardiac death in both adjusted and unadjusted analyses. In each case, increases in the biomarker had a stronger association with pump failure than sudden cardiac death, but this relationship was attenuated after adjustment for clinical risk factors. Clinical variables along with NT-proBNP levels were stronger predictors of pump failure (C statistic: 0.87) than sudden cardiac death (C statistic: 0.73). Addition of ST2 and galectin-3 led to improved net risk classification of 11% for sudden cardiac death, but not pump failure.

Conclusions: Clinical predictors along with NT-proBNP levels were strong predictors of pump failure risk, with insignificant incremental contributions of ST2 and galectin-3. Predictability of sudden cardiac death risk was less robust and enhanced by information provided by novel biomarkers.

Keywords: biomarker; heart failure; mode of death; prognosis.

Figure 1. Kaplan-Meier Cause-Specific Survival Curves

The cumulative probability of events according to mode of death. Pump failure death was most common (N=49), followed by sudden cardiac death (N=42). 36 patients died from other cardiac causes (CVA, cardiac procedure-related, fatal myocardial infarction, or unknown), and 28 from non-cardiac causes.

Figure 2. Cumulative Incidence of Sudden Cardiac Death, Pump Failure, and Non-Cardiovascular Death According to Levels of Biomarkers

The cumulative incidence functions of adverse outcomes according to above versus below median biomarker level groups. Incidence of non-cardiovascular death was equivalent between patient groups. Patients with more than the medial level of all three biomarkers had a significantly greater incidence of pump failure (P<0.001, all). Incidence of sudden cardiac death was significantly greater in those with more than median levels of galectin-3 (P<0.01) and NT-proBNP (P=0.05), and of borderline significance in case of ST2 (P=0.06).

Figure 3. Reclassification of 4 Year Predictive Probabilities of Pump Failure with Addition of Novel Biomarkers

Graphic representation of reclassification across low and high risk of pump failure and change in predicted probabilities with addition of galectin-3 and ST2 to Clinical model + NT-proBNP in entire cohort (top panel), cases (middle panel), and controls (bottom panel). Overlapping shading in risk categories represents the number of patients reclassified with addition of galectin-3 (left column), ST2 (middle column), and ST2+galectin-3 (right column). For example, among all patients (top panel), the addition of galectin-3 (left most column) moved 23 low risk patients into the high risk category, and 25 high risk patients to the low risk category.

Figure 4. Reclassification of 4 Year Predictive Probabilities of Sudden Cardiac Death with Addition of Novel Biomarkers

Graphic representation of reclassification across low and high risk of sudden cardiac death and change in predicted probabilities with addition of galectin-3 and ST2 to Clinical model + NT-proBNP in entire cohort (top panel), cases (middle panel), and controls (bottom panel). Overlapping shading in risk categories represents the number of patients reclassified with addition of galectin-3 (left column), ST2 (middle column), and ST2+galectin-3 (right column). For example, among all patients (top panel), the addition of galectin-3+ST2 (right most column) moved 38 low risk patients into the high risk category, and 51 high risk patients to the low risk category.