Angiotensin receptor type 1 single nucleotide polymorphism 1166A/C is associated with malignant arrhythmias and altered circulating miR-155 levels in patients with chronic heart failure.

BACKGROUND: Sudden cardiac death (SCD) from ventricular tachyarrhythmias accounts for approximately 450,000 annual deaths in the United States; many of these cases involve patients with chronic heart failure (HF). Prediction of which HF patients are most susceptible to SCD is difficult, and it is uncertain whether gene polymorphisms associated with HF outcomes are also linked to arrhythmic risk. METHODS: We evaluated 485 patients with chronic HF to see whether the angiotensin receptor type 1 (AT1R) 1166A/C or angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphisms were associated with a higher rate of ventricular arrhythmias requiring implantable cardioverter defibrillator (ICD) therapies over a 5-year period. We assessed the correlation between polymorphisms and antitachycardia pacing (ATP) and/or ICD shocks. RESULTS: Patients with AT1R-1166CC genotype had an increased rate of all events: ATP plus ICD shocks (P = .02). There was no association between ACE I/D genotype and ICD therapies. Furthermore, circulating levels of microRNA-155 (miR-155), a microRNA known to posttranscriptionally regulate AT1R expression, were significantly decreased in the CC compared with the AC and AA genotypes and were associated with ICD events. CONCLUSION: Our study suggests that the AT1R-1166CC genotype is associated with increased ICD therapies in patients with chronic HF, and the level of circulating miR-155 may be a potential marker for arrhythmic risk. Although these findings are novel, they will need replication and validation in larger cohorts of chronic HF patients.

Risk stratification of ventricular arrhythmias in patients with systolic heart failure.

PURPOSE OF REVIEW: Sudden cardiac death (SCD) accounts for an estimated 310 000 deaths in the United States each year. Implantable cardioverter defibrillator (ICD) implantation has revolutionized SCD prevention in heart failure patients, but only a minority of patients with ICDs receive appropriate therapy for ventricular arrhythmias. At present, the selection of patients for ICD is based largely on left ventricular ejection fraction and heart failure, but further risk stratification is still needed to determine which patients will derive the greatest benefit. RECENT FINDINGS: Multicenter studies have failed to confirm the utility of microvolt T-wave alternans to predict ventricular arrhythmias in patients with ICDs. Additional risk stratification tools including resting ECG characteristics, nonsustained ventricular tachycardia, tests of autonomic function, and cardiac MRI demonstrate predictive value but have limited clinical applicability at present. SUMMARY: Depressed ejection fraction with symptomatic heart failure remains the most powerful predictor of SCD and is the primary method currently used in patient care decisions. Progress continues in evaluation of additional risk factors and risk stratification tools, but no one test or combination of tests is definitive for prediction of arrhythmic events.

Usefulness of the Duke Sudden Cardiac Death risk score for predicting sudden cardiac death in patients with angiographic (>75% narrowing) coronary artery disease.

The currently available sudden cardiac death (SCD) risk prediction tools fail to identify most at-risk patients and cannot delineate a specific patient's SCD risk. We sought to develop a tool to improve the risk stratification of patients with coronary artery disease. Clinical, demographic, and angiographic characteristics were evaluated among 37,258 patients who had undergone coronary angiography from January 1, 1985 to May 31, 2005, and who were found to have at least one native coronary artery stenosis of > or =75%. After a median follow-up of 6.2 years, SCD had occurred in 1,568 patients, 14,078 patients had died from other causes, and 21,612 patients remained alive. A Cox proportional hazards model identified 10 independent patient characteristic variables significantly associated with SCD. A simplified model accounting for 97% of the predictive capacity of the full model included the following 7 variables: depressed left ventricular ejection fraction, number of diseased coronary arteries, diabetes mellitus, hypertension, heart failure, cerebrovascular disease, and tobacco use. The Duke SCD risk score was created from the simplified model to predict the likelihood of SCD among patients with coronary artery disease. It was internally validated with bootstrapping (c-index = 0.75, chi-square = 1,220.8) and externally validated in patients with ischemic cardiomyopathy from the Sudden Cardiac Death Heart Failure Trial (SCD-HeFT) database (c-index = 0.64, chi-square = 14.1). In conclusion, the Duke SCD risk score represents a simple, validated method for predicting the risk of SCD among patients with coronary artery disease and might be useful for directing treatment strategies designed to mitigate the risk of SCD.