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Congestive Heart Failure

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Criteria for Iron Deficiency in Patients With Heart Failure Association of loop diuretics use and dose with outcomes in outpatients with heart failure: a systematic review and meta-analysis of observational studies involving 96,959 patients The multiple causes and treatments of heart failure: focus on genetic and molecular mechanisms and non-pharmacological interventions Empagliflozin, Health Status, and Quality of Life in Patients with Heart Failure and Preserved Ejection Fraction: The EMPEROR-Preserved Trial A population-based study of 92 clinically recognised risk factors for heart failure: co-occurrence, prognosis and preventive potential 3D Printing and Heart Failure: The Present and the Future When and how to use SGLT2 inhibitors in patients with HFrEF or chronic kidney disease Myeloid-Derived Growth Factor Protects Against Pressure Overload–Induced Heart Failure by Preserving Sarco/Endoplasmic Reticulum Ca2+-ATPase Expression in Cardiomyocytes 2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure) Nuclear Imaging of the Cardiac Sympathetic Nervous System: A Disease-Specific Interpretation in Heart Failure

Clinical TrialJune 2018

JOURNAL:JACC Clin Electrophysiol. Article Link

Improving the Use of Primary Prevention Implantable Cardioverter-Defibrillators Therapy With Validated Patient-Centric Risk Estimates

WC Levy, AS Hellkamp, DB Mark et al. Keywords: heart failure; ICD; non-sudden death; prognosis; proportional risk; regression analysis; risk prediction model; sudden death

ABSTRACT


OBJECTIVES - The authors previously developed the Seattle Proportional Risk Model (SPRM) in systolic heart failure patients without implantable cardioverter-defibrillators (ICDs)to predict the proportion of deaths that were sudden. They subsequently validated the SPRM in 2 observational ICD data sets. The objectives in the present study were to determine whether this validated model could improve identification of clinically important variations in the expected magnitude of ICD survival benefit by using a pivotal randomized trial of primary prevention ICD therapy.


BACKGROUND - Recent data show that <50% of nominally eligible subjects receive guideline- recommended primary prevention ICDs.

METHODS - In the SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial), a placebo-controlled ICD trial in 2,521 patients with an ejection fraction ≤35% and symptomatic heart failure, we tested the use of patient-level SPRM-predicted probability of sudden death (relative to that of non-sudden death) as a summary measurement of the potential for ICD benefit. A Cox proportional hazards model was used to estimate variations in the relationship between patient-level SPRM predictions and ICD benefit.

RESULTS - Relative to use of mortality predictions with the Seattle Heart Failure Model, the SPRM was much better at partitioning treatment benefit from ICD therapy (effect size was 2- to 3.6-fold larger for the ICD×SPRM interaction). ICD benefit varied significantly across SPRM-predicted risk quartiles: for all-cause mortality, a +10% increase with ICD therapy in the first quartile (highest risk of death, lowest proportion of sudden death) to a decrease of 66% in the fourth quartile (lowest risk of death, highest proportion of sudden death; p = 0.0013); for sudden death mortality, a 19% reduction in SPRM quartile 1 to 95% reduction in SPRM quartile 4 (p < 0.0001).

CONCLUSIONS - In symptomatic systolic heart failure patients with a Class I recommendation for primary prevention ICD therapy, the SPRM offers a useful patient-centric tool for guiding shared decision making.