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Identifying Predictors for Heart Failure Outcomes in Phospholamban p.(Arg14del)-Positive Individuals.
Myrthe Y C van der Heide1, Tom E Verstraelen1, Remco de Brouwer2
1Heart Center, Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
This study identifies key clinical predictors for heart failure outcomes in individuals with Phospholamban (PLN) p.(Arg14del) mutations, aiding in risk stratification for emerging genetic therapies.
Area of Science:
- Cardiovascular Genetics
- Heart Failure Pathophysiology
- Genetic Medicine
Background:
- Phospholamban (PLN) p.(Arg14del) mutations confer high risk for cardiomyopathy and heart failure.
- Current heart failure treatments are often ineffective for PLN p.(Arg14del)-related cardiomyopathy.
- Identifying at-risk patients is crucial for advancing genetic therapies for hereditary cardiomyopathies.
Purpose of the Study:
- To identify clinical predictors of heart failure outcomes in individuals with PLN p.(Arg14del) mutations.
- To improve risk prediction models for hereditary cardiomyopathy.
- To guide patient selection for novel genetic therapies.
Main Methods:
- Analysis of data from 904 individuals in the PLN/ACM Registry.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression.
- Primary endpoint: composite of heart failure hospitalization, device implantation, transplantation, or death.
Main Results:
- 116 participants (13%) experienced a primary endpoint event over a median 5.4-year follow-up.
- Significant predictors identified: reduced left ventricular ejection fraction, low-voltage ECG, and NYHA functional class ≥II at baseline.
- These predictors remained significant across all LASSO regression models.
Conclusions:
- Left ventricular ejection fraction, low-voltage ECG, and NYHA class ≥II are significant predictors of heart failure outcomes in PLN p.(Arg14del) carriers.
- These findings enhance risk stratification for patients with PLN p.(Arg14del)-related cardiomyopathy.
- Accurate risk prediction is vital for the timely application of advancing genetic therapies.
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