Publications by authors named "R Lampert"

Background: Females with hypertrophic cardiomyopathy present at a more advanced stage of the disease and have a higher risk of heart failure and death. The factors behind these differences are unclear. We aimed to investigate sex-related differences in clinical and genetic factors affecting adverse outcomes in the Sarcomeric Human Cardiomyopathy Registry.

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  • - Hypertrophic cardiomyopathy (HCM) was traditionally seen as caused by rare, high-risk single-gene changes, but new research indicates common low-risk variants (LowSVs) also play a significant role in the disease.
  • - In a study of over 6000 patients, 12 LowSVs were discovered, which are relatively common in the general population and more prevalent in HCM patients, suggesting they may influence disease severity and risk.
  • - While LowSVs alone are linked to a later onset of HCM and fewer complications, their presence alongside more severe genetic variants increases health risks significantly.
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  • The study investigates how acute psychological stress impacts cardiovascular disease (CVD) mortality, focusing on autonomic dysfunction as indicated by electrocardiographic measures.
  • In a cohort of 765 participants with stable CVD, researchers monitored heart rate variability (HRV) during stress tests and found a significant association between decreased HRV during stress and a higher risk of CVD death.
  • The findings suggest that both stress-induced decreases in HRV and low resting HRV independently increase the risk of CVD mortality, highlighting the need for further research on managing autonomic dysfunction to improve health outcomes.
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  • A new machine learning algorithm was developed to predict all-cause mortality and heart failure hospitalization in patients with implantable cardioverter-defibrillators (ICDs), aiming to improve personalized risk assessments compared to traditional methods.
  • The study used a large cohort from the Veterans Health Administration, analyzing data to identify risk factors and utilizing random forest techniques for accurate predictions over 3-month and 1-year intervals.
  • Results showed strong predictive accuracy, with receiver-operating characteristic curve values indicating the model's effectiveness in distinguishing patient outcomes based on baseline demographics and ICD data.
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