Left ventricular hypertrophy phenotype to predict incident atrial fibrillation: The Multi-Ethnic Study of

Lili Wang1, Jiayi Yi1, Zeming Zhou1

  • 1National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China.

Insights

Left ventricular hypertrophy (LVH) phenotypes predict atrial fibrillation (AF) risk. Malignant LVH, with elevated biomarkers, poses the highest AF risk, improving prediction models.

Area of Science:

  • Cardiology
  • Cardiac Imaging
  • Epidemiology

Background:

  • Left ventricular hypertrophy (LVH) is linked to atrial fibrillation (AF).
  • The predictive capability of different LVH phenotypes for incident AF requires further investigation.
  • Understanding these associations is crucial for risk stratification and prevention strategies.

Purpose of the Study:

  • To evaluate the predictive value of LVH phenotypes for incident AF.
  • To compare the risk associated with isolated LVH versus malignant LVH.
  • To assess the incremental predictive value of LVH phenotypes in existing AF risk models.

Main Methods:

  • Utilized data from the Multi-Ethnic Study of Atherosclerosis (MESA).
  • Defined LVH using cardiac magnetic resonance imaging and cardiac biomarkers.
  • Employed Receiver-Operating Characteristic (ROC) analysis to assess predictive performance.

Main Results:

  • Included 4983 participants; 279 (5.6%) had isolated LVH, 222 (4.5%) had malignant LVH.
  • During 8.5 years follow-up, 272 incident AF cases were observed.
  • Malignant LVH (HR: 4.13) and isolated LVH (HR: 1.82) significantly increased AF risk compared to no LVH.

Conclusions:

  • AF incidence risk varies significantly across LVH phenotypes.
  • Malignant LVH presents the highest risk for incident AF.
  • Incorporating LVH phenotypes enhances the predictive accuracy of AF risk models like CHARGE-AF.
Abstract

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