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Published on: October 14, 2016
Atrial cardiomyopathy: markers and outcomes
Oliver B Vad1,2,3, Nick van Vreeswijk1, Ahmed S Yassin1
1Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Atrial cardiomyopathy (AtCM) affects one in seven individuals and is linked to increased risks of atrial fibrillation (AF), heart failure (HF), and stroke. Identifying AtCM markers improves risk prediction for these cardiovascular conditions.
Area of Science:
- Cardiology
- Biomarkers
- Epidemiology
Background:
- Atrial cardiomyopathy (AtCM) is increasingly recognized as a significant factor contributing to atrial fibrillation (AF).
- Understanding AtCM's risk factors and its association with major cardiovascular events is crucial for clinical practice.
Purpose of the Study:
- To investigate potential markers and risk factors for AtCM.
- To assess the association of AtCM with incident AF, heart failure (HF), and stroke.
- To evaluate the additive value of AtCM markers in cardiovascular risk prediction.
Main Methods:
- Utilized UK Biobank data with cardiac MRI and ECG information from 26,467 individuals.
- Defined AtCM markers including left atrial dilation, mechanical dysfunction, P-wave prolongation, and abnormal P-wave terminal force.
- Employed logistic regression for risk factors and Cox regression/cumulative incidence models for outcomes, integrating clinical and genetic risk scores.
Main Results:
- 15.7% of individuals had at least one AtCM marker; 2.3% had two or more.
- AtCM markers were associated with significantly increased hazard ratios for AF (HR: 4.59 for ≥2 markers), HF (HR: 3.08), and stroke (HR: 3.07).
- Adding AtCM markers improved AF risk reclassification by 13.7% (NRI).
Conclusions:
- Atrial cardiomyopathy is a prevalent condition, with markers present in one in seven individuals.
- AtCM markers are independently associated with increased risks of AF, HF, and stroke.
- AtCM serves as a common pathophysiological substrate for these cardiovascular outcomes, enhancing risk prediction models.
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