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Published on: July 20, 2022
Subclinical left atrial dysfunction profiles for prediction of cardiac outcome in the general population
Nicholas Cauwenberghs1, Francois Haddad2, František Sabovčik1
1Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Belgium.
Insights
New echocardiographic thresholds for left atrial dysfunction, including left atrial emptying fraction (LAEF) and left atrial reservoir strain (LARS), predict cardiac events and atrial fibrillation (AFib). These findings enhance cardiac disease prediction in the community.
Area of Science:
- Cardiology
- Echocardiography
- Preventive Medicine
Background:
- Subclinical left atrial dysfunction lacks established echocardiographic definitions.
- Epidemiological data are needed to define normal and abnormal left atrial function parameters.
Purpose of the Study:
- To derive outcome-driven echocardiographic thresholds for left atrial function parameters.
- To define subclinical left atrial dysfunction using population-based data.
- To assess the predictive value of these thresholds for cardiac events and atrial fibrillation (AFib).
Main Methods:
- A population study of 1306 individuals (mean age 50.7 years, 51.6% women).
- Echocardiographic assessment of left atrial function (LAEF, LAFI, LARS) and LV global longitudinal strain.
- Receiver-operating curve analysis to derive cut-off values for left atrial dysfunction.
- Prospective follow-up for cardiac events and AFib incidence over 8.5 years.
Main Results:
- Outcome-driven cut-offs for AFib prediction: LAEF <55%, LAFI <40.5, LARS <23%.
- Abnormal LARS (<23%) independently predicted higher risk for cardiac events and AFib (P≤0.012).
- Concomitant abnormal LAEF and LARS significantly increased risk for cardiac events (HR 2.10) and AFib (HR 6.45).
- Combined LARS and LV strain improved prognostic accuracy beyond clinical models for cardiac events and AFib.
Conclusions:
- Outcome-driven thresholds for left atrial dysfunction predict cardiac events and AFib independently of traditional risk factors.
- Screening for subclinical left atrial and LV systolic dysfunction can improve community-based cardiac disease prediction.
Objective:
Echocardiographic definitions of subclinical left atrial dysfunction based on epidemiological data remain scarce. In this population study, we derived outcome-driven thresholds for echocardiographic left atrial function parameters discriminating between normal and abnormal values.
Methods:
In 1306 individuals (mean age, 50.7 years; 51.6% women), we echocardiographically assessed left atrial function and LV global longitudinal strain. We derived cut-off values for left atrial emptying fraction (LAEF), left atrial function index (LAFI) and left atrial reservoir strain (LARS) to define left atrial dysfunction using receiver-operating curve threshold analysis. Main outcome was the incidence of cardiac events and atrial fibrillation (AFib) on average 8.5 years later.
Results:
For prediction of new-onset AFib, left atrial cut-offs yielding the best balance between sensitivity and specificity (highest Youden index) were: LAEF less than 55%, LAFI less than 40.5 and LARS less than 23%. Applying these cut-offs, abnormal LAEF, LAFI and LARS were, respectively, present in 27, 37.1 and 18.1% of the cohort. Abnormal LARS (<23%) was independently associated with higher risk for cardiac events and new-onset AFib (P ≤ 0.012). Participants with both abnormal LAEF and LARS presented a significantly higher risk to develop cardiac events (hazard ratio: 2.10; P = 0.014) and AFib (hazard ratio: 6.45; P = 0.0036) than normal counterparts. The concomitant presence of an impaired LARS and LV global longitudinal strain improved prognostic accuracy beyond a clinical risk model for cardiac events and the CHARGE-AF Risk Score for AFib.
Conclusion:
Left atrial dysfunction based on outcome-driven thresholds predicted cardiac events and AFib independent of conventional risk factors. Screening for subclinical left atrial and LV systolic dysfunction may enhance cardiac disease prediction in the community.
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