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Left Ventricular Dysfunction in Outpatients with Frequent Ventricular Premature Complexes.
Kyoung-Min Park1, Sung Il Im1, Sung Ho Lee1
1Division of Cardiology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Texas Heart Institute Journal
|February 28, 2022
Summary
Frequent ventricular premature complexes (VPCs) can lead to left ventricular (LV) dysfunction. Key predictors of LV dysfunction in patients with frequent VPCs include male sex, lack of typical symptoms, and prolonged VPC QRS duration.
Area of Science:
- Cardiology
- Electrophysiology
- Heart Failure
Background:
- Frequent ventricular premature complexes (VPCs) are recognized risk factors for left ventricular (LV) dysfunction.
- Identifying clinical and electrocardiographic predictors of LV dysfunction in patients with frequent VPCs is crucial for early intervention.
Purpose of the Study:
- To determine clinical characteristics and electrocardiographic features associated with LV dysfunction in patients diagnosed with frequent VPCs.
- To identify predictors for VPC-related LV dysfunction in an outpatient setting.
Main Methods:
- Retrospective review of 412 outpatients diagnosed with frequent VPCs without structural heart disease.
- Analysis of transthoracic echocardiograms, 24-hour Holter monitoring, and electrocardiographic data.
- Correlation of VPC occurrence and symptoms with LV function (ejection fraction <50%).
Main Results:
- VPC-related LV dysfunction was diagnosed in 6.3% of patients.
- Patients with impaired LV function were more frequently men (P=0.027) and lacked typical VPC symptoms (P=0.006).
- A significantly longer VPC QRS duration (mean 157 ms vs 139 ms; P <0.01) was observed in patients with impaired LV function.
Conclusions:
- Male sex, absence of typical VPC-related symptoms, and VPC QRS duration exceeding 157 ms are associated with LV dysfunction.
- These factors may aid in predicting LV dysfunction in individuals with frequent VPCs.
- Further research can validate these predictors for clinical risk stratification.
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