Role of Valsalva maneuver electrocardiography in the diagnosis of coronary artery disease in senior adults: a prospective cross-sectional study

瓦尔萨尔瓦动作心电图在老年人冠状动脉疾病诊断中的作用:一项前瞻性横断面研究

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Abstract

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide. The exercise treadmill test (ETT) is widely used for CAD diagnosis but unsuitable for patients who are elderly, frail, or have mobility issues. This study examined whether electrocardiogram monitoring during the Valsalva maneuver, known as Valsalva maneuver electrocardiography (VM-ECG), is a potential screening instrument for CAD in these patients. METHODS: This prospective cross-sectional study enrolled 106 elderly patients aged 60-75 years. All participants underwent the ETT, VM-ECG, and coronary imaging (invasive coronary angiography or computed tomography coronary angiography). Diagnostic performance metrics, including sensitivity, specificity, and accuracy, were calculated for VM-ECG. Adverse reactions during ETT and VM-ECG were recorded and compared. Patients were categorized into negative, equivocal, and positive groups based on VM-ECG results. Sequential logistic regression analysis was performed to identify factors that independently influenced VM-ECG outcomes. RESULTS: VM-ECG demonstrated a maximum sensitivity of 71.4%, specificity of 76.5%, and accuracy of 73.9% for CAD diagnosis. The incidence of adverse reactions was significantly lower during VM-ECG than the ETT (2.8% vs. 20.8%, P < 0.0001). Sequential logistic regression analysis identified the maximum heart rate during the expiratory phase (odds ratio = 1.118, 95% confidence interval: 1.046-1.211, P = 0.002) as factor independently influencing VM-ECG outcomes. CONCLUSIONS: The VM-ECG exhibits potential as a screening tool for CAD in elderly populations, comparable to the ETT. The observation that the maximum heart rate during the expiratory phase correlates with an increased positivity rate on VM-ECG indicates its potential utility as a critical diagnostic parameter. Further research is necessary to substantiate its long-term diagnostic accuracy, cost-effectiveness, and applicability in real-world settings.

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