Diagnostic accuracy of strain and strain rate imaging detecting coronary artery disease in a stable chest pain population: a prospective diagnostic accuracy study

应变和应变率成像技术在稳定型胸痛患者中检测冠状动脉疾病的诊断准确性:一项前瞻性诊断准确性研究

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Abstract

OBJECTIVES: Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide, and detecting CAD in stable chest pain patients is challenging but crucial for early intervention. Strain and strain rate (S/SR) imaging offers a non-invasive method to assess myocardial function and detect coronary stenosis before symptoms occur. In this study, we aimed to demonstrate how effectively and accurately resting strain echocardiography can diagnose CAD. DESIGN: We conducted a prospective diagnostic accuracy study of patients with chest pain who were referred for CT coronary angiography (CCTA). SETTING: Single-centre study conducted in the University Hospital of North Norway in Tromsø, Norway between 2016 and 2021. PARTICIPANTS: A total of 510 patients with chest pain were included in the present study. BASELINE MEASURES: Echocardiography examination with S/SR imaging was performed. OUTCOME MEASURES: Echocardiography findings were compared with CCTA and coronary angiography findings. A novel scoring model incorporating S/SR parameters was developed to assess diagnostic accuracy. RESULTS: In this study, we showed that receiver operating characteristic curve analysis of early diastolic strain rate (SRe), systolic strain rate (SRs) and peak longitudinal strain (PLS) has high sensitivity and specificity with area under the curve (AUC) scores: SRe, 0.91; PLS, 0.81; SRs, 0.71 in identifying patients undergoing coronary artery bypass graft (CABG). However, these parameters showed lower sensitivity and specificity with AUC scores: SRe, 0.580; SRs, 0.539; PLS, 0.552 in detecting patients undergoing percutaneous coronary intervention (PCI). CONCLUSIONS: Our study emphasises the potential of S/SR imaging in detecting CAD, particularly in high-risk CABG patients. However, its diagnostic utility in PCI patients is limited. Our study highlights the need for comprehensive approaches in coronary disease prediction.

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