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Background <p>The prognostic value of functional echocardiographic parameters for risk stratification in chronic coronary syndrome (CCS) remains incompletely understood. This study aimed to integrate resting and stress echocardiographic parameters to identify sensitive non-invasive predictors of major adverse cardiovascular events (MACEs).</p> Methods <p>A total of 754 CCS patients with a resting left ventricular ejection fraction (LVEF) ≥ 50% undergoing adenosine stress echocardiography were prospectively enrolled. Parameters including myocardial perfusion, coronary flow velocity reserve (CFVR), and myocardial work were assessed. Resting and stress values were compared within groups, while dynamic changes were analyzed between CFVR-normal and impaired subgroups. Cox regression was used to identify independent predictors.</p> Results <p>The incidence of MACEs was significantly higher in patients with impaired CFVR compared to those with normal CFVR (71.4% vs. 6.2%, <i>P</i> &lt; 0.0001). After stress, the impaired CFVR group exhibited myocardial perfusion defects, mechanical dyssynchrony, and reduced myocardial work efficiency, in contrast to the normal CFVR group. Patients with impaired CFVR combined with regional wall motion and perfusion abnormalities had the worst prognosis. Multivariate Cox model integrating CFVR and stress-derived dynamic parameters demonstrated superior predictive performance for MACEs, significantly exceeding that of the stress-substitution and base models (C-index: 0.867 vs. 0.841 vs. 0.709).</p> Conclusion <p>In CCS patients with preserved LVEF, reduced CFVR indicates early myocardial dysfunction and predicts the worst prognosis when combined with wall motion and perfusion abnormalities. An integrated functional model combining CFVR and dynamic stress parameters enhances risk stratification for MACEs and supports individualized therapy.</p> Graphical Abstract <p></p>

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Prognostic value of adenosine stress echocardiography in chronic coronary syndromes with preserved left ventricular ejection fraction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Li Zhao,
  • Peng-Li Xu,
  • Qing-Yi Luo,
  • Xuan Su,
  • Shu-Han Ye,
  • Zi-Long Yang,
  • Xiao-Lei Song,
  • Qing-Hui Wang,
  • Yun-Chuan Ding

摘要

Background

The prognostic value of functional echocardiographic parameters for risk stratification in chronic coronary syndrome (CCS) remains incompletely understood. This study aimed to integrate resting and stress echocardiographic parameters to identify sensitive non-invasive predictors of major adverse cardiovascular events (MACEs).

Methods

A total of 754 CCS patients with a resting left ventricular ejection fraction (LVEF) ≥ 50% undergoing adenosine stress echocardiography were prospectively enrolled. Parameters including myocardial perfusion, coronary flow velocity reserve (CFVR), and myocardial work were assessed. Resting and stress values were compared within groups, while dynamic changes were analyzed between CFVR-normal and impaired subgroups. Cox regression was used to identify independent predictors.

Results

The incidence of MACEs was significantly higher in patients with impaired CFVR compared to those with normal CFVR (71.4% vs. 6.2%, P < 0.0001). After stress, the impaired CFVR group exhibited myocardial perfusion defects, mechanical dyssynchrony, and reduced myocardial work efficiency, in contrast to the normal CFVR group. Patients with impaired CFVR combined with regional wall motion and perfusion abnormalities had the worst prognosis. Multivariate Cox model integrating CFVR and stress-derived dynamic parameters demonstrated superior predictive performance for MACEs, significantly exceeding that of the stress-substitution and base models (C-index: 0.867 vs. 0.841 vs. 0.709).

Conclusion

In CCS patients with preserved LVEF, reduced CFVR indicates early myocardial dysfunction and predicts the worst prognosis when combined with wall motion and perfusion abnormalities. An integrated functional model combining CFVR and dynamic stress parameters enhances risk stratification for MACEs and supports individualized therapy.

Graphical Abstract