Development of a resting myocardial contrast echocardiography multi-indicator diagnostic model for coronary heart disease: a preliminary study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Studies have demonstrated a good concordance between myocardial contrast echocardiography (MCE) and single-photon emission computed tomography (SPECT) results for myocardial perfusion assessment. Moreover, when combined with multiple indicators, MCE has shown superior diagnostic ability for detecting myocardial hypoperfusion compared to SPECT. However, no MCE multi-indicator diagnostic model has been reported to date. We aimed to develop a diagnostic model using resting MCE for detecting myocardial hypoperfusion. Methods: Resting MCE and myocardial SPECT examinations were performed on 163 patients with clinically suspected or confirmed coronary heart disease (CHD) based on coronary computed tomography angiography (CCTA) or coronary angiography (CAG). A total of 360 myocardial segments were included in the MCE quantitative analysis (180 normal and 180 abnormal segments), categorized according to SPECT results. Key parameters-time to peak intensity (TP), duration time (T), peak intensity (PI), and myocardial blood flow (A × β)-were calculated to determine cutoff values for myocardial hypoperfusion. Logistic regression and receiver operating characteristic (ROC) curves were used to construct a multi-indicator MCE diagnostic model for myocardial hypoperfusion and evaluate its performance. Results: /s. The diagnostic model was defined as follows: logit P = 0.769 × TP + 0.871 × T - 0.662 × PI - 0.301 × A × β - 8.464. The model achieved a sensitivity and specificity of 97.9% and 96.8% for diagnosing myocardial hypoperfusion, respectively. Conclusions: The resting multi-indicator MCE diagnostic model demonstrated performance comparable to that of SPECT in detecting myocardial hypoperfusion. This model shows promise for CHD screening, follow-up, and prognostic evaluation.
Authors: Hui-Fang Lu, Xiao-Yan Huo, Bing-Yu Zhu, Feng Guo, Kang Ding, Xin Li
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414484160
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