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: The TP and T values of abnormal segments were significantly longer than those of normal segments (P<0.01). Conversely, the PI and A × β values were significantly lower in abnormal segments than in normal segments (P<0.01). The cutoff values for predicting myocardial hypoperfusion were as follows: TP =6.54 s, T =13.36 s, PI =6.67 dB, and A × β =8.70 dB(2)/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.