Construction and validation of a diagnostic model for obstructive coronary artery disease in patients with suspected unstable angina pectoris based on resting 18F-fluorodeoxyglucose positron emission tomography myocardial ischemia memory imaging.

BACKGROUND: Identifying obstructive coronary artery disease (OCAD) via non-invasive imaging modalities in patients with suspected unstable angina (UA) holds substantial clinical significance. This study aimed to develop and validate a diagnostic model for OCAD in patients with suspected UA, by leveraging resting 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) myocardial ischemia memory imaging combined with clinical indicators. METHODS: We retrospectively analyzed 162 patients with a Global Registry of Acute Coronary Events (GRACE) score ≤140 who presented with chest pain or chest tightness and were clinically suspected of having UA. After collecting clinical indicators, predictive factors were screened using logistic regression. A diagnostic model was constructed using binary logistic regression based on the predictive factors, with internal validation via 1,000 bootstrap resamples. The discriminative ability, calibration, and clinical net benefit of the established model were evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Furthermore, the enhancement value of the final model over the basic model was quantified using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: Of the 162 enrolled patients with suspected UA, 89 (54.9%) were diagnosed with OCAD. Six predictors were incorporated into the diagnostic model, including hyperlipidemia, diabetes, typical angina pectoris, 18F-FDG PET results, serum creatinine, and left ventricular ejection fraction (LVEF). The area under the curve (AUC) of the model was 0.89 [95% confidence interval (CI): 0.84-0.94], with a sensitivity of 0.84 and a specificity of 0.81; the Brier score was 0.1319. The Hosmer-Lemeshow goodness-of-fit test revealed good model fit (χ2=6.15, P=0.63). After internal validation via the bootstrap method, the optimism-corrected AUC was 0.87 (95% CI: 0.82-0.93). Calibration curve and DCA demonstrated that the model exhibited satisfactory calibration and promising clinical utility. CONCLUSIONS: The OCAD diagnostic model for suspected UA patients, based on resting 18F-FDG PET and clinical indicators, demonstrated excellent diagnostic performance.