Abstract
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Objectives: The aim of this study was to develop and validate a predictive model for concurrent presence of both myocardial ischemia and obstructive coronary artery disease (CAD) in patients with suspected CAD as a simple, non-invasive measures which incorporated traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV).
Methods: We retrospectively studied 164 Chinese patients with suspected CAD who underwent single photon emission computerized tomography-computed tomography (SPECT-CT) and coronary angiography (CAG) within 6 months. Myocardial ischemia was defined as reversible perfusion defect with summed difference score (SDS) ≥ 2. Obstructive CAD was defined as coronary stenosis severity ≥ 50%. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the traditional risk factors, CAC and EFV, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed by the bootstrap resampling method (times = 500).
Results: Among the 164 patients, 62 (37.8%) patients had both myocardial ischemia and obstructive CAD. Predictors contained in the individualized prediction nomogram included hypertension, diabetes mellitus, symptom, CAC and EFV. The model showed good discrimination, with a C-index of 0.886 (95% CI, 0.834 and 0.938 through internal validation), and good calibration. The calibration curve for the probability of concurrent presence of both myocardial ischemia and obstructive CAD demonstrated good agreement between prediction and observation. Decision curve analysis showed that the integrative model was clinically useful.
Conclusions: In Chinese patients with suspected CAD, integrative model including hypertension, diabetes mellitus, symptom, CAC and EFV shows good performance in predicting concurrent presence of both myocardial ischemia and obstructive CAD. The integrative model provides a useful method for risk stratification and clinical management.