Key result
A prediction model combining rest-only ECG-gated SPECT MPI and cardiovascular risk factors effectively predicted obstructive CAD with an AUC of 0.795, significantly outperforming traditional risk factor models.
Why the study?
Rest-only SPECT MPI has low sensitivity for diagnosing obstructive CAD, creating a need to improve its diagnostic efficacy for patients with contraindications to stress.
Does a prediction model combining rest-only ECG-gated SPECT MPI and cardiovascular risk factors improve the prediction of obstructive CAD in patients with suspected CAD and contraindications to stress MPI?
Cohort (n=260)
No
Does a prediction model combining rest-only ECG-gated SPECT MPI and cardiovascular risk factors improve the prediction of obstructive CAD in patients with suspected CAD and contraindications to stress MPI?
Absolute Event Rate: 0.795% vs 0.725%
p-value: p=0.005
A prediction model combining rest-only ECG-gated SPECT MPI parameters and cardiovascular risk factors effectively predicts obstructive CAD in patients with contraindications to stress testing.
May enhance rest-only MPI accuracy for obstructive CAD; leaves open prospective validation before clinical adoption.
OBJECTIVE: The rest-only single photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) had low sensitivity in diagnosing obstructive coronary artery disease (CAD). Improving the efficacy of resting MPI in diagnosing CAD has important clinical significance for patients with contraindications to stress. The purpose of this study was to develop and validate a model predicting obstructive CAD in suspected CAD patients, based on rest-only MPI and cardiovascular risk factors. METHODS: A consecutive retrospective cohort of 260 suspected CAD patients who underwent rest-only gated SPECT MPI and coronary angiography was constructed. All enrolled patients had stress MPI contraindications. Clinical data such as age and gender were collected. Automated quantitative analysis software QPS and QGS were used to evaluate myocardial perfusion and function parameters. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the prediction model. RESULTS: Among the enrolled 260 patients with suspected CAD, there were 95 (36.5%, 95/260) patients with obstructive CAD. The prediction model was presented in the form of a nomogram and developed based on selected predictors, including age, sex, SRS ≥ 4, SMS ≥ 2, STS ≥ 2, hypertension, diabetes, and hyperlipidemia. The AUC of the prediction model was 0.795 (95% CI: 0.741-0.843), which was better than the traditional models. The AUC calculated by enhanced bootstrapping validation (500 bootstrap resamples) was 0.785. Subsequently, the calibration curve (intercept = - 0.106; slope = 0.843) showed a good calibration of the model. The decision curve analysis (DCA) shows that the constructed clinical prediction model had good clinical applications. CONCLUSIONS: In patients with suspected CAD and contraindications to stress MPI, a prediction model based on rest-only ECG-gated SPECT MPI and cardiovascular risk factors have been developed and validated to predict obstructive CAD effectively.
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Liu et al. (2022) conducted a cohort in Suspected coronary artery disease (n=260). Prediction model combining rest-only ECG-gated SPECT MPI and cardiovascular risk factors vs. Traditional cardiovascular risk factors model was evaluated on Prediction of obstructive CAD (Area Under the Curve) (95% CI 0.741-0.843, p=0.005). A prediction model combining rest-only ECG-gated SPECT MPI and cardiovascular risk factors effectively predicted obstructive CAD with an AUC of 0.795, significantly outperforming traditional risk factor models.
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