Incremental value of regional wall motion abnormalities for detecting obstructive coronary artery disease by rest-only electrocardiogram-gated single-photon emission computerized tomography myocardial perfusion imaging in suspected coronary artery disease patients

Regional wall motion abnormalities on focused transthoracic echocardiography in patients presenting with acute chest pain: a predefined post hoc analysis of the prospective single-centre observational EPIC-ACS study

OBJECTIVES

We evaluated the ability of the assessment of regional wall motion abnormalities (RWMA) detected via transthoracic echocardiography to predict the presence of obstructive coronary artery disease (CAD) in patients presenting with acute chest pain to the emergency department.

DESIGN

Prospective single-centre observational study.

SETTING

Tertiary care university hospital emergency unit.

PARTICIPANTS

Patients presenting to the emergency department with acute chest pain suggestive of obstructive CAD.

PRIMARY OUTCOME MEASURE

The primary endpoint was defined as the presence of obstructive CAD, requiring revascularisation therapy.

RESULTS

Overall, 657 patients (age 58.1±18.0 years, 53% men) were included in our study. RWMA were detected in 76 patients (11.6%). RWMA were significantly more frequent in patients reaching the primary endpoint (26.2% vs 7.6%, p<0.001). In multivariable regression analysis, the presence of RWMA was associated with threefold increased odds of the presence of obstructive CAD (3.41 (95% CI 1.99 to 5.86), p<0.001). Adding RWMA to a multivariable model of the Thrombolysis in Myocardial Infarction (TIMI) risk score, cardiac biomarkers and traditional risk factors significantly improved the area under the curve for prediction of obstructive CAD (95% CI 0.777 to 0.804, p=0.0092).

CONCLUSION

RWMA strongly and independently predicts the presence of obstructive CAD in patients presenting with acute chest pain to the emergency department.

TRIAL REGISTRATION

The study has been registered online (NCT03787797).

A model combining rest-only ECG-gated SPECT myocardial perfusion imaging and cardiovascular risk factors can effectively predict obstructive coronary artery disease

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.