Prognostic Value of Qualitative and Quantitative Stress CMR in Patients With Known or Suspected CAD.

BACKGROUND

Recent studies suggest that quantitative cardiac magnetic resonance (CMR) may have more accuracy than qualitative CMR in coronary artery disease (CAD) diagnosis. However, the prognostic value of quantitative and qualitative CMR has not been compared systematically.

OBJECTIVES

The objective was to conduct a systematic review and meta-analysis assessing the utility of qualitative and quantitative stress CMR in the prognosis of patients with known or suspected CAD.

METHODS

A comprehensive search was performed through Embase, Scopus, Web of Science, and Medline. Studies that used qualitative vasodilator CMR or quantitative CMR assessments to compare the prognosis of patients with positive and negative CMR results were extracted. A meta-analysis was then performed to assess: 1) major adverse cardiovascular events (MACE) including cardiac death, nonfatal myocardial infarction (MI), unstable angina, and coronary revascularization; and 2) cardiac hard events defined as the composite of cardiac death and nonfatal MI.

RESULTS

Forty-one studies with 38,030 patients were included in this systematic review. MACE occurred significantly more in patients with positive qualitative (HR: 3.86; 95% CI: 3.28-4.54) and quantitative (HR: 4.60; 95% CI: 1.60-13.21) CMR assessments. There was no significant difference between qualitative and quantitative CMR assessments in predicting MACE (P = 0.75). In studies with qualitative CMR assessment, cardiac hard events (OR: 7.21; 95% CI: 4.99-10.41), cardiac death (OR: 5.63; 95% CI: 2.46-12.92), nonfatal MI (OR: 7.46; 95% CI: 3.49-15.96), coronary revascularization (OR: 6.34; 95% CI: 3.42-1.75), and all-cause mortality (HR: 1.66; 95% CI: 1.12-2.47) were higher in patients with positive CMR.

CONCLUSIONS

The presence of myocardial ischemia on CMR is associated with worse clinical outcomes in patients with known or suspected CAD. Both qualitative and quantitative stress CMR assessments are helpful tools for predicting clinical outcomes.