Key result
CMR quantification of ischemia and LGE improves risk prediction for adverse cardiac events.
Why the study?
No standardized consensus exists on how to assess and quantify myocardial ischemia and necrosis to improve prognostic models and patient management in CAD.
Does CMR-derived quantification of myocardial ischemia and scar improve risk stratification in patients with known or suspected coronary artery disease?
Cohort (n=845)
Blinded to patient history and symptoms
No
Does CMR-derived quantification of myocardial ischemia and scar improve risk stratification in patients with known or suspected coronary artery disease?
Hazard Ratio: 1.056 (95% CI 1.036–1.076)
p-value: p=<0.0001
Quantification of myocardial ischemia and scar by CMR significantly improves cardiovascular risk prediction in patients with stable coronary artery disease.
May refine CAD risk stratification; hypothesis-generating for CMR-guided management trials.
BACKGROUND: Quantification of myocardial ischemia and necrosis might ameliorate prognostic models and lead to improved patient management. However, no standardized consensus on how to assess and quantify these parameters has been established. The aim of this study was to quantify these variables by cardiac magnetic resonance imaging (CMR) and to establish possible incremental implications in cardiovascular risk prediction. METHODS: This study is a retrospective analysis of patients with known or suspected coronary artery disease (CAD) referred for adenosine perfusion CMR was performed. Myocardial ischemia and necrosis were assessed and quantified using an algorithm based on standard first-pass perfusion imaging and late gadolinium enhancement (LGE). The combined primary endpoint was defined as cardiac death, non-fatal myocardial infarction, and stroke. RESULTS: 845 consecutive patients were enrolled into the study. During the median follow-up of 3.64 [1.03; 10.46] years, 61 primary endpoints occurred. Patients with primary endpoint showed larger extent of ischemia (10.7 ± 12.25% vs. 3.73 ± 8.29%, p < 0.0001) and LGE (21.09 ± 15.11% vs. 17.73 ± 10.72%, p < 0.0001). A risk prediction model containing the extent of ischemia and LGE proved to be superior in comparison to all other models (χ² increase: from 39.678 to 56.676, integrated discrimination index: 0.3851, p = 0.0033, net reclassification index: 0.11516, p = 0.0071). The ben-eficial effect of revascularization tended to be higher in patients with greater extents of ischemia, though statistical significance was not reached. CONCLUSIONS: Quantification of myocardial ischemia and LGE was shown to significantly improve existing risk prediction models and might thus lead to an improvement in patient management.
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Buckert et al. (2017) conducted a cohort in Stable coronary artery disease (n=845). Quantification of myocardial ischemia and late gadolinium enhancement (LGE) by CMR vs. Basic clinical and CMR risk prediction models was evaluated on Combined primary endpoint of cardiac death, non-fatal myocardial infarction, and stroke (HR 1.056, 95% CI 1.036-1.076, p=<0.0001). Quantification of myocardial ischemia and late gadolinium enhancement by CMR significantly improved risk prediction models for adverse cardiac events (net reclassification index 0.11516, p=0.0071).
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