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Detection of Coronary Inflammation Using CT: CRISP CT Trial
Study Questions:
What is the utility of perivascular fat attenuation index (FAI) for clinical risk stratification?
Methods:
The CRISP-CT (Cardiovascular RISk Prediction using Computed Tomography) study investigators conducted a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary computed tomography angiography (CTA) in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries—the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. They assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features. The prognostic value of the perivascular FAI for all-cause, cardiac, or noncardiac mortality was assessed using multivariable Cox regression models, and plotted with Kaplan-Meier curves.
Results:
Between 2005 and 2009, 1,872 participants in the derivation cohort underwent coronary CTA (median age, 62 years [range 17–89]). Between 2008 and 2016, 2,040 patients in the validation cohort had coronary CTA (median age, 53 years [range 19–87]). Median follow-up was 72 months (range 51–109) in the derivation cohort and 54 months (range 4–105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR], 2.15; 95% confidence interval [CI], 1.33–3.48; p = 0.0017 in the derivation cohort, and 2.06, 1.50–2.83; p < 0,0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as –70.1 Hounsfield units (HU) or higher in the derivation cohort (HR, 9.04; 95% CI, 3.35–24.40; p < 0.0001 for cardiac mortality; 2.55, 1.65–3.92; p < 0.0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR, 5.62; 95% CI, 2.90–10.88; p < 0.0001 for cardiac mortality; 3.69, 2.26–6.02; p < 0.0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality.
Conclusions:
The authors concluded that perivascular FAI enhances cardiac risk prediction and restratification over and above current state of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation.
Perspective:
This study reports that the imaging biomarker perivascular FAI predicts all-cause and cardiac mortality over and above clinical risk factors and the current interpretation of coronary CTA. Furthermore, by quantifying the residual inflammatory risk, the perivascular FAI can be used for risk restratification in both primary and secondary prevention, dissociating risk prediction from the anatomical severity of coronary stenosis or the degree of myocardial ischemia. The perivascular FAI also appears to identify vulnerable plaques in patients with established coronary atherosclerosis, and this biomarker could potentially guide individualized use of intensive and expensive measures of secondary prevention such as PCSK9 inhibitors.
Keywords: ESC Congress, ESC18, Biomarkers, Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Stenosis, Diagnostic Imaging, Inflammation, Myocardial Ischemia, Plaque, Atherosclerotic, Risk Factors, Secondary Prevention, Tomography, X-Ray Computed
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