Additive value of semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome.

Versteylen MO, Kietselaer BL, Dagnelie PC
J Am Coll Cardiol. 2013 Jun 4;61(22):2296-305. doi: 10.1016/j.jacc.2013.02.065

Background

Effective use of imaging diagnostics may be useful in improving the assessment of cardiovascular risk in individuals. Coronary computed tomographic angiography (CCTA)is now widely implemented in clinical practice and can be used to rule out coronary artery disease. Also the predictive value of CCTA seems promising, specifically when maximal luminal stenosis severity is used as a prognostic parameter [1,2]. CCTA can furthermore identify morphological and geometric characteristics of atherosclerotic plaques [3]. Positive plaque remodeling and the presence of low-attenuation plaque core have been recognized as risk factors for the occurrence of ACS [4,5]. These characteristics are, however, not regularly reported. Standardisation of measurement and a semiautomated approach is currently lacking.
Although validated methods to quantify coronary plaque morphology exist, no clinical data on incremental value of plaque quantification over conventional reading of CAD using CCTA is available. This study aims to investigate the additional predictive value of the use of a semiautomated quantification algorithm over conventional CCTA reading (consisting of calcium score, luminal stenosis, extend of CAD and morphology assessment). 1650 patients with stable chest pain were followed for a mean of 26+10 months for the occurrence of ACS, which occurred in 29 patients.

Main results

• Conventional CT-derived parameters did now show predictive value for ACS. In contrast, the semiautomated quantitative parameters total plaque volume, total number of plaques and total non-calcified volume were all higher (P<0.02 for all) in the ACS group than in randomly selected control patients. Patients who had an ACS also had significantly higher maximal plaque volume, maximal  plaque burden, maximal plaque area, maximal percentage noncalcified plaque, minimal plaque attenuation and maximal remodelling (P<0.01 for all).
• After transforming all characteristics into dichotomous variables, sum scores were calculated for the conventional CT parameters and for the 5 semi-automated quantitative CT parameters with the best accuracy. When comparing the quantification model with the conventional model, 24% of the event group was reclassified into a higher risk group, and 3% of the nonevent group was reclassified into the lower risk group.
• Four different incremental risk profiling models were assessed for their added value. While the area under curve (AUC) for the Framingham risk score (FRS) alone was 0.59 (95%CI: 0.45-0.73), it increased to 0.67 (95%CI: 0.54-0.79) when adding calcium score to FRS. A model incorporating FRS and conventional CT parameters showed AUC of 0.64 (95%CI: 0.52-0.76). Addition of the semiautomated quantification parameters to the method significantly improved the AUC to 0.79 (95%CI: 0.69-0.90, P=0.047).

Conclusion

Semiautomated quantified plaque characteristics provide additional prognostic value over both clinical risk profiling and conventional CT reading. Implementation of such a semiautomated plaque quantification algorithm in clinical practice may improve stratification for ACS risk in patients with stable chest pain undergoing CCTA.

References

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