Non-diagnostic coronary artery calcification and stenosis: a correlation of coronary computed tomography angiography and invasive coronary angiography

Abstract

Background Heavy coronary artery calcification (CAC) impairs diagnostic accuracy of coronary computed tomography angiography (cCTA) and is considered to be a major limitation. Purpose To investigate the effect of non-evaluable CAC seen on cCTA on clinical decision-making by determining the degree of subsequent invasive testing and to assess the relationship between non-evaluable segments containing CAC and significant stenosis as seen in invasive coronary angiography (ICA). Material and Methods The study comprised of 356 patients who underwent cCTA and subsequent ICA within 2 months between 2005 and 2009. Clinical reports were reviewed to identify the indications for referral to ICA. In a subset of 68 patients where non-diagnostic CAC on cCTA and significant stenosis on ICA were present in the same segment, we correlated and analyzed the underlying stenosis severity of the lesion on ICA to the cCTA. Lesions with CAC were analyzed in a standardized fashion by application of reading rules. Results Non-diagnostic CAC on cCTA prompted ICA in 5.6% of patients. CAC occurred at the site of maximum stenosis in segments with stenosis <50% (95.9% [47/49]), 50-69% (82.4% [28/34]), 70-99% (64.5% [31/48]), and 100% (33.3% [1/3]). At the point of maximum calcium deposit, non-obstructive disease was present in 61.2%. Application of reading rules resulted in a 44% reduction in non-diagnostic cCTA reads. Conclusion Severe CAC may prompt further investigation with ICA. There is less CAC with increasing lesion severity at the point of maximum stenosis. Additional application of reading rules improved non-diagnostic cCTA reads.

Pre-screening for non-diagnostic coronary computed tomography angiography

“…When test results are of insufficient quality to provide meaningfully inform clinical decision-making, the non-diagnostic CCTA study may lead to misdiagnoses, inappropriate downstream resource utilization, delays in patient diagnosis, and increased healthcare costs. 5 We developed a prediction model that could be implemented at the ‘point-of-scheduling’ to identify patients who would be best served by another non-invasive diagnostic test.…”

“…3 , 4 With the wider adoption of CCTA, there is concern that indiscriminate referrals could result in a higher rate of equivocal and non-diagnostic studies, or false positive and false negative findings, leading to inappropriate downstream resource utilization and delays in diagnosis. 5 We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test. We validated the predictor selection and prediction performance using machine learning (ML).…”