Several recent trials have evaluated invasive versus medical therapy for stable ischemic heart disease. Importantly, patients with significant left main coronary stenosis (LMCS) were excluded from these trials. In the ISCHEMIA trial, these patients were identified by a coronary CT angiogram (CCTA), which adds time, expense, and contrast exposure. We tested whether a coronary artery calcium scan (CACS), a simpler, less expensive test, could replace CCTA to exclude significant LMCS.

We hypothesized that patients with ≥50% LMCS would have a LM CACS score > 0. As a corollary, we postulated that a LM CACS = 0 would exclude patients with LMCS. To test this, we searched Intermountain Healthcare's electronic medical records database for all adult patients who had undergone non-contrast cardiac CT for quantitative CACS scoring prior to invasive coronary angiography (ICA). Patients aged <50 and those with a heart transplant were excluded. Cases with incomplete (qualitative) angiographic reports for LMCS and those with incomplete or discrepant LM CACS results were reviewed and reassessed blinded to CACS or ICA findings, respectively.

Among 669 candidate patients with CACS followed by ICA, 36 qualifying patients were identified who had a quantitative CACS score and LMCS ≥ 50%. Their age averaged 71.8 years, and 81% were men. Angiographic LMCS averaged 72% (range 50%-99%). Median time between CACS and ICA was 6 days. Total CACS score averaged 2,383 Agatston Units (AU), range 571-6,636. LM CACS score averaged 197 AU, range 31-610. Importantly, no LMCS patient had a LM CACS score of 0 vs 57% (362/633) of non-LMCS controls (P < .00001).

Our results support the hypothesis that an easily administered, inexpensive, low radiation CACS can identify a large subset of patients with a very low risk of LMCS who would not have the need for routine CCTA. Using CACS to exclude LMCS may efficiently allow for safe implementation of an initial medical therapy strategy of patients with stable ischemic heart disease in clinical practice. These promising results deserve validation in larger data sets.