Predicting the risk of hemodynamically significant coronary artery stenosis based on the development of a mathematical model

Objective: to develop a model for predicting the risk of hemodynamically significant coronary atherosclerosis in patients with a pretest probability of coronary heart disease greater than 15%.

Materials and methods: in 56 patients matched for age and gender with a presumed diagnosis of coronary artery disease, the coronary artery calcification index (CAI) was calculated using the Agatston method, GLSavg was assessed, and selective coronary angiography was performed. Using Statistica 12.0 (StatSoft, USA), a mathematical model was developed using logistic regression to calculate the K coefficient, which allows for determining the indications for selective angiography.

Results: patients in the study groups were randomized based on the Agaston index as follows: group 1 — 74.0±17.82 U, group 2 — 297.0±79.83 U, group 3 — 716.0±116.24 U. Global longitudinal strain of the left ventricle was significantly higher in group 1 and did not differ between groups 2 and 3. A mathematical model was then developed to calculate the K coefficient, where the cutoff level for assessing the risk of detecting hemodynamically significant stenosis was 0.37. The diagnostic sensitivity was 92.86% and specificity 89.29% (p<0.0001).

Conclusion: this study demonstrates the importance of developing tools for predicting the presence of hemodynamically significant stenosis based not only on structural changes in the coronary arteries, including calcification, but also on global left ventricular systolic function.

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