Background: Clinical implications of different types of vascular calcification are poorly understood. The two most abundant forms of calcification, nodular and sheet calcification, have not been quantitatively analyzed in relation to the clinical presentation of lower extremity arterial disease (LEAD).
Methods: The study analyzed 51 femoral artery plaques collected during femoral endarterectomy, characterized by the presence of > 90% stenosis. Comprehensive clinical data was obtained from patient records, including magnetic resonance angiography (MRA) images, toe pressure and ankle brachial index measurements and laboratory values. The plaques were longitudinally sectioned, stained with Hematoxylin and Eosin and digitized in a deep learning platform for quantification of the relative area of nodular and sheet calcification to the plaque section area. A deep learning artificial intelligence algorithm was designed and independently validated to reliably quantify nodular calcification and sheet calcification. Vessel measurements and quantity of each calcification category was compared to the risk factors and clinical presentation.
Results: On average, > 90% stenosed vessels contained 22.4 ± 12.3% of nodular and 14.5 ± 11.8% of sheet calcification. Nodular calcification area proportion in lesions with > 90% stenosis is associated with reduced risk of critically low toe pressure (< 30 mmHg) (OR = 0.910, 95% CI = 0.835-0.992, p < 0.05), severely lowered ankle brachial index (< 0.4) (OR = 0.912, 95% CI = 0.84-0.986, p < 0.05), and semi-urgent operation (OR = 0.882, 95% CI = 0.797-0.976, p < 0.05). Sheet calcification did not show any significant association.
Conclusions: Large amount of nodular calcification is associated with less severe LEAD. Patients with nodular calcification may have better flow reserves despite local obstruction.
Keywords: Atherosclerosis; Deep learning algorithm; Lower extremity arterial disease; Nodular calcification; Sheet calcification; Vascular calcification.
© 2022. The Author(s).