Quantitative Analysis of Vascular Calcification Using a Novel Semi-Automated Software

Shubhasree Banerjee¹, Mohammadhadi Bagheri², Veit Sandfort³, Ashkan Malayeri⁴, Mark Ahlman⁴, David A. Bluemke⁴, Jianhua Yao² and Peter C. Grayson⁵, ¹Fellowship and training branch, NIAMS/NIH, Bethesda, MD, ²Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, ³Clinical Center, Radiology and Imaging Sciences, NIH, Bethesda, MD, ⁴Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, ⁵Research, National Institutes of Health, Bethesda, MD

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Calcium and vasculitis

Session Information

Date: Sunday, November 5, 2017

Title: Imaging of Rheumatic Diseases Poster I

Session Type: ACR Poster Session A

Session Time: 9:00AM-11:00AM

Background/Purpose:

Calcification of the coronary arteries, aorta, and branch vessels can occur in both large vessel vasculitis (LVV) and atherosclerosis. The study objective was to determine 1) the location and amount of vascular calcification in LVV versus hyperlipidemia (HLD) and 2) risk factors associated with vascular calcification in LVV.

Methods:

Patients with giant cell arteritis (GCA), Takayasu’s arteritis (TAK), and HLD were recruited into an observational cohort. All subjects underwent computed tomography of the aorta and branch vessels. We developed a novel semi-automated software to compute vascular calcification in 14 specific arterial territories (ascending aorta, aortic arch, descending thoracic aorta, abdominal aorta, carotids, subclavians, innominate, iliacs, femorals and coronary arteries). Total amount of calcification throughout the large arteries was quantified by calculating a cumulative Agatston score. Multivariate linear regression analyses were performed in LVV to determine associations between total Agatston score and traditional or disease-specific risk factors. Traditional risk factors were age, gender, body mass index, smoking, statin use and hypertension. Disease-specific risk factors were disease duration, clinical activity status, glucocorticoid dose, inflammatory markers (ESR, CRP, fibrinogen) and vascular inflammation as measured by positron emission tomography (FDG-PET). Only variables with p<0.10 in univariate analyses were included in the multivariate models. Frequencies were compared by the chi-squared test. Agatston scores were compared by Kruskal-Wallis test with post-hoc Dunn’s test.

Results:

A total of 88 subjects, including GCA (n=29, median age=72, %female=79); TAK (n=22, median age=37, %female=73); and HLD (n=37, median age= 66, %female=43), participated in the study. There were no differences in the location of vascular calcification in the aorta and branch vessels between LVV and HLD, except coronary artery calcification was more prevalent in HLD compared to both TAK and GCA (p<0.01). Total Agatston scores were higher in GCA (median 3260, range 25-18138) versus HLD (460.5, 19-17215) (p<0.01) but did not significantly differ between GCA and TAK (1944, 52-47520) (p=0.53). An Agatston score >1000, consistent with severe calcification burden, was observed in many patients with GCA (74%), TAK (56%), and HLD (42%). Factors associated with calcification in LVV are shown in the table.

Conclusion:

We have developed a novel software to quantify calcification in vascular territories. This software could be repurposed to calculate calcification burden in other regions of interest. Location of vascular calcification was found to be similar between LVV and HLD; however, the amount of calcification was higher in patients with LVV. Both traditional and disease-specific risk factors are associated with vascular calcification in LVV.

Table

Variable	Univariable		Multivariable
	Estimate	P Value	Estimate	P value
Age	0.06 (1.06)	0.01	0.14 (1.15)	<0.01
Diagnosis *(TAK vs GCA)*	-0.96 (0.38)	0.30	4.02 (55.70)	0.03
Prednisone dose	0.05 (1.16)	0.08	0.05 (1.05)	0.04
Hypertensive Medication	2.43 (11.35)	<0.01	1.16 (3.19)	0.19
Gender *(Female vs Male)*	0.85 (2.3)	0.04	0.12 (1.12)	0.90
C-reactive Protein	-0.03 (0.97)	0.11	Not included in multivariable model.
FDG-PET interpretation	0.99 (2.69)	0.29
Statin Medication	1.49 (4.43)	0.17
Treatment duration	0 (1)	0.53
Clinical Assessment	0.44(1.55)	0.65
Disease duration	0 (1)	0.74
Body mass index	0.02 (1.02)	0.77
Erythrocyte sedimentation rate	0.003 (1.0)	0.89

Disclosure: S. Banerjee, None; M. Bagheri, None; V. Sandfort, None; A. Malayeri, None; M. Ahlman, None; D. A. Bluemke, None; J. Yao, None; P. C. Grayson, None.

To cite this abstract in AMA style:

Banerjee S, Bagheri M, Sandfort V, Malayeri A, Ahlman M, Bluemke DA, Yao J, Grayson PC. Quantitative Analysis of Vascular Calcification Using a Novel Semi-Automated Software [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/quantitative-analysis-of-vascular-calcification-using-a-novel-semi-automated-software/. Accessed .

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