Objective Methods to Assess Aorto-Iliac Calcifications: A Systematic Review

Author:

Fornasari Anna1ORCID,Kuntz Salomé23,Martini Chiara45ORCID,Perini Paolo15ORCID,Cabrini Elisa1ORCID,Freyrie Antonio15,Lejay Anne23,Chakfé Nabil23

Affiliation:

1. Vascular Surgery, Cardio-Thoracic and Vascular Department, Parma University Hospital, 43126 Parma, Italy

2. Vascular Surgery, Kidney Transplantation and Innovation, Department of Vascular Surgery, University Hospital of Strasbourg, 67085 Strasbourg, France

3. Gepromed, Medical Device Hub for Patient Safety, 67085 Strasbourg, France

4. Department of Diagnostic, Parma University Hospital, 43126 Parma, Italy

5. Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy

Abstract

Vascular calcifications in aorto-iliac arteries are emerging as crucial risk factors for cardiovascular diseases (CVDs) with profound clinical implications. This systematic review, following PRISMA guidelines, investigated methodologies for measuring these calcifications and explored their correlation with CVDs and clinical outcomes. Out of 698 publications, 11 studies met the inclusion criteria. In total, 7 studies utilized manual methods, while 4 studies utilized automated technologies, including artificial intelligence and deep learning for image analyses. Age, systolic blood pressure, serum calcium, and lipoprotein(a) levels were found to be independent risk factors for aortic calcification. Mortality from CVDs was correlated with abdominal aorta calcification. Patients requiring reintervention after endovascular recanalization exhibited a significantly higher volume of calcification in their iliac arteries. Conclusions: This review reveals a diverse landscape of measurement methods for aorto-iliac calcifications; however, they lack a standardized reproducibility assessment. Automatic methods employing artificial intelligence appear to offer broader applicability and are less time-consuming. Assessment of calcium scoring could be routinely employed during preoperative workups for risk stratification and detailed surgical planning. Additionally, its correlation with clinical outcomes could be useful in predicting the risk of reinterventions and amputations.

Publisher

MDPI AG

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