Aortic calcification index predicts mortality and cardiovascular events in operatively treated patients with peripheral artery disease A prospective PUREASO cohort follow-up study

Targeted drug delivery systems for atherosclerosis

Atherosclerosis is a complex cardiovascular disease driven by multiple factors, including aging, inflammation, oxidative stress, and plaque rupture. The progression of this disease is often covert, emphasizing the need for early biomarkers and effective intervention measures. In recent years, advancements in therapeutic strategies have highlighted the potential of targeting specific processes in atherosclerosis, such as plaque localization, macrophage activity, and key enzymes. Based on this, this review discusses the potential role of targeted drugs in the treatment of atherosclerosis. It also focuses on their clinical efficacy in anti-atherosclerosis treatment and their ability to provide more precise therapeutic approaches. The findings underscore that future research can concentrate on exploring newer drug delivery systems and biomarkers to further refine clinical treatment strategies and enhance the long-term dynamic management of atherosclerosis.

The Association between Major Adverse Cardiovascular Events and Peripheral Artery Disease Burden

OBJECTIVE

The aim of the present study was to investigate the possible relationship between the segmental burden of lower limb atherosclerosis and Major Adverse Cardiovascular Events (MACEs).

METHODS

All the consecutive symptomatic peripheral artery disease (PAD) patients admitted for digital subtraction angiography (DSA) at Turku University Hospital department of Vascular Surgery between 1 January 2009 and 30 July 2011 were retrospectively analyzed. Angiography due to symptomatic PAD was used as the index date for the inclusion in the study. The segmental burden of atherosclerosis based on DSA was divided into three categories according to the highest disease burden of the defined artery segment: aorto-iliac, femoropopliteal, or tibial segments. The major association for the study was MACEs (defined as a cerebrovascular event, heart failure (HF) and myocardial infarction requiring hospital admission). Demographic data and MACEs were obtained from the hospital electronic medical records system.

RESULTS

The lower limb atherosclerosis burden of tibial vessels was related to an increased probability for HF (OR 3.9; 95%CI 2.4-6.5) and for MACEs overall (OR 2.3; 95%CI 1.4-3.6). The probability of both HF and MACEs overall rose with the increasing severity of the atherosclerosis burden. Moreover, the more severe the tibial vessel atherosclerosis, the higher the risk of HF and MACEs. The most extensive tibial atherosclerosis patients had an OR 4.5; 95%CI 2.6-8.0 for HF and an OR 3.1; and 95%CI 1.7-5.6 for MACEs overall. The femoropopliteal disease burden was also associated with an increased risk of HF (OR 2.3; 95%CI 1.6-3.2) and MACE (OR 1.9; 95%CI 1.3-2.7). However, the increasing extent of atherosclerosis of the femoropopliteal segment solely increased the risk of MACEs.

CONCLUSIONS

PAD patients with severe tibial atherosclerosis are likely to present with MACEs. The risk is further enhanced as the extent of tibial vessel atherosclerosis is increased. An association between MACE and severe atherosclerosis on the aortoiliac segment was not detected. However, when the femoropopliteal segment was the most affected artery segment, the risk of MACEs was increased.

Quantifying the calcification of abdominal aorta and major side branches with deep learning

AIM

To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches.

MATERIALS AND METHODS

In total, 58 computed tomography (CT) angiography volumes were selected from a dataset of 609 to represent different stages of sclerosis. The ground truth segmentations of the abdominal aorta, coeliac trunk, superior mesenteric artery, renal arteries, common iliac arteries, and their calcifications were delineated manually. Two V-Net ensemble models were trained, one for segmenting arteries of interest and another for calcifications. The branches of interest were shortened algorithmically. The volumes of calcification were then evaluated from the arteries of interest.

RESULTS

The results indicate that automatic detection is possible with a high correlation to the ground truth. The scores for the ensemble calcification model were dice score of 0.69 and volumetric similarity (VS) of 0.80 and for the arteries of interest segmentations: aorta: dice 0.96, VS 0.98; aortic branches: dice 0.74, VS 0.87; and common iliac arteries: dice 0.72, VS 0.91.

CONCLUSIONS

The presented neural network model is the first to be capable of automatically segmenting, in addition to calcification, both the aorta and its branches from contrast-enhanced CT angiography. This technology shows promise in addressing limitations inherent in earlier methods that relied solely on plain CT.