Radial Artery Tonometry is Associated With Major Adverse Cardiac Events in Patients With Peripheral Artery Disease

The effect of successful lower extremity revascularization on aortic stiffness in patients with peripheral arterial disease

OBJECTIVE

Peripheral arterial disease (PAD) is a global health problem and associated with poor outcomes. It causes increased arterial stiffness. The association of PAD with aortic arterial stiffness was investigated in previous studies. However, there is limited data regarding the effect of peripheral revascularization on arterial stiffness. The aim of our study is to investigate the effect of peripheral revascularization on aortic stiffness parameters in patients with symptomatic PAD.

METHODS

A total of 48 patients with PAD who underwent peripheral revascularization were included in the study. Echocardiography was performed before and after the procedure, and aortic stiffness parameters were obtained by using aortic diameters and arterial blood pressure measurements.

RESULTS

Post-procedural aortic strain (5.1 [1.3-14] vs. 6.3 [2.8-6.3], p = 0.009) and aortic distensibility (0.2 [0.0-0.9] vs. 0.3 [0.1-1.1], p = 0.001) measurements were significantly increased compared to pre-procedural values. Patients were also compared according to the lesion laterality, site and treatment methods. It was found that the change in aortic strain (p = 0.031) and distensibility (p = 0.043) were significantly higher in unilateral lesion compared to bilateral lesion. Also, the change in aortic strain (p = 0.042) and distensibility (p = 0.033) were significantly higher in iliac site lesion compared to superficial femoral artery (SFA) site lesion. Moreover, the change in aortic strain was significantly higher (p = 0.013) in patients treated with stent compared to only balloon angioplasty.

CONCLUSION

Our study showed that successful percutaneous revascularization significantly reduced aortic stiffness in PAD. The change in aortic stiffness was significantly higher in unilateral lesions, iliac site lesions and stent-treated lesions.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.