Fractal analysis of plaque border, a novel method for the quantification of atherosclerotic plaque contour irregularity, is associated with pro-atherogenic plasma lipid profile in subjects with non-obstructive carotid stenoses

<div><p>Background and aims</p><p>Plaque border irregularity is a known imaging characteristic of vulnerable plaques, but its evaluation heavily relies on subjective evaluation and operator expertise. Aim of the present work is to propose a novel fractal-analysis based method for the quantification of atherosclerotic plaque border irregularity and assess its relation with cardiovascular risk factors.</p><p>Methods and results</p><p>Forty-two asymptomatic subjects with carotid stenosis underwent ultrasound evaluation and assessment of cardiovascular risk factors. Total, low-density lipoprotein (LDL), high-density lipoprotein (HDL) plasma cholesterol and triglycerides concentrations were measured for each subject. Fractal analysis was performed in all the carotid segments affected by atherosclerosis, i.e. 147 segments. The resulting fractal dimension (FD) is a measure of irregularity of plaque profile on long axis view of the plaque. FD in the severest stenosis (main plaque FD,mFD) was 1.136±0.039. Average FD per patient (global FD,gFD) was 1.145±0.039. FD was independent of other plaque characteristics. mFD significantly correlated with plasma HDL (r = -0.367,p = 0.02) and triglycerides-to-HDL ratio (r = 0.480,p = 0.002).</p><p>Conclusions</p><p>Fractal analysis is a novel, readily available, reproducible and inexpensive technique for the quantitative measurement of plaque irregularity. The correlation between low HDL levels and plaque FD suggests a role for HDL in the acquisition of morphologic features of plaque instability. Further studies are needed to validate the prognostic value of fractal analysis in carotid plaques evaluation.</p></div

Fractal analysis of all segments involved by atherosclerosis in a representative subject.

<p>Panel A shows the image of the right carotid bifurcation, in which segments affected by atherosclerosis can be identified: the internal carotid artery (denoted with c) and the carotid bulb, the latter by a plaque extending in the external carotid artery (d). Panel B displays the left carotid bifurcation of the same patient. Again, two involved segments can be identified: the internal carotid artery (by a plaque extending from the carotid bulb, e) and the carotid bulb (f). Panels C-F display the magnified contour extracted from plaque c-f respectively. Fractal dimension (FD) for each contour is indicated in each panel. The total number of involved segments in this patient was 4. Average FD was 1.137.</p

Fractal analysis of a plaque located in the right carotid artery of a representative subject.

<p>Panel A shows the longitudinal view of a non-obstructive plaque located in the right common carotid artery. Panel B shows the same image after binarization using a modified IsoData algorithm, while Panel C displays contour extraction by computing edges in the areas of highest gradient magnitude using the Sobel operator. Panel D shows two grids of different scales generated as part of the evaluation of fractal dimension (FD) using a box counting algorithm. The FD of the plaque border corresponds to the opposite of the slope of logarithmic plot of the number of boxes containing the objects (Y, Ln Count) vs. the dimension of the boxes side (X, Ln<b>ε</b>). The higher is the FD, the higher is the irregularity of the plaque surface.</p

Bland Altman plot for fractal analysis.

<p>Panel A shows Bland-Altman plot for inter-operator reproducibility analysis, while Panel B shows results for intra-operator analysis.</p

Correlation between fractal dimension, plasma HDL cholesterol and triglycerides-to-HDL ratio.

<p>Panel A-B compare and contrast two representative patients. The subject in Panel A appear to have a higher plaque border complexity and lower HDL-C compared to the subject in panel B. Lower panels show a magnified image of plaque contour. Panel C displays the scatter plot of fractal dimension versus HDL-C, while Panel D shows the scatter plot of fractal dimension versus triglycerides-to-HDL ratio. HDL-C = high density lipoprotein cholesterol; FD = fractal dimension; Trig:HDL = triglycerides-to-HDL ratio.</p

Population characteristics.

<p>Population characteristics.</p

Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: results of an international multi-centre study

Introduction: Increased mortality has been demonstrated in older adults with coronavirus disease 2019 (COVID-19), but the effect of frailty has been unclear. Methods: This multi-centre cohort study involved patients aged 18 years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS) and delirium on risk of increased care requirements on discharge, adjusting for the same variables. Results: Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, interquartile range [IQR] 54–83; 55.2% male). The risk of death increased independently with increasing age (>80 versus 18–49: hazard ratio [HR] 3.57, confidence interval [CI] 2.54–5.02), frailty (CFS 8 versus 1–3: HR 3.03, CI 2.29–4.00) inflammation, renal disease, cardiovascular disease and cancer, but not delirium. Age, frailty (CFS 7 versus 1–3: odds ratio 7.00, CI 5.27–9.32), delirium, dementia and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9. Conclusion: Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age.</p