Hemispheric symptoms and carotid plaque echomorphology

AbstractPurpose: In patients with carotid bifurcation disease, the risk of stroke mainly depends on the severity of the stenosis, the presenting hemispheric symptom, and, as recently suggested, on plaque echodensity. We tested the hypothesis that asymptomatic carotid plaques and plaques of patients who present with different hemispheric symptoms are related to different plaque structure in terms of echodensity and the degree of stenosis. Methods: Two hundred sixty-four patients with 295 carotid bifurcation plaques (146 symptomatic, 149 asymptomatic) causing more than 50% stenosis were examined with duplex scanning. Thirty-six plaques were associated with amaurosis fugax (AF), 68 plaques were associated with transient ischemic attacks (TIAs), and 42 plaques were associated with stroke. B-mode images were digitized and normalized using linear scaling and two reference points, blood and adventitia. The gray scale median (GSM) of blood was set to 0, and the GSM of the adventitia was set to 190 (gray scale range, black = 0; white = 255). The GSM of the plaque in the normalized image was used as the objective measurement of echodensity. Results: The mean GSM and the mean degree of stenosis, with 95% confidence intervals, for plaques associated with hemispheric symptoms were 13.3 (10.6 to 16) and 80.5 (78.3 to 82.7), respectively; and for asymptomatic plaques, the mean GSM and the mean degree of stenosis were 30.5 (26.2 to 34.7) and 72.2 (69.8 to 74.5), respectively. Furthermore, in plaques related to AF, the mean GSM and the mean degree of stenosis were 7.4 (1.9 to 12.9) and 85.6 (82 to 89.2), respectively; in those related to TIA, the mean GSM and the mean degree of stenosis were 14.9 (11.2 to 18.6) and 79.3 (76.1 to 82.4), respectively; and in those related to stroke, the mean GSM and the mean degree of stenosis were 15.8 (10.2 to 21.3) and 78.1 (73.4 to 82.8), respectively. Conclusion: Plaques associated with hemispheric symptoms are more hypoechoic and more stenotic than those associated with no symptoms. Plaques associated with AF are more hypoechoic and more stenotic than those associated with TIA or stroke or those without symptoms. Plaques causing TIA and stroke have the same echodensity and the same degree of stenosis. These findings confirm previous suggestions that hypoechoic plaques are more likely to be symptomatic than hyperechoic ones. They support the hypothesis that the pathophysiologic mechanism for AF is different from that for TIA and stroke. (J Vasc Surg 2000;31:39-49.

Ultrasonic plaque character and outcome after lower limb angioplasty

AbstractPurpose: The value of ultrasonic plaque characteristics in identifying patients at “high-risk” of restenosis after percutaneous transluminal angioplasty (PTA) was studied. Methods: Thirty-one arterial stenoses (6 common iliac, 2 external iliac, 1 profunda femoris, 21 superficial femoral, and 1 popliteal) in 17 patients who underwent angioplasty were studied by means of duplex scanning. With a computer-based program, B-mode images were digitized and normalized using 2 reference points, blood and adventitia. A grey level of 0 to 5 was allocated for the lumen (blood) and 180 to 190 for the adventitia on a linear gray scale of 0 to 255 (0 = absolutely black; 255 = absolutely white), and the overall plaque gray-scale median (GSM) of the pixels of the plaque was used as a measure of plaque echodensity. After PTA, follow-up of stenoses was done on day 1, weekly for 8 weeks, at 3 months, 6 months, and 1 year. The total plaque thickness (sum of anterior and posterior components), minimal luminal diameter (MLD), and peak systolic velocity ratio (PSVR) were measured for all stenoses. An increase of more than 2 in the PSVR was the duplex criterion used to signify restenosis. Results: The GSM of the stenoses before angioplasty ranged from 6 to 71 (mean, 31.3 ± 17.9); 17 stenoses had a GSM less than 25 (mean, 18.7 ± 5.3), and 14 had a GSM more than 25 (mean, 46.4 ± 15.8). When the GSM was less than 25, the absolute reduction in plaque thickness on day 1 post-PTA was 3.3 ± 1.8 mm, in contrast to 1.8 ± 1.6 mm when GSM was more than 25 (P < .03). The restenosis rate (PSVR more than 2) was 41% at 6 months and remained unchanged at 1 year. When the GSM was less than 25, restenosis occurred in 11% of lesions, in comparison with 78% when the GSM was more than 25 (P < .001). Conclusion: Plaque echodensity can be used to evaluate stenoses before PTA, to predict initial success and identify a subgroup that has a high prevalence of restenosis. The identification of a group at “high-risk” of restenosis can improve the selection of patients for the procedure and also be used in prospective studies on the prevention of restenosis. (J Vasc Surg 1999;29:110-21.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients