The efficacy of the new SCD Response Compression System in the prevention of venous stasis

AbstractObjective: The current commercially available sequential intermittent pneumatic compression device used for the prevention of deep venous thrombosis has a constant cycle of 11 seconds’ compression and 60 seconds’ deflation. This deflation period ensures that the veins are filled before the subsequent cycle begins. It has been suggested that in some positions (eg, semirecumbent or sitting) and with different patients (eg, those with venous reflux), refilling of the veins may occur much earlier than 60 seconds, and thus a more frequent cycle may be more effective in expelling blood proximally. The aim of the study was to test the effectiveness of a new sequential compression system (the SCD Response Compression System), which has the ability to detect the change in the venous volume and to respond by initiating the subsequent cycle when the veins are substantially full. Methods: In an open controlled trial at an academic vascular laboratory, the SCD Response Compression System was tested against the existing SCD Sequel Compression System in 12 healthy volunteers who were in supine, semirecumbent, and sitting positions. The refilling time sensed by the device was compared with that determined from recordings of femoral vein flow velocity by the use of duplex ultrasound scan. The total volume of blood expelled per hour during compression was compared with that produced by the existing SCD system in the same volunteers and positions. Results: The refilling time determined automatically by the SCD Response Compression System varied from 24 to 60 seconds in the subjects tested, demonstrating individual patient variation. The refilling time (mean ± SD) in the sitting position was 40.6 ± 10.0 seconds, which was significantly longer (P <.001) than that measured in the supine and semirecumbent positions, 33.8 ± 4.1 and 35.6 ± 4.9 seconds, respectively. There was a linear relationship between the duplex scan–derived refill time (mean of 6 readings per leg) and the SCD Response device–derived refill time (r = 0.85, P <.001). The total volume of blood (mean ± SD) expelled per hour by the existing SCD Sequel device in the supine, semirecumbent, and sitting positions was 2.23 ± 0.90 L/h, 2.47 ± 0.86 L/h, and 3.28 ± 1.24 L/h, respectively. The SCD Response device increased the volume expelled to 3.92 ± 1.60 L/h or a 76% increase (P =.001) in the supine position, to 3.93 ± 1.55 L/h or a 59% increase (P =.001) in the semirecumbent position, and to 3.97 ± 1.42 L/h or a 21% increase (P =.026) in the sitting position. Conclusions: By achieving more appropriately timed compression cycles over time, the new SCD Response System is effective in preventing venous stasis by means of a new method that improves on the clinically documented effectiveness of the existing SCD system. Further studies testing its potential for improved efficacy in preventing deep venous thrombosis are justified. (J Vasc Surg 2000;32:932-40.

Screening for chronic cerebrospinal venous insufficiency (CCSVI) using ultrasound. Recommendations for a protocol

Chronic cerebrospinal venous insufficiency (CCSVI) is a syndrome characterized by stenoses or obstructions of the internal jugular and/or azygos veins with disturbed flow and formation of collateral venous channels. Ultrasound and venographic studies of the internal jugular and azygos venous systems in patients with multiple sclerosis (MS) have demonstrated a high prevalence of CCSVI (mean 71%, range 0-100%; n=1336)associated with activation of collaterals. By contrast, ultrasound and venographic examinations of normal controls and patients without MS have demonstrated a much lower prevalence (mean 7.1%, range 0-22%; n=505). Ultrasound in the form of duplex scanning uses a combination of physiological measurements as well as anatomical imaging and has been used for the detection of CCSVI by different centers with variable results. A high prevalence of obstructive lesions, ranging from 62% to 100%, has been found by some teams in patients with MS compared with a low prevalence (0-25%) in controls. However, others have reported absence of these lesions or a lower prevalence (16-52%). This variability could be the result of differences in technique, training, experience or criteria used. In order to ensure a high reproducibility of duplex scanning with comparable accuracy between centers a detailed protocol with standard methodology and criteria is needed. Also, standardization of the method of reporting of duplex measurements and other findings will facilitate validation of the proposed criteria by different centers. The aim of this document is to produce recommendations for such a protocol and indicate what future research is needed in order to address areas of uncertainty

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.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P&lt;0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P&lt;0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment