UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

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.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Performance of the Wingman catheter in peripheral artery chronic total occlusions: Short-term results from the international Wing-It trial.

OBJECTIVES: To determine the safety and effectiveness of a peripheral artery chronic total occlusion (CTO) crossing catheter following failed crossing attempts with standard guidewires. BACKGROUND: CTO crossing remains a challenge during peripheral artery interventions. METHODS: In this prospective, international, single-arm study, patients with a peripheral artery CTO that was uncrossable with standard guidewires were treated with a crossing catheter (Wingman, Reflow Medical). The primary efficacy endpoint of CTO crossing success was compared to a performance goal of 70.7%. The primary composite safety endpoint (major adverse event [MAE], clinically significant perforation or embolization, or grade C or greater dissection) was assessed over a 30-day follow-up period and compared to a performance goal of 13.0%. RESULTS: A total of 85 patients were treated using the Wingman catheter for peripheral artery CTO crossing. Key patient characteristics were mean age of 71±9 years, 66% male, and mean lesion length of 188±94 mm in the superficial femoral artery (71%), popliteal artery (15%), or infrapopliteal arteries (14%). Both primary endpoints of the trial were met¾CTO crossing success was 90% (lower confidence limit=82.5%) and 5 primary safety events occurred in 4 (4.8%) patients (upper confidence limit=10.7%). Over 30 days of follow-up, Rutherford score decreased by at least 2 categories in 74% patients; the percentage of patients with normal hemodynamics assessed with the ankle-brachial index increased from 1% to 51%. CONCLUSIONS: Among patients with a CTO that was unable to be crossed with a standard guidewire, the Wingman catheter was able to cross 90% of occlusions with a favorable safety profile.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Development and validation of a predictive score for anterograde crossing of infrapopliteal chronic total occlusions: (The Infrapop-CTO Score).

BACKGROUND: Patients with critical limb ischemia often have infrapopliteal (IP) chronic total occlusions (CTOs). While revascularization is indicated to prevent major amputation, anterograde crossing of these lesions can be challenging, with high failure rates. OBJECTIVE: To develop and validate a scoring system that can adequately predict successful anterograde crossing of infrapopliteal CTOs. METHODS AND RESULTS: A total of 213 IP CTOs (147 successfully crossed with the anterograde approach vs. 66 where anterograde crossing failed) were included in the analysis. Backwards stepwise selection (p for retention <.05) was used to create a multivariable logistic regression model for the prediction of successful anterograde crossing using variables that were found to have a p < .1 in univariate analysis. The model was internally validated with bootstrapping and demonstrated excellent discriminatory ability (C-statistic 0.78 and Hosmer-Lemeshow p value = .61). A point score based on the beta-coefficient of the model variables was created, with one point assigned for presence of a noncentral (blunt) stump, one point for severe calcification at the entry site, and two points each for non-restenotic lesions or lesion length > 200 mm. The score was shown to have an excellent discriminatory ability for successful crossing, with low scores (0-2) associated with low failure rates (10%) and high scores associated with high failure rates (79%). CONCLUSIONS: The Infrapop-CTO score can stratify the procedural complexity of IP CTOs based on the likelihood of successful anterograde crossing. This score may help to direct optimal approaches toward infrapopliteal revascularization of patients with critical limb ischemia.

Laser atherectomy and drug-coated balloons for the treatment of femoropopliteal in-stent restenosis: 2-Year outcomes.

BACKGROUND: Femoropopliteal (FP) artery is one of the most anatomically challenging areas for sustained stent patency. The incidence of FP in-stent restenosis (ISR) is estimated at 50% at 24 months. Prior studies have shown that lesion debulking with laser atherectomy (LA) combined with drug coated balloon (DCB) have superior outcomes compared to LA + balloon angioplasty (BA) ISR, but there have not been studies evaluating 2-year outcomes. METHODS: This was a dual-center retrospective cohort study that compared patients with FP-ISR treated with LA + DCB versus LA + BA. Cox regression analysis was used to examine 2-year outcomes of target lesion revascularization (TLR) and the composite outcome of TLR or restenosis. Multivariable analysis was performed for clinical and statistically significant (in the univariate analysis) variables. RESULTS: One hundred and seventeen consecutive patients with Tosaka II (n = 32) and III (n = 85) ISR were analyzed. Sixty-six patients were treated with LA + DCB and 51 with LA + BA. The LA + DCB group had more lesions with moderate to severe calcification (58% vs. 13%; p < .0001). The LA + DCB group was more likely to be treated with the use of embolic protection devices (64% vs. 23%, p < .001) and cutting balloons (61% vs. 6%, p < .001). Bail-out stenting rates were lower in the LA + DCB group (32% vs. 57%, p = .008). LA + DCB was superior (HR: 0.57; 95% CI: 0.34-0.9, p = .027) for the composite outcome of 2-year TLR or restenosis. The 12-month KM estimates for freedom from TLR or restenosis were 66% in the LA + DCB group versus 46% in the LA + BA group. The 24-month KM estimates were 45% in the LA + DCB group versus 24% in the LA + BA group. CONCLUSIONS: The combination of DCB + LA was associated with decreased rates of bail-out stenting and improved 2-year TLR or restenosis rates. Randomized clinical trials examining the DCB + LA combination for FP-ISR are needed.

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Total IN.PACT drug-coated balloon initiative reporting pooled imaging and propensity-matched cohorts.

OBJECTIVE: Randomized controlled trials have shown that drug-coated balloons (DCBs) provide superior results compared with percutaneous transluminal angioplasty (PTA) for the treatment of femoropopliteal artery disease. However, these trials have generally included short lesions, few occlusions, and small sample sizes. The present study was an individual-level pooled analysis of duplex ultrasonography (DUS) core laboratory-adjudicated and clinical events committee-adjudicated IN.PACT Admiral DCB subjects across two randomized controlled trials and two single-arm prospective studies to characterize the safety and effectiveness of DCB compared with PTA. METHODS: The subjects were treated with DCB (n = 926) or PTA (n = 143). The end points through 12 months included DUS core laboratory-adjudicated primary patency and clinically driven target lesion revascularization (CD-TLR) using Kaplan-Meier estimates and primary safety using proportions. A propensity-matched analysis of DCB (n = 466) to PTA (n = 136) was conducted to address confounders. RESULTS: At 12 months, DCB compared with PTA had significantly greater primary patency (88.8% vs 53.9%; P < .001), freedom from CD-TLR (94.3% vs 80.2%; P < .001), and better primary safety composite end point (94.1% vs 78.0%; P < .001). After propensity-matched analysis, DCB remained superior to PTA at 12 months for primary patency (90.5% vs 53.8%; P < .001), freedom from CD-TLR (96.9% vs 80.7%; P < .001), and the primary safety composite end point (96.3% vs 78.4%; P < .001). Across multiple prespecified subgroup analyses, including provisional stenting, DCB remained persistently superior to PTA. CONCLUSIONS: In the largest, DUS core laboratory-adjudicated, multiethnic, pooled DCB series to date, the IN.PACT Admiral DCB demonstrated significantly greater primary patency, freedom from CD-TLR, and better composite safety at 12 months compared with PTA.

Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.

PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. RECENT FINDING: In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue-specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning- and deep learning-based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning-based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography. RECENT FINDINGS: In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

Long-term outcomes of carotid artery stenting in patients with a contralateral carotid artery occlusion.

OBJECTIVES: To examine the association between a contralateral carotid artery occlusion (CCO) and the rates of subsequent target-lesion restenosis and revascularization after carotid artery stenting (CAS). BACKGROUND: Patients with carotid artery disease undergoing revascularization often have a CCO. The association of a CCO with long-term outcomes after CAS is uncertain. METHODS: At two institutions, 267 CAS procedures were performed from 2006 to 2016 including 47 (18%) with a CCO. Regular follow-up with duplex carotid ultrasound was performed to assess for restenosis. Univariate Cox regression analysis was performed to evaluate the association between the presence of a CCO and repeat revascularization. RESULTS: The mean patient age was 70 years. There was no significant difference (P > 0.05) in procedural indication (asymptomatic vs ischemic symptoms) or medical comorbidities between groups. During 5-year follow up, the rate of duplex-derived >80% stenosis was 6% in the non-CCO group and 9% in the CCO group (P = 0.45). Despite similar rates of >80% restenosis, there was a significant association between CCO and subsequent target-lesion revascularization (TLR), with rates of 6.4% vs 0.9% at 5 years (HR 7.2, confidence interval (CI) 1.2-43, P = 0.04). There were no significant differences between groups in the 5-year rates of stroke (4.3% in CCO group vs 4.5% in non-CCO group, HR 0.53, CI 0.07-4.22, P = 1.0) or MACCE (15% vs 18%, HR 0.55, CI 0.2-1.55, P = 0.68). CONCLUSIONS: Patients undergoing CAS with a CCO were more likely to undergo TLR during long-term follow up, but they did not have any differences in procedural success or short- and long-term outcomes.

Acute and Midterm Outcomes of Antegrade vs Retrograde Crossing Strategies for Endovascular Treatment of Iliac Artery Chronic Total Occlusions.

PURPOSE: To examine whether an antegrade or retrograde crossing strategy for treatment of iliac artery chronic total occlusions (CTOs) is associated with differences in procedural or midterm outcomes. MATERIALS AND METHODS: A dual-center retrospective cohort study was conducted in 168 patients (mean age 66.4±10.6 years; 116 men) treated for CTOs in 110 common iliac arteries (CIA), 52 external iliac arteries (EIA), and 26 combined CIA/EIAs. Logistic regression models were developed to determine the association between crossing strategy and procedural complications, 1- and 3-year target lesion revascularization (TLR), and major adverse limb events (MALE). Results are presented as the odds ratio (OR) and 95% confidence interval (CI). RESULTS: An initial antegrade strategy was more common for EIA CTOs (p<0.005), and an initial retrograde strategy was more often used in CIA (p<0.005) and combined CIA/EIA (p<0.005) CTOs. Crossover to an alternate approach was required in 27.6% of initial antegrade attempts and 9.6% of initial retrograde attempts. EIA CTOs were the most likely lesions to be treated successfully with the initial attempt (either strategy). In all, 123 (65.4%) lesions were successfully crossed with a final retrograde approach and 65 with a final antegrade approach. Overall target lesion success was high for both groups (95.1% vs 93.2%, p=0.456). Lesions treated with a final retrograde approach were shorter (75.3±34.9 vs 87.6±31.3 mm, p=0.005) and were more likely to be treated with a reentry device (34.2% vs 9.2%, p<0.001) and with balloon-expandable stents (39.2% vs 17.7%, p=0.005). The final antegrade approach was associated with a lower risk of target lesion complications (OR 0.07, 95% CI 0.01 to 0.81, p=0.034). The two crossing approaches were associated with similar estimates of 1- and 3-year TLR and MALE. CONCLUSION: A final antegrade approach was associated with lower rates for complications but the 2 approaches were similar in terms of lesion success, TLR, and MALE. The EIA CTOs were more likely to be treated with an antegrade approach and more likely to be crossed successfully with the initial approach irrespective of the crossing direction.

Balloon-Expandable Vascular Covered Stent in the Treatment of Iliac Artery Occlusive Disease: 9-Month Results from the BOLSTER Multicenter Study.

PURPOSE: This study sought to assess the performance of the LIFESTREAM balloon-expandable covered stent for the treatment of iliac artery atherosclerotic lesions. METHODS: A total of 155 patients were treated in a prospective, single-arm study at 17 centers in the United States, Europe, and New Zealand. The primary endpoint was a composite of device- or procedure-related death or myocardial infarction (MI) over the course of 30 days, or target lesion revascularization (TLR), major amputation of the target limb, or re-stenosis through 9-months. Secondary endpoints included primary patency, TLR, sustained clinical success, quality of life, and major adverse events (MAE). RESULTS: At 9 months, the primary composite endpoint rate was 16.2% (93.5% confidence interval [CI]: 10.6%-23.2%), primary patency was 89.1% (95% CI: 82.6%-93.7%), and freedom from TLR was 96%. There was a cumulative clinical improvement of at least one Rutherford category from baseline to 9 months of 90.5% (95% CI: 84.3%-94.9%). Quality of life, assessed by using the Walking Impairment Questionnaire (WIQ), demonstrated a mean change in total score from baseline through 9 months of 32.1 ± 26.84; overall, improvements were noted from baseline in each WIQ category. Seven of one-hundred fifty patients (4.7%; 95% CI: 1.9%-9.4%) experienced MAEs, but none were determined to be related to device or procedure. CONCLUSIONS: The LIFESTREAM balloon-expandable covered stent provided satisfactory 9-month clinical outcomes including a low rate of target lesion revascularization for the treatment of stenotic and occlusive lesions of the iliac arteries.

iCAST Balloon-Expandable Covered Stent for Iliac Artery Lesions: 3-Year Results from the iCARUS Multicenter Study.

PURPOSE: To evaluate safety and effectiveness of the iCAST Covered Stent for treatment of iliac artery atherosclerotic lesions. MATERIALS AND METHODS: The iCARUS trial (ClinicalTrials.gov Identifier: NCT00593385) was a single-arm, prospective, multicenter study that enrolled 152 per protocol subjects at 25 sites in the United States and Germany. Subjects with multiple lesions and/or stents were eligible. The primary endpoint was the composite rate of death within 30 days, target lesion revascularization (TLR) within 9 months, or restenosis at 9 months after procedure. Secondary endpoints included major adverse vascular events (MAVEs), primary patency, freedom from TLR, and clinical success. RESULTS: Device and acute procedural success were achieved in 98.7% and 92.7% of cases, respectively. MAVE rate was 4.6% at 30 days. The 9-month primary composite endpoint rate was 8.1% (10/123), which was below the performance goal of 16.57%. Nine-month primary patency, defined as continuous flow without revascularization, bypass, or target limb amputation, was 96.4%. Freedom from TLR at 9 months and 3 years was 97.2% and 86.6%, respectively. Early clinical success was seen in 88.7% of subjects at 30 days with sustained clinical benefit in 72.4% of subjects at 3 years. CONCLUSIONS: The iCARUS study demonstrated that the iCAST Covered Stent was safe and effective for treatment of atherosclerotic iliac artery lesions with sustained clinical benefit out to 3 years.

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study.

MOTIVATION: This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY: Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.

A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography.

PURPOSE OF REVIEW: Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques. RECENT FINDING: Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque tissue characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.