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Introduction:Venous leg ulcers (VLU) embody the most severe stage of the broad spectrum of chronic venous disease. Approximately 40% of patients with VLU present with the underlying deep venous disease (DVD). Although the data are scarce, these deep venous disease-related VLU (DRV) are thought to have higher recurrence rates and a substantial economic burden. The objective of this study was to assess the economic burden of DRV across Australia, France, Germany, Italy, Spain, the UK, and the USA.Methods:A comprehensive literature review was undertaken to identify publications documenting the incidence and prevalence of VLU and DRV, medical resource utilization, and associated costs of DRV. Findings from this literature review were used to estimate the economic burden of illness, including direct medical costs over a 12-month interval following initial presentation of a newly formed DRV.Results:Total annual incidence of new or recurrent DRV in Australia, France, Germany, Italy, Spain, UK, and the US are estimated at 122,000, 263,000, 345,000, 253,000, 85,000, 230,000, and 643,000 events, respectively, in 2019. Incidence ranges from 0.73 to 3.12 per 1000 persons per year. The estimated annual direct medical costs for patients managed conservatively in these geographies total ~ $10.73 billion (USD) or $5527 per person per year.Conclusion:The availability of published data on the costs of VLU care varies widely across countries considered in this analysis. Although country-specific VLU practice patterns vary, there is a uniform pattern of high-cost care.

Describe the incidence and predictors of acute limb ischemia in patients with acute myocardial infarction complicated by cardiogenic shock.Patients with index hospitalizations for acute myocardial infarction complicated by cardiogenic shock from 2016 to 2019 in the U.S. National Readmission Database were identified. We evaluated the incidence of acute limn ischemia and its associated mortality, length of stay, and cost of hospitalization. We used multivariable logistic regression to determine independent predictors of acute limb ischemia in this population.A total of 84,615 patients had acute myocardial infarction complicated by cardiogenic shock and 1302 (1.54%) developed acute limb ischemia. The rates of acute limb ischemia increased from 1.29% in 2016 to 1.66% in 2019 (p=0.002). Utilization of microaxial mechanical circulatory support increased from 2.25% in 2016 to 13.36% in 2019 (p=0.0001). The major predictors of acute limb ischemia included peripheral arterial disease (OR, 7.34, 95% CI 6.12 to 8.81), veno-arterial extracorporeal membrane oxygenation (OR, 4.40, 95% CI of 3.19 to 6.07), and microaxial mechanical circulatory support (OR, 3.12, 95% CI of 2.74 to 3.55). Acute limb ischemia in patients with cardiogenic shock was associated higher mortality (39.20% vs 33.53%, p=0.0001).This nationwide observational study shows that acute limb ischemia is an important complication of acute myocardial infarction with cardiogenic shock. This complication is associated with higher mortality. In addition to peripheral artery disease the use of mechanical circulatory devices was associated with significantly higher rates of acute limb ischemia.

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification system into binary classes, namely symptomatic and asymptomatic types via the deep learning (DL) framework implemented on a supercomputer. We hypothesize that on ultrasound images, symptomatic carotid plaques have (a) a low grayscale median because of a histologically large lipid core and relatively little collagen and calcium, and (b) a higher chaotic (heterogeneous) grayscale distribution due to the composition. The methodology consisted of building a DL model of Artificial Intelligence (called Atheromatic 2.0, AtheroPoint, CA, USA) that used a classic convolution neural network consisting of 13 layers and implemented on a supercomputer. The DL model used a cross-validation protocol for estimating the classification accuracy (ACC) and area-under-the-curve (AUC). A sample of 346 carotid ultrasound-based delineated plaques were used (196 symptomatic and 150 asymptomatic, mean age 69.9 ± 7.8 years, with 39% females). This was augmented using geometric transformation yielding 2312 plaques (1191 symptomatic and 1120 asymptomatic plaques). K10 (90% training and 10% testing) cross-validation DL protocol was implemented and showed an (i) accuracy and (ii) AUC without and with augmentation of 86.17%, 0.86 (p-value

Background: Iliofemoral venous obstruction is recognized with increasing frequency as the underlying cause of lower extremity symptoms including edema, pain, skin changes, and, in advanced cases, ulceration. This study sought to evaluate the safety and effectiveness of the Abre venous self-expanding stent system for the treatment of symptomatic iliofemoral venous outflow obstruction. Methods: The ABRE Study (A Multi-Center, Non-Randomized Study to Evaluate the Safety and Effectiveness of the Abre Venous Self-Expanding Stent System in Patients With Symptomatic Iliofemoral Venous Outflow Obstruction) is a single-arm, multicenter, prospective study that included 200 subjects from 24 global sites. The primary end points were 12-month primary patency and major adverse events within 30 days. Secondary end points included lesion and procedure success, primary-assisted and secondary patency, major adverse events, stent migration, stent fracture, and quality of life changes. End point-related adverse events and imaging studies were adjudicated by independent clinical events committee and core laboratories, respectively. Results: Venous obstruction cause was classified as acute deep vein thrombosis (16.5%, 33/200), post-thrombotic syndrome (47.5%, 95/200), or nonthrombotic iliac vein lesion (36.0%, 72/200). The common iliac and external iliac veins were stented in 96.0% (192/200), 80.5% (161/200) of subjects, respectively. Stent implant into the common femoral vein was required in 44.0% (88/200). Primary patency at 12 months was 88.0% (162/184). Four (2.0%) major adverse events occurred within 30 days. Twelve-month primary-assisted and secondary patency were 91.8% (169/184) and 92.9% (171/184), respectively. No stent fractures or migrations were reported. Mean target limb Villalta score decreased from 11.2±5.6 at baseline to 4.1±4.8 at 12 months, and the mean target limb revised Venous Clinical Severity Score decreased from 8.8±4.7 at baseline to 4.3±3.6 at 12 months. Clinically meaningful improvements in quality of life and venous functional assessment scores from baseline were demonstrated through 12 months in all measures. Conclusions: Symptomatic iliofemoral venous obstruction can be successfully treated with an Abre venous stent. Study outcomes demonstrated a high patency rate with a good safety profile. Patients demonstrated a significant reduction in clinical symptoms and improvement in quality of life that was maintained through 12-month follow-up. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03038438.

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.

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.

The aim of the present study was to report a large, single-center experience using the ClotTriever thrombectomy system (Inari Medical, Irvine, CA) for the management of acute iliofemoral (IF) deep vein thrombosis (DVT). One limitation of all endovascular devices for the treatment of acute IF-DVT has been the inability to completely remove all acute thrombus and the need for adjunctive thrombolysis with its attendant risk of bleeding complications.A single-center retrospective review of consecutive patients with acute IF-DVT treated with the ClotTriever thrombectomy system (Inari Medical) is reported. Procedural efficacy was evaluated by an independent core imaging laboratory (Syntactx, New York, NY). Both procedural and in-hospital safety were assessed during the index hospitalization. The treated vein patency was assessed using duplex ultrasound at 30 days after the procedure.A total of 96 patients were included in the present retrospective review, 40 of whom (40%) had contraindications to thrombolytic therapy. In terms of efficacy, 93 patients (97%) had ≥75% thrombus removal. During the index hospitalization, two patients (2%) had experienced a symptomatic pulmonary embolus. However, no mortality, major bleeding, or device-related complications had occurred in the study population. Of the 96 patients, 64 had undergone duplex ultrasound at 30 days after the procedure. Of the 64 patients, 62 had normal flow (97%), 53 (83%) had normal compressibility, and 11 (17%) had partial compressibility.The ClotTriever thrombectomy catheter was both safe and effective in our cohort of patients with acute IF-DVT outside a randomized clinical trial.

Both catheter-directed thrombolysis (CDT) and ultrasound-assisted thrombolysis (USAT) are novel treatment modalities for patients presenting with acute pulmonary embolism (PE). The objective of this study was to compare clinical and quality-of-life (QOL) outcomes for patients undergoing either treatment modality. We retrospectively studied 70 consecutive patients treated with either CDT or USAT over 3 years at a multicenter health system. The primary clinical efficacy endpoint was right ventricular systolic pressure (RVSP) reduction post-procedurally. Safety endpoints were mortality and bleeding incidents based on Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Arteries (GUSTO) criteria. Long-term QOL was assessed using the 36-Item Short-Form Health Survey (SF-36) via phone interview. Thirty-seven patients (53%) in our study underwent USAT and 33 (47%) patients were treated with conventional CDT. Among all patients studied, 96% had echocardiographic evidence for right ventricular strain on admission. Mean RVSP decreased by 18 ± 13 mmHg in the USAT group post-procedurally as compared to 14 ± 16 mmHg in the CDT group, without significant difference between groups ( p = 0.31). Rates of moderate and severe bleeding were largely identical between USAT and CDT groups (USAT: 3%; CDT: 0%; p = 0.09). There was no death in either group during admission. At long-term follow-up, there was no significant difference in QOL between both treatment modalities in all eight functional domains of SF-36. Our retrospective study demonstrated using USAT over conventional CDT for acute submassive or massive PE did not yield additional clinical, safety, or long-term QOL benefit.

Objective To evaluate the 36-month efficacy and safety of cyanoacrylate closure for the treatment of incompetent great saphenous veins in comparison with radiofrequency ablation. Methods In this multicenter, prospective, randomized controlled trial, 222 symptomatic subjects with incompetent great saphenous veins were assigned to either cyanoacrylate closure or radiofrequency ablation. The primary endpoint, complete closure of the target great saphenous vein, was determined using duplex ultrasound examination starting from three-month visit. Results At month 36, the great saphenous vein closure rates were 94.4% for the cyanoacrylate closure group and 91.9% for the radiofrequency ablation group. Stable improvement in symptoms and quality of life was observed in both groups. Adverse event rates between the 24- and 36-month visits were similar between the groups as were serious adverse events which were infrequent and judged unrelated to either the device or the procedure in both groups. Conclusions This trial continues to demonstrate the safety and efficacy of cyanoacrylate closure for the treatment of great saphenous vein incompetence with great saphenous vein closure rate at 36 months similar to that of radiofrequency ablation, indicating non-inferiority of cyanoacrylate closure to radiofrequency ablation. The improvement in quality of life outcomes were also sustained and similar between the two treatment groups.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification system into binary classes, namely symptomatic and asymptomatic types via the deep learning (DL) framework implemented on a supercomputer. We hypothesize that on ultrasound images, symptomatic carotid plaques have (a) a low grayscale median because of a histologically large lipid core and relatively little collagen and calcium, and (b) a higher chaotic (heterogeneous) grayscale distribution due to the composition. The methodology consisted of building a DL model of Artificial Intelligence (called Atheromatic 2.0, AtheroPoint, CA, USA) that used a classic convolution neural network consisting of 13 layers and implemented on a supercomputer. The DL model used a cross-validation protocol for estimating the classification accuracy (ACC) and area-under-the-curve (AUC). A sample of 346 carotid ultrasound-based delineated plaques were used (196 symptomatic and 150 asymptomatic, mean age 69.9 ± 7.8 years, with 39% females). This was augmented using geometric transformation yielding 2312 plaques (1191 symptomatic and 1120 asymptomatic plaques). K10 (90% training and 10% testing) cross-validation DL protocol was implemented and showed an (i) accuracy and (ii) AUC without and with augmentation of 86.17%, 0.86 (p-value 0.0001), and 89.7%, 0.91 (p-value 0.0001), respectively. The DL characterization system consisted of validation of the two hypotheses: (a) mean feature strength (MFS) and (b) Mandelbrot's fractal dimension (FD) for measuring chaotic behavior. We demonstrated that both MFS and FD were higher in symptomatic plaques compared to asymptomatic plaques by 64.15 ± 0.73% (p-value 0.0001) and 6 ± 0.13% (p-value 0.0001), respectively. The benchmarking results show that DL with augmentation (ACC: 89.7%, AUC: 0.91 (p-value 0.0001)) is superior to previously published machine learning (ACC: 83.7%) by 6.0%. The Atheromatic runs the test patient in 2 s. Deep learning can be a useful tool for carotid ultrasound-based characterization and classification of symptomatic and asymptomatic plaques.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis., Highlights • This paper presents the four pathways that can lead to heart and brain injuries following COVID-19 infection. • This study gives several insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. • This study also discusses the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient.

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called “AtheroEdge Composite Risk Score 2.0” (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.

Background Endovenous revascularization is the standard in the management of acute thrombotic, chronic post-thrombotic iliocaval or iliofemoral obstruction, and nonthrombotic iliac vein lesions. The purpose of this study is to describe our single-center experience of postprocedure anticoagulation and antiplatelet regimens used after endovenous revascularization for a variety of venous occlusive conditions. Methods We conducted a retrospective analysis of 100 consecutive patients who underwent endovenous stenting for iliocaval or iliofemoral obstruction from January 1, 2014, to April 30, 2018. Patients treated with direct oral anticoagulants, warfarin, or low-molecular-weight heparin (LMWH) with or without antiplatelet therapy were identified. Demographic, procedural, patency, and follow-up data were collected. Stent patency was evaluated using duplex Doppler ultrasound examination or contrast venography. Results Seventy-one of 100 patients were treated with direct oral anticoagulant therapy (DOAC). Sixteen (23%) were lost to follow-up, leaving 55 (77%) available for analysis. The mean follow-up was 14 months (range, 1-43 months) with 32 patients (58%) followed for 12 months or longer. Primary, primary-assisted, and secondary-assisted patency rates were 87%, 97%, and 98%, respectively, at 12 months. In the non-DOAC group (patients treated with warfarin or LMWH), these rates were 87%, 93%, and 95%, respectively, at 12 months. Antiplatelet therapy, including clopidogrel, aspirin, or both, was used in 53 of 55 patients in the DOAC cohort and 18 of 19 patients in the non-DOAC group. Conclusions Our-single center retrospective analysis demonstrates acceptable primary patency rates when using DOAC therapy compared with those treated with warfarin or LMWH.

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.

Summary: Background: This study sought to compare effectiveness and safety of percutaneous mechanical thrombectomy (PMT) and thrombolysis alone (THR) in patients with acute or subacute iliofemoral deep vein thrombosis (IfDVT). Patients and methods: Observational and randomized trials, published between January 2001 to February 2019 were identified by searching MEDLINE. Studies on deep venous thrombosis (DVT) treated with either THR or PMT adjunctive to conventional anticoagulation and compressive intervention were included. Meta-analysis of proportions was conducted to assess effectiveness outcomes of successful lysis and primary patency, post-thrombotic syndrome (PTS), valvular reflux, recurrent DVT, as well as safety outcomes of major bleeding, hematuria, and pulmonary embolism. Results: Of 77 identified records, 17 studies including 1417 patients were eligible. Pooled proportion of successful lysis was similar between groups (THR: 95 % [I2 = 68.4 %], PMT 96 %, [I2 = 0 %]; Qbet [Cochran’s Q between groups] 0.3, p = 0.61). However, pooled proportion of 6-month primary patency was lower after THR than after PMT (68 % [I2 = 15.6 %] versus 94 %; Qbet 26.4, p 2 = 0 %] versus 1.0 % [I2 = 0 %]; Qbet 12.3, p 2 = 56 %] versus 91.3 % [I2 = 91.7 %]; Qbet 714, p bet 0.7, p = 0.39 and THR: 2 % versus PMT: 1 %; Qbet 1.1, p = 0.30, respectively). Conclusions: In patients with iliofemoral DVT, percutaneous mechanical thrombectomy was associated with a higher cumulative 6-month primary patency and a lower incidence of major bleeding compared to thrombolysis alone. Risk of hemolysis from mechanical thrombectomy needs further consideration.

Although acute intracranial hemorrhage (ICH) is a rare complication of catheter-directed thrombolysis (CDT), it remains a major concern associated with the use of CDT. The incidence and clinical predictors of developing ICH in the setting of CDT are not known.The National Inpatient Sample database was used to identify all patients with proximal lower extremity or caval deep vein thrombosis (DVT) from January 2005 to December 2013 in the United States. Multivariate logistic regression was performed to identify the clinical predictors of ICH between patients with DVT who had received anticoagulation therapy alone and those who had been treated with CDT plus anticoagulation therapy.Of 138,049 patients with proximal lower extremity or caval DVT, 7119 (5.2%) had received anticoagulation therapy and CDT. Of the patients treated with anticoagulation alone, ICH had occurred in 0.2% compared with 0.7% for those treated with CDT (P .01). The independent predictors of ICH in the CDT cohort were a history of stroke (odds ratio [OR], 19.4; 95% confidence interval [CI], 8.8-42.8; P .01), chronic kidney disease (OR, 2.2; 95% CI, 1.1-4.7; P = .03), age74 years (OR, 2.2; 95% CI, 1.2-4.3; P = .02), male sex (OR, 1.8; 95% CI, 1.01-3.3; P = .048). Of those patients treated with anticoagulation alone, the risk factors for the development of ICH were a history of stroke, hospital teaching status, and age74 years.The results from the present nationwide observational study showed that of patients with DVT treated with CDT, the independent predictors for developing ICH were a history of stroke, chronic kidney disease, male sex, and age 74 years.

Recent findings Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called “integrated predictive CVD risk models.” Purpose of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. Conclusion Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.

In-stent restenosis is complex, difficult to treat and has led to a ‘leave less metal behind’ approach to femoropopliteal intervention. Postangioplasty dissection often requires scaffolding to maintain patency. The Tack Endovascular System provides minimal-metal dissection repair that preserves future treatment options. Tack implants are designed to minimise the inflammation and neointimal hyperplasia that lead to in-stent restenosis. An independent angiographic core laboratory evaluated the restenosis patterns in clinically driven target lesion revascularisation (CD-TLR) during the 12 months following the index procedure in the Tack Optimized Balloon Angioplasty (TOBA) II study and compared these results to those published for nitinol stent implantation. Of the 213 patients in TOBA II, 31 (14.6%) required a CD-TLR. Of these, 28 had angiograms that were evaluated by the core laboratory, and 45.2%, 16.1%, and 29% were graded as Tosaka class I, II and III, respectively. There were no significant differences (p>0.05) in lesion length, degree of calcification or dissection class between the three groups. Tack restenotic lesion classification and analysis show a prevalence of both class I and shorter lesions relative to in-stent restenosis, which may be beneficial to long-term patient outcomes.

Background Chronic venous insufficiency (CVI) is a widely prevalent condition. Saphenous venous reflux is the most common underlying pathology that leads to CVI. Endovenous thermal ablations (ETA) are the current gold standard. However, some patients present with some unique challenges making ETA less ideal. Nonthermal nontumescent therapies could be considered as alternative therapy in these patients. Methods We performed a retrospective review of treatment effects with United States Food and Drug Administration-approved polidocanol endovenous microfoam (PEM; Varithena® 1%) on venous symptomology in 10 (n = 10) C3-C6 patients with CVI and concomitant complex disease/pathology that limited the use of ETA. The pathology included risk of bruising or bleeding, severe lipodermatosclerosis, hidradenitis suppurativa, chronic fibrosis of the vein from prior superficial thrombosis, risk for nerve injury, and failed prior ETA. Before treatment, all patients underwent a venous incompetence study by duplex ultrasound (DUS). Preprocedure pain, symptoms, and Venous Clinical Severity Scores (VCSSs) were recorded. Clinical assessments and DUS were performed 4 days, 6 weeks, 6 months, and 1 year after treatment. Results DUS demonstrated reflux in the great saphenous vein (GSV), anterior accessory GSV, and/or the small saphenous vein. Pain scores and VCSS were reduced 4 days and 6 weeks after treatment. These lower scores were maintained for up to 1 year after treatment. Conclusions PEM use in C3-C6 patients resulted in successful improvement in VCSS, CVI symptoms, and wound healing among patients in whom ETA was not considered to be the optimal therapy.

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non- invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Objective Although duplex ultrasound (DUS) imaging is the current gold standard in the diagnosis of femoropopliteal deep venous thrombosis, it is not an optimal diagnostic modality to diagnose iliocaval occlusion. Screening for iliocaval occlusion thus remains a challenge for clinicians because of the lack of a reliable noninvasive technique. This challenge results in most patients undergoing computed tomography venography or magnetic resonance venography or invasive venography and intravascular ultrasound imaging. This study reports a novel, yet simple, reproducible and intuitive, surface DUS finding of physiologic flow reversal within the superficial epigastric vein (SEV) as a sign of proximal iliocaval occlusion (ICO). Methods This was a retrospective study of 15 patients who were diagnosed with ICO based on the finding of SEV flow reversal on DUS imaging. Patient demographics, presenting CEAP C scores, ICO characteristics, correlation with advanced imaging, and short-term follow-up findings are reported. Results Physiologic reversal of the SEV resulted in confirmation of ICO in all patients who underwent advanced imaging, including computed tomography venography or traditional venogram along with intravascular ultrasound imaging. All patients who underwent follow-up DUS scans demonstrated normalization of the SEV flow after ICO recanalization. Conclusions ICO can result in deep venous thrombosis, post-thrombotic syndrome, and chronic venous insufficiency. Physiologic flow reversal in SEV is diagnostic of ICO. To the best of our knowledge, this is the first report of this novel DUS finding.

Catheter-directed thrombolysis (CDT) is being increasingly used for the treatment of proximal lower extremity (LE) deep venous thrombosis (DVT). However, sex differences in utilization and safety outcomes of CDT in these patients are unknown. The Nationwide Inpatient Sample (NIS) database was used to identify all patients with a principal discharge diagnosis of proximal LE or caval DVT who underwent CDT between January 2005 and December 2011 in the United States. We evaluated the comparative safety outcomes of CDT among a propensity-matched group of 1731 men versus 1731 women. Among 108,243 patients with proximal LE or caval DVT, 4826 patients (4.5%) underwent CDT. Overall, women underwent CDT less often compared to men (4.1% vs 4.9%, p

Introduction: While traditional ambulatory phlebectomy (AP) is a safe and effective procedure, few adverse effects such as hematoma, bleeding, bruising and nerve injury have been reported. To improve AP and to reduce some of these adverse events, herein we report a novel technique – Foam Sclerotherapy Assisted Phlebectomy (SAP). Report: The details of the above technique, along with the potential advantages and limitations will be discussed. Discussion: Foam sclerotherapy and DUS act as valuable adjuncts to traditional AP. SAP increases precision and creates additional venospasm leading to reduced bruising, reduced nerve injury and sclerosis of residual vein segments. Keywords: Varicose veins, Sclerotherapy, Foam sclerotherapy, Phlebectomy, Venous insufficiency

Background Chronic venous disease (CVD) is a common vascular disorder with manifestations ranging from asymptomatic spider veins to venous ulcers. Elevated right atrial pressure, otherwise called central venous pressure (CVP), can also result in edema and hyperpigmentation similar to chronic venous insufficiency. Obstructive sleep apnea (OSA) is a known risk factor for elevation of CVP. Prevalence rates of elevated CVP or OSA are unknown in patients presenting with a diagnosis of CVD. Methods This is a single-center, retrospective, descriptive study of patients referred to our tertiary care center with a diagnosis of CVD. Each patient was evaluated by simultaneous venous duplex ultrasound (to assess venous reflux) and limited echocardiography of the right side of the heart (to assess elevated CVP). We assessed the prevalence and predictors of elevated CVP in this cohort using multivariate logistic regression. Results A total of 264 patients with CVD were evaluated, and of these, 22.7% had elevated CVP and 26.9% had OSA. There was no significant difference in the prevalence of OSA or elevated body mass index in the group with elevated CVP compared with patients with normal CVP. The predictors of elevated CVP were age >64.6 years (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.003-1.05; P = .026), diabetes mellitus (OR, 2.19; 95% CI, 1.05-4.5; P = .035), and right lower extremity Venous Clinical Severity Score of ≥8.5 (OR, 1.098; 95% CI, 1.011-1.193; P = .026). Other predictors included prior history of pulmonary embolism and renal insufficiency. Conclusions Compared with the general population, the prevalence of elevated CVP and OSA is significant in this cohort of patients. Age, diabetes, and right lower extremity chronic venous insufficiency symptoms seem to be predictors of elevated CVP. Larger, population-based prevalence studies are needed to confirm these findings.