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  • Ovid Technologies (Wolters Kluwer Health)  (9)
  • 1
    Online-Ressource
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    Abstract 21015: Coronary Inflammation in Humans Drives Spatial Changes of Perivascular Adipose Tissue Composition Detectable by a Novel Computed Tomography-Based Technology
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Circulation Vol. 136, No. suppl_1 ( 2017-11-14)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 136, No. suppl_1 ( 2017-11-14)
    Kurzfassung: Background: Non-invasive detection of vascular inflammation remains an unmet goal. We hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation can be quantified using a new computed tomography angiography (CTA) methodology. Methods: In Arm 1, human PVAT adipocytes were cultured +/- inflammatory cytokines (n=7) or co-cultured with vascular tissue (+/-Angiotensin-II, n=6) to assess the effects of vascular inflammation on PVAT differentiation. In Arm 2, AT explants (epicardial, subcutaneous and thoracic AT) from 453 cardiac surgery patients were used in histology and gene expression studies to relate the ex vivo images with in vivo CT scan information (n=105) on the biology of the explants. In Arm 3, in 267 patients undergoing diagnostic CTA, PVAT attenuation (Fat Attenuation Index (FAI) defined as the average CT attenuation of AT), was analysed around the proximal right coronary artery. In Arm 4, PVAT FAI around unstable (culprit) and stable coronary plaques was calculated in 22 CAD patients undergoing CTA. Results: In Arm 1, Angiotensin-II (A) and proinflammatory cytokines (B) prevented lipid accumulation and adipocyte differentiation in cultured PVAT. In Arm 2, adipocyte size by histology was inversely correlated with FAI in vivo (C). Both FAI of AT explants (not shown) and FAI in-vivo (n=105) were negatively associated with epicardial AT differentiation as assessed by FABP4 expression (D). In Arm 3, PVAT FAI change over distance from RCA wall was significantly different in CAD compared to no CAD patients (E). In Arm 4, PVAT FAI was significantly increased around unstable plaques (F). Conclusions: Human vessels exert paracrine effects on surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can then be monitored using a CT imaging approach. This novel methodology can be implemented in clinical practice to detect unstable plaques in the human coronary vasculature.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.136.suppl_1.21015
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2017
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    Online-Ressource
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    Cardiac Computed Tomography : Assessment of Coronary Inflammation and Other Plaque Features
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Arteriosclerosis, Thrombosis, and Vascular Biology Vol. 39, No. 11 ( 2019-11), p. 2207-2219
    Details
    In: Arteriosclerosis, Thrombosis, and Vascular Biology, Ovid Technologies (Wolters Kluwer Health), Vol. 39, No. 11 ( 2019-11), p. 2207-2219
    Kurzfassung: Unstable coronary plaques that are prone to erosion and rupture are the major cause of acute coronary syndromes. Our expanding understanding of the biological mechanisms of coronary atherosclerosis and rapid technological advances in the field of medical imaging has established cardiac computed tomography as a first-line diagnostic test in the assessment of suspected coronary artery disease, and as a powerful method of detecting the vulnerable plaque and patient. Cardiac computed tomography can provide a noninvasive, yet comprehensive, qualitative and quantitative assessment of coronary plaque burden, detect distinct high-risk morphological plaque features, assess the hemodynamic significance of coronary lesions and quantify the coronary inflammatory burden by tracking the effects of arterial inflammation on the composition of the adjacent perivascular fat. Furthermore, advances in machine learning, computational fluid dynamic modeling, and the development of targeted contrast agents continue to expand the capabilities of cardiac computed tomography imaging. In our Review, we discuss the current role of cardiac computed tomography in the assessment of coronary atherosclerosis, highlighting its dual function as a clinical and research tool that provides a wealth of structural and functional information, with far-reaching diagnostic and prognostic implications.
    Materialart: Online-Ressource
    ISSN: 1079-5642 , 1524-4636
    URL: Article
    DOI: 10.1161/ATVBAHA.119.312899
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2019
    ZDB Id: 1494427-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
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    Abstract 60: Impact of a Multisite, Protocol-Driven, Nurse Practitioner-Led, Cardiovascular Prevention Program in a Hispanic Medicare Advantage Population at High Risk for ASCVD: Healthy Heart Program at Cano Health.
    Ovid Technologies (Wolters Kluwer Health) ; 2023
    In:  Circulation Vol. 147, No. Suppl_1 ( 2023-02-28)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 147, No. Suppl_1 ( 2023-02-28)
    Kurzfassung: Introduction: Despite availability of effective and inexpensive pharmacologic therapies for hypercholesterolemia and hypertension, many patients at high risk for atherosclerotic cardiovascular disease (ASCVD) do not achieve optimal low-density lipoprotein (LDL) and systolic blood pressure (SBP) levels. We hypothesized that risk factor control could be improved by using nurse practitioners and a guideline-directed protocol in a Medicare Advantage (MA) population. Methods: We designed and implemented an ongoing 18 site, multistate (FL, TX, NV), ASCVD risk assessment and management program (Healthy Heart) in a large national MA primary care clinic (Cano Health). The cardiometabolic risk assessment and management program was designed by a team of preventive cardiologists, with the plan of being Nurse Practitioner (NP)-led, with remote support by a cardiologist. Protocols provided details on initiation and titration of drug therapy to achieve LDL-C and SBP goals. Patients with organ transplants, advanced cancer, an ejection fraction 〈 35%, and on hemodialysis were excluded. Results: From October 2021-October 2022, 5430 patients were enrolled in the program. A total of 1858 (34.2%) had established ASCVD, 1033 (19.0%) had diabetes mellitus (DM). A total of 713 (13.1%) had both ASCVD and DM. In patients who had ASCVD and diabetes together, high intensity statin use increased from 39.4% to 68.3% after enrollment; 52.66% achieved an LDL-C 〈 70 mg/dl after enrollment compared to 31.0% at baseline. Antihypertensive medications were intensified in 408/1041 (39.2%) of ASCVD and 276/558 (49.5%) of DM patients, with a higher proportion achieving a SBP 〈 130 mm Hg after enrollment. Conclusions: Implementing a novel cardiovascular prevention program in a population of mostly Hispanic MA patients at high risk for ASCVD, using NPs, with strict adherence to a step-by-step evidence-based protocol supervised by cardiologists, is associated with reduction in LDL levels and SBP and with improvement in reaching LDL and SBP targets.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.147.suppl_1.60
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2023
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
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    Stem Cell Therapy for Congenital Heart Disease : A Systematic Review
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Circulation Vol. 136, No. 24 ( 2017-12-12), p. 2373-2385
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 136, No. 24 ( 2017-12-12), p. 2373-2385
    Kurzfassung: Congenital heart disease (CHD) constitutes the most prevalent and heterogeneous group of congenital anomalies. Although surgery remains the gold standard treatment modality, stem cell therapy has been gaining ground as a complimentary or alternative treatment option in certain types of CHD. The aim of this study was to present the existing published evidence and ongoing research efforts on the implementation of stem cell-based therapeutic strategies in CHD. Methods: A systematic review was conducted by searching Medline, ClinicalTrials.gov, and the Cochrane library, along with reference lists of the included studies through April 23, 2017. Results: Nineteen studies were included in this review (8 preclinical, 6 clinical, and 5 ongoing trials). Various routes of cardiac stem cell delivery have been reported, including intracoronary, intramyocardial, intravenous, and epicardial. Depending on their origin and level of differentiation at which they are harvested, stem cells may exhibit different properties. Preclinical studies have mostly focused on modeling right ventricle dysfunction or failure and pulmonary artery hypertension by using pressure or volume overload in vitro or in vivo. Only a limited number of clinical trials on patients with CHD exist, and these primarily focus on hypoplastic left heart syndrome. Cell-based tissue engineering has recently been introduced, and research currently is focusing on developing cell-seeded grafts and patches that could potentially grow in parallel with whole body growth once implanted in the heart. Conclusions: It seems that stem cell delivery to the diseased heart as an adjunct to surgical palliation may provide some benefits over surgery alone in terms of cardiac function, somatic growth, and quality of life. Despite encouraging preliminary results, stem cell therapies for patients with CHD should only be considered in the setting of well-designed clinical trials. More wet laboratory research experience is needed, and translation of promising findings to large clinical studies is warranted to clearly define the efficacy and safety profile of this alternative and potentially groundbreaking therapeutic approach.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/CIRCULATIONAHA.117.029607
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2017
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Abstract 13475: Epicardial Adiposity Measured by a Deep Learning Network, Predicts Mortality and Cardiovascular Events in Patients Undergoing Cardiac CT
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Circulation Vol. 144, No. Suppl_1 ( 2021-11-16)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 144, No. Suppl_1 ( 2021-11-16)
    Kurzfassung: Introduction: Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac. The automated quantification of EAT volume is possible from routine CCTA scans via deep-learning. The use of automated EAT quantification for the assessment of cardiovascular disease (CVD) risk in addition to standard measures of obesity like BMI has not been fully explored. Purpose: To use deep-learning for automated segmentation of EAT from routine CCTA scans to assess the long-term CVD risk conveyed by EAT. Methods: A deep-learning automated EAT segmentation tool using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created and trained on over 2500 consecutive CCTAs from within the Oxford Risk Factors And Non Invasive Imaging (ORFAN) Study. External validation in 817 patients demonstrated excellent correlation between machine and human expert (CCC = 0.972). The prognostic value of deep-learning derived EAT volume was assessed against 5 years outcomes from the SCOTHEART trial (n=1588), with adjustment for CVD risk factors. An optimal cutoff was selected by identifying the EAT value that maximized the Youden’s J index (sum of sensitivity and specificity) for the three outcomes of interest - high risk was deemed to be EAT ≥ 170.5cm 3 . Results: There were 35 deaths (all-cause mortality), 35 non-fatal myocardial infarctions and 8 non-fatal strokes during the 5 years follow up period. By using multi-variable cox-regression, EAT volume was predictive of all-cause mortality (Figure 1A), non-fatal MI (Figure 1B), and non-fatal stroke (Figure 1C) independently from CVD risk factors. Conclusions: Automatically segmented EAT volume measured using a deep learning network, predicts 5-year all-cause mortality, heart attacks and stroke independently of BMI and clinical risk profile of the patients. This suggests that measures of visceral obesity will be of value in the interpretation of cardiovascular computed tomography.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.144.suppl_1.13475
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2021
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
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    Abstract 11487: Individualizing the Cardiovascular Benefits of Intensive Blood Pressure Reduction Using Computational Phenomaps of the SPRINT and ACCORD Trials
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Circulation Vol. 144, No. Suppl_1 ( 2021-11-16)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 144, No. Suppl_1 ( 2021-11-16)
    Kurzfassung: Introduction: Intensive systolic blood pressure (SBP) control reduces major adverse cardiovascular events (MACE) in patients without type 2 diabetes mellitus (T2DM). However, these benefits are less clear in T2DM. We evaluated an application of machine learning to clinical trials of patients without and with T2DM in assessing a personalized cardiovascular benefit of intensive SBP control. Methods: In SPRINT, a trial of intensive (n=4678, SBP 〈 120 mmHg) versus standard (n=4683, SBP 〈 140 mmHg) SBP control in 9361 patients without T2DM, we created a topological representation of the trial patients using 59 baseline variables (trial phenomaps). Within each patient’s 5% topological neighborhood, we calculated hazard ratios (HR) for recurrent MACE (cardiovascular death, acute coronary syndrome, stroke, acute decompensated heart failure). We trained an extreme gradient boosting algorithm to predict the personalized effects of intensive SBP control using features linked to topological benefit. We then tested this machine learning tool in the ACCORD BP trial of patients with T2DM (n=2362 & 2371 in the intensive and standard arms respectively). Results: In SPRINT (age 68±9 years, 36% women) there were a total of 1046 recurrent MACE endpoints. The median individual patient HR was 0.58 [IQR, 0.38-0.82] ( A ). We developed a 10-variable tool in SPRINT ( B ) and subsequently tested in ACCORD BP (age 63±7 years, 48% women) where it identified individual patients with a higher benefit with intensive vs standard SBP control (adj. HR for time-to-MACE 0.77 [95% CI 0.61-0.98] in individuals with above median predicted benefit [high responders] ( C ) vs 0.97 [95% CI 0.77-1.23] ( D ) for below median predicted benefit [low responders]). Conclusions: We present a clinical trial-based, machine learning tool that identifies an individual’s personalized benefit from intensive versus standard SBP goals in patients with and without T2DM and may be used to guide clinical decision-making.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.144.suppl_1.11487
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2021
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
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    Abstract 13659: Automated Detection of Aortic Stenosis From Single-View 2-Dimensional Echocardiography Using a Semi-Supervised, Contrastive Learning Approach
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation Vol. 146, No. Suppl_1 ( 2022-11-08)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
    Kurzfassung: Introduction: Early diagnosis of aortic stenosis (AS) is critical for its timely management. However, despite emergence of hand-held echocardiography, the detection and grading of AS requires Doppler imaging, which is limited by both access and expertise. We developed a semi-supervised, contrastive learning approach to identify severe AS using limited labelled data of parasternal long axis (PLAX) videos from transthoracic echocardiography (TTE). Methods: We sampled TTE studies performed between 2015-2021 in a large health system. TTEs from 2015-2020 were used for training, with oversampling of AS for diagnostic enrichment (5311 studies, age 70±16 years, n=2601 [49%] women, 5029 unique patients). The testing set represented studies in 2021 without oversampling for AS (2040 studies, mean age 66±16 years, n=997 [49%] women, n=2034 unique patients). We performed self-supervised pretraining by selecting different PLAX videos from the same patient as positive samples for contrastive learning (multi-instance self-supervised learning) ( A ). The learned weights were used to initialize a 3D convolutional neural network to predict severe AS ( B ). Results: An ensemble model of three different weight initialization methods achieved an AUC of 0.97 (95% CI: 0.96-0.99) for severe AS detection, with 0.96 (95% CI: 0.83-0.97) specificity at 90% sensitivity. Among patients without severe AS, positive predictions were characterized by significantly higher peak aortic velocities compared to negative predictions, with no differences in LV function - a negative control ( C ). Saliency maps highlighted the aortic valve as most relevant to the final predictions ( D, i-v: positive; vi: negative predictions). Conclusions: We have developed a novel method to detect severe AS using single-view TTE videos without requiring Doppler data. Our findings have significant implications for point-of-care ultrasound screening as part of routine clinic visits and in low-resource settings.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.146.suppl_1.13659
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2022
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
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    Association Between Impaired Myocardial Flow Reserve on 82 Rubidium Positron Emission Tomography Imaging and Adverse Events in Patients With Autoimmune Rheumatic Disease
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Circulation: Cardiovascular Imaging Vol. 14, No. 9 ( 2021-09)
    Details
    In: Circulation: Cardiovascular Imaging, Ovid Technologies (Wolters Kluwer Health), Vol. 14, No. 9 ( 2021-09)
    Kurzfassung: Coronary microvascular dysfunction has been described in patients with autoimmune rheumatic disease (ARD). However, it is unknown whether positron emission tomography (PET)-derived myocardial flow reserve (MFR) can predict adverse events in this population. Methods: Patients with ARD without coronary artery disease who underwent dynamic rest-stress 82 Rubidium PET were retrospectively studied and compared with patients without ARD matched for age, sex, and comorbidities. The association between MFR and a composite end point of mortality or myocardial infarction or heart failure admission was evaluated with time to event and Cox-regression analyses. Results: In 101 patients with ARD (88% female, age: 62±10 years), when compared with matched patients without ARD (n=101), global MFR was significantly reduced (median: 1.68 [interquartile range: 1.34–2.05] versus 1.86 [interquartile range: 1.58–2.28]) and reduced MFR ( 〈 1.5) was more frequent (40% versus 22%). MFR did not differ among subtypes of ARDs. In survival analysis, patients with ARD and low MFR (MFR 〈 1.5) had decreased event-free survival for the combined end point, when compared with patients with and without ARD and normal MFR (MFR 〉 1.5) and when compared with patients without ARD and low MFR, after adjustment for the nonlaboratory-based Framingham risk score, rest left ventricular ejection fraction, severe coronary calcification, and the presence of medium/large perfusion defects. In Cox-regression analysis, ARD diagnosis and reduced MFR were both independent predictors of adverse events along with congestive heart failure diagnosis and presence of medium/large stress perfusion defects on PET. Further analysis with inclusion of an interaction term between ARD and impaired MFR revealed no significant interaction effects between ARD and impaired MFR. Conclusions: In our retrospective cohort analysis, patients with ARD had significantly reduced PET MFR compared with age-, sex-, and comorbidity-matched patients without ARD. Reduced PET MFR and ARD diagnosis were both independent predictors of adverse outcomes.
    Materialart: Online-Ressource
    ISSN: 1941-9651 , 1942-0080
    URL: Article
    DOI: 10.1161/CIRCIMAGING.120.012208
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2021
    ZDB Id: 2440475-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
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    Abstract 13661: An Adaptive Strategy of Predictive Enrichment to Increase Efficiency of Randomized Clinical Trials Using Machine Learning: A Simulation of the IRIS Trial
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation Vol. 146, No. Suppl_1 ( 2022-11-08)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
    Kurzfassung: Introduction: Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but often carry high financial costs and lengthy time frames. Hypothesis: We hypothesized that a machine learning (ML) strategy of predictive enrichment can reduce the size of RCTs through adaptive enrollment based on projected response profiles. Methods: We conducted a post-hoc analysis of individual patient data from the Insulin Resistance Intervention after Stroke (IRIS) trial using dynamic computational trial phenomapping. After a study period of 36 months, we performed serial annual interim analyses ( A ). For this, we designed an algorithm that used a phenotypic trial representation using 59 baseline variables to predict personalized effects of pioglitazone on fatal/nonfatal stroke or myocardial infarction (study outcome). Compared with a complete trial enrollment (100%), in an adaptive fashion, we a priori restricted enrollment to two thirds (66.7%) of eligible participants between each interim analysis based on the projected treatment benefit. Since this was defined by their baseline phenotypic profile, treatment randomization was preserved. We employed these patient subsets as comparators and assessed the effect of pioglitazone on the study outcome through Cox regression models. Results: Compared to the unrestricted enrollment of participants ( blue line ), an ML-guided adaptive strategy of predictive enrichment ( green line ) was associated with defining the same treatment effect size with a lower trial size - 3876 vs 2946 participants ( B-C ). Both approaches appeared to meet statistical significance at 6 years after the trial onset ( D-E ). In contrast, a strategy of random patient selection ( orange line ) did not show significant benefit at the same population size. Conclusions: We developed an adaptive strategy for predictive enrichment to increase efficiency of RCTs using machine learning.
    Materialart: Online-Ressource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.146.suppl_1.13661
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2022
    ZDB Id: 1466401-X
    Standort Signatur Einschränkungen Verfügbarkeit
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