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  • Ovid Technologies (Wolters Kluwer Health)  (16)
  • 1
    Online Resource
    Online Resource
    Thoughts on the Role of the Healing Professions and the Events of September 11, 2001
    Ovid Technologies (Wolters Kluwer Health) ; 2002
    In:  Circulation Vol. 105, No. 12 ( 2002-03-26), p. 1509-1510
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 105, No. 12 ( 2002-03-26), p. 1509-1510
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/hc1202.105940
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2002
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  • 2
    Online Resource
    Online Resource
    Influenza Infection Exerts Prominent Inflammatory and Thrombotic Effects on the Atherosclerotic Plaques of Apolipoprotein E–Deficient Mice
    Ovid Technologies (Wolters Kluwer Health) ; 2003
    In:  Circulation Vol. 107, No. 5 ( 2003-02-11), p. 762-768
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 107, No. 5 ( 2003-02-11), p. 762-768
    Abstract: Background— The role of infection in the development and complications of atherosclerosis has been the focus of much attention. We reported previously that influenza vaccination was associated with reduced risk of recurrent myocardial infarction. Here, we report the effect of influenza A virus on the apolipoprotein E–deficient (apoE −/− ) mouse, an animal model of atherosclerosis. Methods and Results— Twenty-four apoE −/− mice 〉 24 months old were injected with 1 LD 50 (lethal dose 50) of influenza A virus. Ten wild-type C57BL/6 infected mice and 11 noninfected age-matched apoE −/− mice served as controls. Multiple aortic sections were studied histologically 3, 5, and 10 days later. The infected mice showed markedly increased intimal cellularity compared with the noninfected apoE −/− mice. No aortic abnormalities were seen in infected wild-type mice. Ten infected apoE −/− mice had a significant subendothelial infiltrate composed of a heterogeneous group of cells that stained positively for smooth muscle cell actin, F4/80 (macrophages), and CD3 (T lymphocytes). One case of subocclusive platelet and fibrin-rich thrombus was seen. Conclusions— This study shows that influenza infection promotes inflammation, smooth muscle cell proliferation, and fibrin deposition in atherosclerotic plaques.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/01.CIR.0000048190.68071.2B
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2003
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  • 3
    Online Resource
    Online Resource
    From Vulnerable Plaque to Vulnerable Patient : A Call for New Definitions and Risk Assessment Strategies: Part I
    Ovid Technologies (Wolters Kluwer Health) ; 2003
    In:  Circulation Vol. 108, No. 14 ( 2003-10-07), p. 1664-1672
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 108, No. 14 ( 2003-10-07), p. 1664-1672
    Abstract: Atherosclerotic cardiovascular disease results in 〉 19 million deaths annually, and coronary heart disease accounts for the majority of this toll. Despite major advances in treatment of coronary heart disease patients, a large number of victims of the disease who are apparently healthy die suddenly without prior symptoms. Available screening and diagnostic methods are insufficient to identify the victims before the event occurs. The recognition of the role of the vulnerable plaque has opened new avenues of opportunity in the field of cardiovascular medicine. This consensus document concludes the following. (1) Rupture-prone plaques are not the only vulnerable plaques. All types of atherosclerotic plaques with high likelihood of thrombotic complications and rapid progression should be considered as vulnerable plaques. We propose a classification for clinical as well as pathological evaluation of vulnerable plaques. (2) Vulnerable plaques are not the only culprit factors for the development of acute coronary syndromes, myocardial infarction, and sudden cardiac death. Vulnerable blood (prone to thrombosis) and vulnerable myocardium (prone to fatal arrhythmia) play an important role in the outcome. Therefore, the term “vulnerable patient” may be more appropriate and is proposed now for the identification of subjects with high likelihood of developing cardiac events in the near future. (3) A quantitative method for cumulative risk assessment of vulnerable patients needs to be developed that may include variables based on plaque, blood, and myocardial vulnerability. In Part I of this consensus document, we cover the new definition of vulnerable plaque and its relationship with vulnerable patients. Part II of this consensus document focuses on vulnerable blood and vulnerable myocardium and provide an outline of overall risk assessment of vulnerable patients. Parts I and II are meant to provide a general consensus and overviews the new field of vulnerable patient. Recently developed assays (eg, C-reactive protein), imaging techniques (eg, CT and MRI), noninvasive electrophysiological tests (for vulnerable myocardium), and emerging catheters (to localize and characterize vulnerable plaque) in combination with future genomic and proteomic techniques will guide us in the search for vulnerable patients. It will also lead to the development and deployment of new therapies and ultimately to reduce the incidence of acute coronary syndromes and sudden cardiac death. We encourage healthcare policy makers to promote translational research for screening and treatment of vulnerable patients.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/01.CIR.0000087480.94275.97
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2003
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  • 4
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    Online Resource
    Abstract 17154: Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA Offering new opportunities for Short-Term Risk Prediction and Early Detection of the Vulnerable Patient
    Ovid Technologies (Wolters Kluwer Health) ; 2018
    In:  Circulation Vol. 138, No. Suppl_1 ( 2018-11-06)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 138, No. Suppl_1 ( 2018-11-06)
    Abstract: Introduction: Machine learning (ML) is poised to revolutionize healthcare. Current national guidelines for prediction and prevention of atherosclerotic cardiovascular disease (ASCVD) use ACC/AHA Pooled Cohort Equation Risk Calculator which relies on traditional risk factors and linear statistical models. Unfortunately, this approach yields a low level of sensitivity and specificity. The low sensitivity results in missing high-risk individuals who need intensive therapy and the low specificity results in millions of people unnecessarily recommended drugs such as statin. We aimed to utilize Machine Learning (ML) to create a more accurate predictor of ASCVD events and whom to recommend statin. Methods: We developed and validated a ML Risk Calculator based on Support Vector Machines (SVMs) using the latest 13-year follow up dataset from MESA (Multi-Ethnic Study of Atherosclerosis) of 6,459 participants who were free of cardiovascular disease at baseline. We provided identical input to the ACC/AHA and ML risk calculators and compared their accuracy. We also validated the ML model in another longitudinal cohort: the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO). Results: According to the ACC/AHA Risk Calculator and a 7.5% 10-year risk threshold, 46.0% would be recommended statin. Despite this high proportion, 23.8% of the 480 “Hard CVD” events occurred in those not recommended statin, resulting in sensitivity (Sn) 0.76, specificity (Sp) 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended statin to 11.4%, and only 14.4% of “Hard CVD” events occurred in those not recommended statin, resulting in Sn 0.86, Sp 0.95, and AUC 0.92. Similar results were seen in prediction of “All CVD” events. Conclusions: The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in predicting short-term (1-5 years) events with additional biomarkers including imaging. Machine learning is paving the way for early detection of asymptomatic high-risk individuals destined to a CVD event in the near future, the Vulnerable Patient.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.138.suppl_1.17154
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2018
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  • 5
    Online Resource
    Online Resource
    Vulnerable Atherosclerotic Plaque : A Multifocal Disease
    Ovid Technologies (Wolters Kluwer Health) ; 2003
    In:  Circulation Vol. 107, No. 16 ( 2003-04-29), p. 2072-2075
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 107, No. 16 ( 2003-04-29), p. 2072-2075
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/01.CIR.0000069329.70061.68
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2003
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  • 6
    Online Resource
    Online Resource
    The First SHAPE (Screening for Heart Attack Prevention and Education) Guideline
    Ovid Technologies (Wolters Kluwer Health) ; 2006
    In:  Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine Vol. 5, No. 4 ( 2006-12), p. 187-190
    Details
    In: Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 5, No. 4 ( 2006-12), p. 187-190
    Type of Medium: Online Resource
    ISSN: 1535-282X
    URL: Article
    DOI: 10.1097/01.hpc.0000249784.29151.54
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2006
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  • 7
    Online Resource
    Online Resource
    Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
    Ovid Technologies (Wolters Kluwer Health) ; 2018
    In:  Journal of the American Heart Association Vol. 7, No. 22 ( 2018-11-20)
    Details
    In: Journal of the American Heart Association, Ovid Technologies (Wolters Kluwer Health), Vol. 7, No. 22 ( 2018-11-20)
    Abstract: Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high‐risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13‐year follow up data set from MESA (the Multi‐Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD‐free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10‐year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 “Hard CVD ” events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of “Hard CVD ” events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of “All CVD ” events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short‐term CVD risk prediction.
    Type of Medium: Online Resource
    ISSN: 2047-9980
    URL: Article
    DOI: 10.1161/JAHA.118.009476
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2018
    detail.hit.zdb_id: 2653953-6
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  • 8
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    Intravascular thermography: a novel approach for detection of vulnerable plaque
    Ovid Technologies (Wolters Kluwer Health) ; 2002
    In:  Current Opinion in Cardiology Vol. 17, No. 6 ( 2002-11), p. 656-662
    Details
    In: Current Opinion in Cardiology, Ovid Technologies (Wolters Kluwer Health), Vol. 17, No. 6 ( 2002-11), p. 656-662
    Type of Medium: Online Resource
    ISSN: 0268-4705
    URL: Article
    DOI: 10.1097/00001573-200211000-00012
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2002
    detail.hit.zdb_id: 2026894-4
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  • 9
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    Online Resource
    Superparamagnetic Iron Oxide–Based Method for Quantifying Recruitment of Monocytes to Mouse Atherosclerotic Lesions In Vivo : Enhancement by Tissue Necrosis Factor-α, Interleukin-1β, and Interferon-γ
    Ovid Technologies (Wolters Kluwer Health) ; 2003
    In:  Circulation Vol. 107, No. 11 ( 2003-03-25), p. 1545-1549
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 107, No. 11 ( 2003-03-25), p. 1545-1549
    Abstract: Background— It has been found recently that the MRI contrast agent superparamagnetic iron oxide (SPIO) localizes to aortic atherosclerotic plaques. We therefore asked whether SPIO might be used to monitor monocyte recruitment into aortic atherosclerotic plaques. Methods and Results— Eleven female apo E knockout (K/O) mice, each 11 months old, were divided into 2 groups. Six mice received tissue necrosis factor-α (0.2 μg IP once), interleukin-1β (0.2 μg IP once), and interferon-γ (100 U/g per day IP for 5 days); 5 received 0.5 mL saline containing1% BSA and served as sham-treated atherosclerotic controls. Two wild-type C57BL/6 mice served as sham-treated nonatherosclerotic controls. Three hours after initial cytokine or sham treatment, all mice received SPIO by intravenous injection (1 mmol/kg iron). Six days later, all mice were euthanized, the hearts and aortas were perfused under physiological pressure, and the entire aortas were studied histologically. Atherosclerotic plaques in cytokine-treated mice contained more iron-positive macrophages per cross section than did those in sham-treated apo E K/O control mice (42±11.8 versus 11.6±5.9) ( P 〈 0.0001). Iron-laden macrophages were present either in subendothelial plaque surfaces or in thin layers overlying the internal elastic lamina, often at the edges of atherosclerotic plaques. No iron deposition was seen in aortas of the wild-type nonatherosclerotic control mice. Immunocytochemistry showed mostly macrophages and few T lymphocytes in atherosclerotic plaques of cytokine-treated mice. Conclusions— SPIO allows detection of iron-laden macrophages in the aortic subendothelium of apo E–deficient mice under basal conditions and monitoring of monocyte recruitment after cytokine injection.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/01.CIR.0000055323.57885.88
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2003
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  • 10
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    Abstract 23075: Machine Learning Outperformed ACC/AHA Pooled Cohort Equations Risk Calculator for Detection of High-Risk Asymptomatic Individuals and Recommending Treatment for Prevention of Cardiovascular Events in the Multi-Ethnic Study of Atherosclerosis (MESA)
    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)
    Abstract: Background: Studies have shown that the status quo for atherosclerotic cardiovascular disease (ASCVD) prediction in the U.S. - using ACC/AHA Pooled Cohort Equations Risk Calculator - is inaccurate and results in overtreatment of low-risk and undertreatment of high-risk individuals. Machine Learning (ML) is poised to revolutionize healthcare. We used ML to develop a new ASCVD risk calculator and tackled the problem. Methods: We developed a ML Risk Calculator using the latest 13-year follow up dataset from MESA (Multi-Ethnic Study of Atherosclerosis) of 6,814 participants who were free of clinical CVD at baseline. We gave identical input to both calculators and compared their accuracy for recommending statin to 5,415 subjects (age 60.6 ± 9.7 years; 47.3% males) who were not on lipid lowering treatment at baseline. Results: Over 13 years, 775 (14.3%) “All CVD” and 381 (7.0%) “Hard CVD” events occurred. According to ACC/AHA Risk Calculator and a 7.5% 10-year risk threshold for treatment, 42.9% would be recommended to take statin. Despite the high proportion recommended for statin treatment, 25.7% of “Hard CVD” and 26.3% of “All CVD” events occurred in those not recommended statin, resulting in sensitivity (Sn) 0.74, specificity (Sp) 0.60, and AUC 0.72 for “Hard CVD” and Sn 0.73, Sp 0.62, and AUC 0.73 for “All CVD”. In sharp contrast, the ML Risk Calculator recommended only 10.6% to take statin, and only 15.0% of “Hard CVD” and 4.9% of “All CVD” events occurred in those not recommended statin, resulting in Sn 0.84, Sp 0.95, and AUC 0.92 for “Hard CVD” and Sn 0.95, Sp 0.88, and AUC 0.95 for “All CVD”. Conclusions: ML clearly outperformed the ACC/AHA Risk Calculator by recommending less drug therapy and missing fewer events. Further studies are underway to validate these findings in other cohorts. As we introduce our ML model to more data particularly to cases in which events occurred weeks or months following data collection instead of years, short-term risk prediction may be possible.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.136.suppl_1.23075
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
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