Literature DB >> 24279112

Cardiovascular disease and bridging the diagnostic gap.

John Kelly Wachira1, Tom P Stys.   

Abstract

Cardiovascular disease (CVD) is now the leading cause of death worldwide. It continues to be on the rise and has become a true pandemic that has no respect to borders.' Coronary artery disease (CAD) is the most common type of CVD. It continues to be the leading cause of mortality both in men and women in the U.S.' Approximately every 25 seconds, an American will suffer an acute coronary syndrome, and approximately every minute someone will die of one. Risk stratification and early disease detection continue to be the bedrock of most preventative strategies. Risk assessment tools like Framingham Heart Score (FHS used in the U.S.), prospective cardiovascular monster (PROCAM used in Germany), or systemic coronary risk evaluation (SCORE used in Europe) are among the common and widely available estimators of a multi-factorial absolute risk of developing CVD.6 Recently, coronary artery calcium (CAC) has emerged as a non-invasive modality that might improve prediction of future cardiovascular events. We have conducted a comprehensive review of CVD risk factors, risk assessment and screening tools being applied to aid in early detection of CVD. As we work on bridging the diagnostic gap of the leading cause of mortality across the globe, utility of accurate and sensitive risk assessment and screening tools for early CVD detection is vital. This will aid in our goal of early detection, modifying risk factors and prevention of CVD incidence.

Entities:  

Mesh:

Year:  2013        PMID: 24279112

Source DB:  PubMed          Journal:  S D Med        ISSN: 0038-3317


  8 in total

1.  Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis.

Authors:  William J Gibson; Tarek Nafee; Ryan Travis; Megan Yee; Mathieu Kerneis; Magnus Ohman; C Michael Gibson
Journal:  J Thromb Thrombolysis       Date:  2020-01       Impact factor: 2.300

2.  Significance of green granules in neutrophils and monocytes.

Authors:  Tina Gorup; Andrea T Cohen; Amelia B Sybenga; Edward S Rappaport
Journal:  Proc (Bayl Univ Med Cent)       Date:  2017-12-29

Review 3.  Cardiac Syndrome X: update 2014.

Authors:  Shilpa Agrawal; Puja K Mehta; C Noel Bairey Merz
Journal:  Cardiol Clin       Date:  2014-06-02       Impact factor: 2.213

4.  Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome.

Authors:  Agnė Šatrauskienė; Rokas Navickas; Aleksandras Laucevičius; Tomas Krilavičius; Rūta Užupytė; Monika Zdanytė; Ligita Ryliškytė; Agnė Jucevičienė; Paul Holvoet
Journal:  Int J Environ Res Public Health       Date:  2021-02-04       Impact factor: 3.390

5.  In vitro effects of sodium nitroprusside and leptin on norepinephrine-induced vasoconstriction in human internal mammary artery.

Authors:  Oktay Burma; Mete Ozcan; Emine Kacar; Ayhan Uysal; Engin Şahna; Ahmet Ayar
Journal:  Cardiovasc J Afr       Date:  2014-12-11       Impact factor: 1.167

Review 6.  Interaction of Hydrogen Sulfide with Nitric Oxide in the Cardiovascular System.

Authors:  B V Nagpure; Jin-Song Bian
Journal:  Oxid Med Cell Longev       Date:  2015-11-10       Impact factor: 6.543

7.  Association between α1-antitrypsin and acute coronary syndrome.

Authors:  Yan Liu; Da Huang; Beilin Li; Wenjing Liu; Suren R Sooranna; Xingshou Pan; Zhaohe Huang; Jun Guo
Journal:  Exp Ther Med       Date:  2020-09-21       Impact factor: 2.447

8.  Amygdalin Attenuates Atherosclerosis and Plays an Anti-Inflammatory Role in ApoE Knock-Out Mice and Bone Marrow-Derived Macrophages.

Authors:  Yiru Wang; Qingyun Jia; Yifan Zhang; Jing Wei; Ping Liu
Journal:  Front Pharmacol       Date:  2020-10-29       Impact factor: 5.810

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.