scholarly journals Risk-prediction tools for cardiovascular disease based on Japanese cohort studies

2009 ◽  
Vol 32 (12) ◽  
pp. 1053-1054
Author(s):  
Tomonori Okamura ◽  
Aya Higashiyama
Keyword(s):  
Cardiovascular Disease ◽  
Cohort Studies ◽  
Risk Prediction ◽  
Prediction Tools ◽  
Japanese Cohort

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 9 (5) ◽  
pp. 522-532 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 18 (7) ◽  
pp. 534-544 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Prediction of cardiovascular risk in cancer patients of South India using WHO/ISH risk prediction charts and Framingham score – A prospective study

2017 ◽  
Vol 24 (5) ◽  
pp. 354-358 ◽  
Author(s):  
MG Rajanandh ◽  
S Suresh ◽  
K Manobala ◽  
R Nandhakumar ◽  
G Jaswanthi ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Prediction ◽  
Smoking Status ◽  
Risk Groups ◽  
Framingham Score ◽  
Prediction Tools ◽  
Significant Difference

Objective Despite the fact that cancer and heart diseases are interconnected, there is lack of information about the prevalence of cardiovascular risk in cancer patients in the South Indian population. With this background, the present study sought to predict the cardiovascular disease in cancer patients. Methods A prospective, cross-sectional study was conducted in the Department of Medical Oncology, Sri Ramachandra University and Hospital, India. Patients’ demographic details, medical information, height, weight, body mass index, blood pressure, total cholesterol and HDL-cholesterol were measured. Two risk prediction tools, namely World Health Organization/International Society of hypertension (WHO/ISH) risk prediction charts and Framingham score were used to assess the prevalence of cardiovascular risk over 10 years. Results A total of 70 patients were included for the study. Breast and stomach cancer were found to be most among the study patients. Cardiovascular disease was assessed using WHO/ISH and Framingham risk assessment tool. With respect to WHO/ISH risk, there is a significant difference in gender, type of cancer, smoking status and age between the risk groups. Males have a high risk compared to females, and smokers have a high risk compared to non-smokers. With respect to Framingham score, there is a significant difference in gender, smoking status and systolic blood pressure between the risk groups. Males have a high risk compared to females, and smokers have a high risk compared to non-smokers. A moderate degree of agreement exists between the two risk prediction tools. Conclusion The findings of the study revealed that there is a low risk of cardiovascular disease in cancer patients.

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 26 (14) ◽  
pp. 1534-1544 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

scholarly journals Dose–Response Relation between Tea Consumption and Risk of Cardiovascular Disease and All-Cause Mortality: A Systematic Review and Meta-Analysis of Population-Based Studies

2020 ◽  
Vol 11 (4) ◽  
pp. 790-814 ◽  
Author(s):  
Mei Chung ◽  
Naisi Zhao ◽  
Deena Wang ◽  
Marissa Shams-White ◽  
Micaela Karlsen ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cohort Studies ◽  
Prospective Cohort ◽  
Lower Risk ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Tea Consumption ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Cvd Mortality

ABSTRACT Tea flavonoids have been suggested to offer potential benefits to cardiovascular health. This review synthesized the evidence on the relation between tea consumption and risks of cardiovascular disease (CVD) and all-cause mortality among generally healthy adults. PubMed, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, Food Science and Technology Abstracts, and Ovid CAB Abstract databases were searched to identify English-language publications through 1 November 2019, including randomized trials, prospective cohort studies, and nested case-control (or case-cohort) studies with data on tea consumption and risk of incident cardiovascular events (cardiac or peripheral vascular events), stroke events (including mortality), CVD-specific mortality, or all-cause mortality. Data from 39 prospective cohort publications were synthesized. Linear meta-regression showed that each cup (236.6 mL)  increase in daily tea consumption (estimated 280 mg  and 338 mg  total flavonoids/d for black and green tea, respectively) was associated with an average 4% lower risk of CVD mortality, a 2% lower risk of CVD events, a 4% lower risk of stroke, and a 1.5% lower risk of all-cause mortality. Subgroup meta-analysis results showed that the magnitude of association was larger in elderly individuals for both CVD mortality (n = 4; pooled adjusted RR: 0.89; 95% CI: 0.83, 0.96; P = 0.001), with large heterogeneity (I2 = 72.4%), and all-cause mortality (n = 3; pooled adjusted RR: 0.92; 95% CI: 0.90, 0.94; P < 0.0001; I2 = 0.3%). Generally, studies with higher risk of bias appeared to show larger magnitudes of associations than studies with lower risk of bias. Strength of evidence was rated as low and moderate (depending on study population age group) for CVD-specific mortality outcome and was rated as low for CVD events, stroke, and all-cause mortality outcomes. Daily tea intake as part of a healthy habitual dietary pattern may be associated with lower risks of CVD and all-cause mortality among adults.

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