Personal Genome Interpretation and Disease Risk Prediction

2019 ◽  
pp. 47-75
Author(s):  
Ju Han Kim
Keyword(s):  
Risk Prediction ◽  
Disease Risk ◽  
Personal Genome ◽  
Genome Interpretation

scholarly journals Cardiovascular Risk Assessment in Angolan Adults: A Descriptive Analysis from CardioBengo, a Community-Based Survey

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
João M. Pedro ◽  
Miguel Brito ◽  
Henrique Barros
Keyword(s):  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Disease Risk ◽  
Descriptive Analysis ◽  
World Health Organisation ◽  
World Health ◽  
Lipid Lowering ◽  
Community Based ◽  
Risk Factor Surveillance ◽  
Using Data

From a community-based survey conducted in Angola, 468 individuals aged 40 to 64 years and not using drug therapy were evaluated according to the World Health Organisation STEPwise Approach to Chronic Disease Risk Factor Surveillance. Using data from tobacco use, blood pressure, blood glucose, and total cholesterol levels, we estimated the 10-year risk of a fatal or nonfatal major cardiovascular event and computed the proportion of untreated participants eligible for pharmacological treatment according to clinical values alone and total cardiovascular risk. The large majority of participants were classified as having a low (<10%) 10-year cardiovascular risk (87.6%), with only 4.5% having a high (≥ 20%) cardiovascular risk. If we consider the single criteria for hypertension, 48.7% of the population should be considered for treatment. This value decreases to 22.0% if we apply the risk prediction chart. The use of hypoglycaemic drugs does not present any differences (19.0% in both situations). The use of lipid-lowering drugs (3.8%) is only recommended by the risk prediction chart. This study reveals the need of integrated approaches for the treatment of cardiovascular disorders in this population. Risk prediction charts can be used as a way to promote a better use of limited resources.

scholarly journals The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes

Diabetologia ◽  
2018 ◽  
Vol 62 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Jingchuan Guo ◽  
Sebhat A. Erqou ◽  
Rachel G. Miller ◽  
Daniel Edmundowicz ◽  
Trevor J. Orchard ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Coronary Artery Calcification ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

scholarly journals Predicting 10-year cardiovascular risk using WHO/ISH risk prediction chart among urban population in Salem

2018 ◽  
Vol 5 (12) ◽  
pp. 5228
Author(s):  
K. Premanandh ◽  
R. Shankar
Keyword(s):  
Cardiovascular Risk ◽  
High Risk ◽  
Risk Prediction ◽  
Disease Risk ◽  
Smoking Status ◽  
Cardiovascular Disease Risk ◽  
Total Serum ◽  
Total Serum Cholesterol ◽  
Cvd Risk ◽  
Very High

Background: Coronary vascular disease (CVD) risk estimation tools are a simple means of identifying those at high risk in a community and hence a potentially cost-effective strategy for CVD prevention in resource-poor countries. The WHO /ISH risk prediction charts provide approximate estimates of cardiovascular disease risk in people who do not have established coronary heart disease, stroke or other atherosclerotic disease.Methods: A total of 280 subjects between 40 to 70 years of age were included in this cross sectional study. Eligible households was selected randomly (every 5th household) for the interview using systematic random sampling. Age, gender, smoking status, systolic blood pressure, presence or absence of diabetes and total serum cholesterol were used to compute the total CVD risk using WHO/ISH CVD risk prediction chart. The chart stratify an individual into low (<10%), moderate (10% to <20%), high (20% to <30%), and very high (>30%) risk groups.Results: Moderate and high CVD risk were 12.14% and 7.5% respectively. Of total study participants, 2.5% had very high risk (>40%). High risk (binge drinking) alcohol drinkers (p=0.04) and abdominal obesity (p=0.0001) were significantly associated with higher CVD risk. Higher prevalence of behavioral risk factors was also reported in our study population.Conclusions: A large proportion of the population is at moderate and high cardiovascular risk. Risk stratification and identification of individuals with a high risk for CHD who could potentially benefit from intensive primary prevention efforts are critically important in reducing the burden of CVD in India.

scholarly journals Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Huanhuan Zhao ◽  
Xiaoyu Zhang ◽  
Yang Xu ◽  
Lisheng Gao ◽  
Zuchang Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Risk Prediction ◽  
Disease Risk ◽  
Learning Algorithms ◽  
Large Population ◽  
Machine Learning Algorithms ◽  
Hypertension Risk ◽  
Model Training

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

scholarly journals Development of Appropriate Coronary Heart Disease Risk Prediction Models in HIV-Infected Patients

Circulation ◽  
2008 ◽  
Vol 118 (2) ◽  
Author(s):  
Morris Schambelan ◽  
Peter W.F. Wilson ◽  
Kevin E. Yarasheski ◽  
W. Todd Cade ◽  
Victor G. Dávila-Román ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Coronary Heart Disease Risk ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Prediction Models ◽  
Heart Disease Risk
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