Clustering of Lifestyle Risk Factors in Acute Coronary Syndrome: Prevalence and Change after the First Event

2018 ◽  
Vol 10 (3) ◽  
pp. 434-456
Author(s):  
Dario Monzani ◽  
Marco D'Addario ◽  
Francesco Fattirolli ◽  
Cristina Giannattasio ◽  
Andrea Greco ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Lifestyle Risk Factors ◽  
Coronary Syndrome ◽  
Lifestyle Risk

IMPACT OF RISK FACTORS OF ISCHEMIC HEART DISEASE ON THE DEVELOPMENT OF ACUTE CORONARY SYNDROME, PLATELET ULTRASTRUCTURE, AND ASPIRIN RESISTANCE

2019 ◽  
Vol 72 (11) ◽  
Author(s):  
Yulian H. Kyyak ◽  
Olga Yu. Barnett ◽  
Marta P. Halkevych ◽  
Olha Ye. Labinska ◽  
Hryhoriy Yu. Kyyak ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Aspirin Resistance ◽  
Ischemic Heart ◽  
Coronary Syndrome

IMPACT OF RISK FACTORS OF ISCHEMIC HEART DISEASE ON THE DEVELOPMENT OF ACUTE CORONARY SYNDROME, PLATELET ULTRASTRUCTURE, AND ASPIRIN RESISTANCE

2019 ◽  
Vol 72 (11) ◽  
Author(s):  
Yulian H. Kyyak ◽  
Olga Yu. Barnett ◽  
Marta P. Halkevych ◽  
Olha Ye. Labinska ◽  
Hryhoriy Yu. Kyyak ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Aspirin Resistance ◽  
Ischemic Heart ◽  
Coronary Syndrome

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

scholarly journals Risk factors and clinical outcomes of acute coronary syndrome in men: A retrospective study

2020 ◽  
Vol 72 ◽  
pp. S5
Author(s):  
Shahood Ajaz Kakroo ◽  
Kala Jeethender Kumar ◽  
O. Sai Satish ◽  
M. Jyotsna ◽  
B. Srinivas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Retrospective Study ◽  
Clinical Outcomes ◽  
Coronary Syndrome

scholarly journals The Prevalence of Insulin Resistance and the Associated Risk Factors in a Sample of 14–18-Year-Old Slovak Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 909
Author(s):  
Jana Jurkovičová ◽  
Katarína Hirošová ◽  
Diana Vondrová ◽  
Martin Samohýl ◽  
Zuzana Štefániková ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Insulin Resistance ◽  
Venous Blood ◽  
Significant Risk ◽  
Lifestyle Changes ◽  
Skewed Distribution ◽  
Model Assessment ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

The prevalence of cardiometabolic risk factors has increased in Slovakian adolescents as a result of serious lifestyle changes. This cross-sectional study aimed to assess the prevalence of insulin resistance (IR) and the associations with cardiometabolic and selected lifestyle risk factors in a sample of Slovak adolescents. In total, 2629 adolescents (45.8% males) aged between 14 and 18 years were examined in the study. Anthropometric parameters, blood pressure (BP), and resting heart rate were measured; fasting venous blood samples were analyzed; and homeostasis model assessment (HOMA)-insulin resistance (IR) was calculated. For statistical data processing, the methods of descriptive and analytical statistics for normal and skewed distribution of variables were used. The mean HOMA-IR was 2.45 ± 1.91, without a significant sex differences. IR (cut-off point for HOMA-IR = 3.16) was detected in 18.6% of adolescents (19.8% males, 17.6% females). IR was strongly associated with overweight/obesity (especially central) and with almost all monitored cardiometabolic factors, except for total cholesterol (TC) and systolic BP in females. The multivariate model selected variables such as low level of physical fitness, insufficient physical activity, breakfast skipping, a small number of daily meals, frequent consumption of sweetened beverages, and low educational level of fathers as significant risk factors of IR in adolescents. Recognizing the main lifestyle risk factors and early IR identification is important in terms of the performance of preventive strategies. Weight reduction, regular physical activity, and healthy eating habits can improve insulin sensitivity and decrease the incidence of metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD).

scholarly journals A study of risk factors and coronary angiographic profile in young patients with acute coronary syndrome

2020 ◽  
Vol 72 ◽  
pp. S6-S7
Author(s):  
Bodhisattya Roy Chaudhuri ◽  
Ram Pratap Saini ◽  
Sandeep Bansal
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Young Patients ◽  
Coronary Syndrome
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