Twenty-five years of the TARF study: The 2015 survey, and temporal trends in mortality and loss to follow-up

Onat, Altan; Kaya, Adnan; Şimşek, Tuğba Akbaş; Şimşek, Barış; Tusun, Eyyup; Karadeniz, Yusuf; Can, Günay

doi:10.5543/tkda.2016.87400

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…There are several national surveys conducted in Turkey to determine the prevalence of major risk factors in the adult population. Among these Turkish Adults Risk Factors Study (TARF) − the earliest and longest epidemiological study was a comprehensive cohort study that evaluated CVD and its risk factors in Turkey from 1992 to 2016 [ 7 , 17 ]. More recently, Turkish Society of Cardiology (TSC) conducted meta-analyses enrolling epidemiologic studies with low bias and high representativeness of the country population distribution using data from the past 15 years [12] , [13] , [14] , [15] , [16] .…”

Section: Major Cardiovascular Risk Factorsmentioning

confidence: 99%

The landscape of preventive cardiology in Turkey: Challenges and successes

Tokgözoğlu

Kayıkçıoğlu

Ekinci

2021

American Journal of Preventive Cardiology

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Turkey, like many countries, is facing a growing burden of non-communicable disease (NCD)s and is among the countries with high cardiovascular mortality in Europe. Moreover, Turkey currently has the highest rate of premature cardiovascular disease (CVD) in Europe. During the last decades, Turkey made fundamental reforms in the health system to improve the treatment of risk factors to prevent CVD. The most outstanding success was in the area of tobacco control (13.4% decrease in smoking prevalence) and decreasing the salt consumption of the population (from 18 to 9.9 g/day) leading to a significant decrease in CVD mortality from 45% to 36.8% of all deaths. However, obesity and diabetes are increasing rapidly as a result of urbanization, low physical activity and unhealthy eating and the new generation is starting to take up smoking. The increase in cardiometabolic risk factors and aging of the population are expected to increase the number of CVD deaths. All CVD risk factors except smoking are significantly more prevalent in women. In addition, rare disease is a country specific problem with a significant contribution to the high rates of premature CVD in Turkey. Despite major improvements in management in acute coronary syndromes, sustained achievement in guideline recommended goals is suboptimal. In patients with a previous cardiovascular event smoking rate is 25.5%, 80.9% of these patients are overweight (BMI ≥25 kg/m 2 ), 30% obese (BMI ≥30 kg/m 2 ), and LDL‐cholesterol targets of 70 mg/dL are attained in only 18%. Herein, we scrutinize the achievements and challenges of Turkey in establishing a ‘National Heart Health Policy’ aiming to decrease the burden of CVD and its risk factors.

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Section: Major Cardiovascular Risk Factorsmentioning

confidence: 99%

The landscape of preventive cardiology in Turkey: Challenges and successes

Tokgözoğlu

Kayıkçıoğlu

Ekinci

2021

American Journal of Preventive Cardiology

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The Association Between Multivessel Disease and Cardiovascular Risk Factors in Patients with Acute Coronary Syndrome

et al. 2019

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Akut koroner sendrom olgularında ciddi darlık olan koroner arter sayısı ile kardiyovasküler risk faktörleri arasındaki ilişki değerlendirildi. Yöntemler: Bu çalışma retrospektif olarak dizayn edildi. 2016-2018 tarihleri arasında akut koroner sendrom sebebiyle koroner anjiografi yapılan 904 hasta çalışmaya dahil edildi. Ciddi koroner arter darlığı olmayan ve koroner operasyon geçiren hastalar çalışma dışı bırakıldı. Hastalar kritik koroner arter darlıklarına göre tek damar hastalığı (TDH) ve çok damar hastalığı (ÇDH) olacak şekilde iki gruba ayrıldı. ÇDH ile TDH olanlar klasik kardiyovasküler risk faktörleri açısından karşılaştırıldı. Bulgular: Hastaların 544'ünde (%60) ÇDH 365'inde (%40) TDH saptandı. ÇDH olan grupta yaş, hipertansiyon ve diyabet mellitus istatistiksel olarak daha fazla saptandı (sırasıyla p<0.001, p=0.003, p=0.005). TDH olan grupta ise erkek cinsiyet, aile öyküsü ve sigara içiciliği istatistiksel olarak daha fazla saptandı (sırasıyla p=0.006, p<0.001, p<0.001). Sonuç: Diyabet ve hipertansiyon ÇDH grubunda yüksek saptanırken aile öyküsü ve sigara içiciliği TDH grubunda yüksek saptandı. Akut koroner sendrom hastalarında bu risk faktörlerin göz önünde bulundurulması önemlidir.

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Early Diagnoses of Acute Coroner Syndrome Based on Machine Learning Model

Tiryaki,

Karaduman,

Cuhadar

et al. 2024

International Journal of Engineering and Applied Sciences

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Cardiovascular diseases are a leading global cause of death, particularly in low to middle-income countries. Early and accurate diagnosis of Acute Coronary Syndrome (ACS) is vital, but limited access to healthcare hinders effective management. This study utilized machine learning to develop mathematical models for ACS risk detection. Data from 249 individuals with ACS or suspected heart disease were used to construct twelve models with different parameters and classifiers. Performance indicators, including accuracy, Matthews correlation coefficient, and precision, were employed for evaluation. The Random Forest classifier demonstrated superior performance, achieving 90.45% accuracy for internal validation and 86% for external validation. Critical criteria for ACS diagnosis were CK-MB, age, coronary artery disease, and Troponin T value. The models developed in this study significantly prevent potential deaths via rapid intervention and reduce healthcare expenditures by minimizing unnecessary human resources and repeat tests.

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Twenty-five years of the TARF study: The 2015 survey, and temporal trends in mortality and loss to follow-up

Cited by 3 publications

References 5 publications

The landscape of preventive cardiology in Turkey: Challenges and successes

The landscape of preventive cardiology in Turkey: Challenges and successes

The Association Between Multivessel Disease and Cardiovascular Risk Factors in Patients with Acute Coronary Syndrome

Early Diagnoses of Acute Coroner Syndrome Based on Machine Learning Model

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