Coronary heart disease incidence still decreased between 2006 and 2014 in France, except in young age groups: Results from the French MONICA registries

Meirhaeghe, Aline; Montaye, Michèle; Biasch, Katia; Kai, Samantha Huo Yung; Moitry, Marie; Amouyel, Philippe; Ferrières, Jean; Dallongeville, Jean

doi:10.1177/2047487319899193

Cited by 14 publications

(4 citation statements)

References 25 publications

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“…While the unfavorable CVD mortality trends are a concern in the US, in Europe recent mortality data do not support such a concern [ 16 ]. Available data from six European populations indicate that from 1985 to 2010 acute MI mortality decreased [ 17 ] and similarly, decreased from 2001 to 2014 in Norway [ 18 ], while in France CHD mortality decreased overall from 2006 to 2014 [ 19 ]. Adverse trends in risk factors, such as diabetes and obesity [ 4 , 5 ], could however unfortunately lead to unfavorable CVD mortality trends as is currently observed in MN and the US.…”

Section: Discussionmentioning

confidence: 99%

Recent trends in cardiovascular disease deaths: a state specific perspective

et al. 2021

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Background The rate of decline in cardiovascular disease (CVD) mortality has lessened nationally. How these findings apply to specific states or causes of CVD deaths is not known. Examining these trends at the state level is important to plan local interventions. Methods We analyzed CVD mortality trends in Minnesota (MN) using the U.S. Centers for Disease Control and Prevention (CDC) Wide-ranging ONline Data for Epidemiologic Research (WONDER). Trends were analyzed by age, sex, type of CVD and location of death. Results CVD mortality rates in MN declined in 2000–2009 and then leveled off in 2010–2018, paralleling national rates. Age- and sex-adjusted CVD mortality decreased by 3.7% per year in 2000–2009 (average annual percent changes [AAPC]: -3.7; 95% CI: − 4.8, − 2.6) with no change observed in 2010–2018. Those aged 65–84 years had the most rapid early decline in CVD mortality (AAPC: -5.9, 95% CI: − 6.2, − 5.7) and had less improvement in 2010–2018 (AAPC: -1.8, 95% CI: − 2.2, − 1.5), and the younger age group (25–64 years) now experiences the most adverse trends (AAPC: 1.2, 95% CI: 0.7–1.8). Coronary heart disease (CHD) and cerebrovascular disease had the largest relative decreases in mortality in 2000–2009 (CHD AAPC: -5.2; 95% CI: − 6.5,-3.9; cerebrovascular disease AAPC: -4.4, 95% CI: − 5.2, − 3.6) with no change 2010–2018. Heart failure (HF)/cardiomyopathy followed similar trends with a 2.5% decrease (AAPC 95% CI: − 3.5, − 1.5) per year in 2000–2009 and no change in 2010–2018. Deaths from other CVD also decreased in the early time period (AAPC: -1.6, 95% CI: − 2.7, − 0.5) but increased in 2010–2018 (AAPC: 1.9, 95% CI: 0.5, 3.3). In- and out-of-hospital death rates improved in 2000–2009 with a slowing in improvement for in-hospital death and no further improvement for out-of-hospital death in 2010–2018. Conclusion Concerning CVD mortality trends occurred in MN. In the most recent decade (2010–2018) mortality from all CVD subtypes plateaued or even increased. CVD mortality among the younger age groups increased as well. These data are congruent with adverse national trends supporting their generalizability. These adverse trends underscore the urgent need for CVD prevention and treatment, as well as continued surveillance to assess progress at the state and national level.

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Section: Discussionmentioning

confidence: 99%

Recent trends in cardiovascular disease deaths: a state specific perspective

et al. 2021

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“…However, some populations experience this disease prematurely [ 2 ]. In the last few decades, different studies have shown an increased incidence of CAD in very young people [ 3 , 4 , 5 ], mainly in Asian populations [ 6 ]. Even though they are relatively few in number, these young patients with CAD represent a significant economic and health care burden for society, thus becoming chronic patients [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Coronary Artery Disease in Very Young Patients: Analysis of Risk Factors and Long-Term Follow-Up

Juan-Salvadores

Díaz

Carreño

et al. 2022

JCDD

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Coronary artery disease (CAD) is a common chronic condition in the elderly. However, the earlier CAD begins, the stronger its impact on lifestyle and costs of health and social care. The present study analyzes clinical and angiographic features and the outcome of very young patients undergoing coronary angiography due to suspected CAD, including a nested case-control study of ≤40-year-old patients referred for coronary angiography. Patients were divided into two groups: cases with significant angiographic stenosis, and controls with non-significant stenosis. Of the 19,321 coronary angiographies performed in our center in a period of 10 years, 504 (2.6%) were in patients ≤40 years. The most common cardiovascular risk factors for significant CAD were smoking (OR 2.96; 95% CI 1.65–5.37), dyslipidemia (OR 2.18; 95% CI 1.27–3.82), and family history of CAD (OR 1.95; 95% CI 1.05–3.75). The incidence of major adverse cardiovascular events (MACE) at follow-up was significantly higher in the cases compared to controls (HR 2.71; 95% CI 1.44–5.11). Three conventional coronary risk factors were directly related to the early signs of CAD. MACE in the long-term follow-up is associated to dyslipidaemia and hypertriglyceridemia. Focusing efforts for the adequate control of CAD in young patients is a priority given the high socio-medical cost that this disease entails to society.

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“…Coronary artery disease (CAD) is a very important chronic condition associated with aging and consequently is scarcely present in young people [ 1 ]. However, different studies have shown an increased incidence of CAD in young subjects over the last decades [ 2 , 3 ]. Even few, those young patients with CAD mean a significant economic and health care-needs burden for the society, thus becoming chronic patients [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients

et al. 2022

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Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69–0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53–0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71–0.89) vs. LR 0.50 (95%CI 0.33–0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.

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Coronary heart disease incidence still decreased between 2006 and 2014 in France, except in young age groups: Results from the French MONICA registries

Cited by 14 publications

References 25 publications

Recent trends in cardiovascular disease deaths: a state specific perspective

Recent trends in cardiovascular disease deaths: a state specific perspective

Coronary Artery Disease in Very Young Patients: Analysis of Risk Factors and Long-Term Follow-Up

Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients

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