Contribution of Major Lifestyle Risk Factors for Incident Heart Failure in Older Adults: The Cardiovascular Health Study.

OBJECTIVES: The goal of this study was to determine the relative contribution of major lifestyle factors on the development of heart failure (HF) in older adults. BACKGROUND: HF incurs high morbidity, mortality, and health care costs among adults ≥65 years of age, which is the most rapidly growing segment of the U.S. METHODS: We prospectively investigated separate and combined associations of lifestyle risk factors with incident HF (1,380 cases) over 21.5 years among 4,490 men and women in the Cardiovascular Health Study, which is a community-based cohort of older adults. Lifestyle factors included 4 dietary patterns (Alternative Healthy Eating Index, Dietary Approaches to Stop Hypertension, an American Heart Association 2020 dietary goals score, and a Biologic pattern, which was constructed using previous knowledge of cardiovascular disease dietary risk factors), 4 physical activity metrics (exercise intensity, walking pace, energy expended in leisure activity, and walking distance), alcohol intake, smoking, and obesity. RESULTS: No dietary pattern was associated with developing HF (p > 0.05). Walking pace and leisure activity were associated with a 26% and 22% lower risk of HF, respectively (pace >3 mph vs. <2 mph; hazard ratio [HR]: 0.74; 95% confidence interval [CI]: 0.63 to 0.86; leisure activity ≥845 kcal/week vs. <845 kcal/week; HR: 0.78; 95% CI: 0.69 to 0.87). Modest alcohol intake, maintaining a body mass index <30 kg/m(2), and not smoking were also independently associated with a lower risk of HF. Participants with ≥4 healthy lifestyle factors had a 45% (HR: 0.55; 95% CI: 0.42 to 0.74) lower risk of HF. Heterogeneity by age, sex, cardiovascular disease, hypertension medication use, and diabetes was not observed. CONCLUSIONS: Among older U.S. adults, physical activity, modest alcohol intake, avoiding obesity, and not smoking, but not dietary patterns, were associated with a lower risk of HF.Role of the funding source: This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01 HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. Fumiaki Imamura was supported by Medical Research Council Unit Programme number MC_UU_125015/5.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jchf.2015.02.00

Identification of supraventricular tachycardia mechanisms with surface electrocardiograms using a convolutional neural network.

BACKGROUND: It remains difficult to definitively distinguish supraventricular tachycardia (SVT) mechanisms using a 12-lead electrocardiogram (ECG) alone. Machine learning may identify visually imperceptible changes on 12-lead ECGs and may improve ability to determine SVT mechanisms. OBJECTIVE: We sought to develop a convolutional neural network (CNN) that identifies the SVT mechanism according to the gold standard of SVT ablation and to compare CNN performance against experienced electrophysiologists among patients with atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular reciprocating tachycardia (AVRT), and atrial tachycardia (AT). METHODS: All patients with 12-lead surface ECG during sinus rhythm and SVT and had successful SVT ablation from 2013 to 2020 were included. A CNN was trained using data from 1505 surface ECGs that were split into 1287 training and 218 test ECG datasets. We compared the CNN performance against independent adjudication&nbsp;by 2 experienced cardiac electrophysiologists on the test dataset. RESULTS: Our dataset comprised 1505 ECGs (368 AVNRT, 304 AVRT, 95 AT, and 738 sinus rhythm) from 725 patients. The CNN areas under the receiver-operating characteristic curve for AVNRT, AVRT, and AT were 0.909, 0.867, and 0.817, respectively. When fixing the specificity of the CNN to the electrophysiologist adjudicators specificity, the CNN identified all SVT classes with higher sensitivity: (1) AVNRT (91.7% vs 65.9%), (2) AVRT (78.4% vs 63.6%), and (3) AT (61.5% vs 50.0%). CONCLUSION: A CNN can be trained to differentiate SVT mechanisms from surface 12-lead ECGs with high overall performance, achieving similar performance to experienced electrophysiologists at fixed specificities

A global database of food and nutrient consumption

In every region of the world, poor diet is a leading cause of both malnutrition and chronic diseases including diabetes, cardiovascular diseases and specific cancers. In 2013, 38.3 million deaths occurred due to chronic diseases globally (70% of all deaths), with most of these deaths occurring in developing countries. Anecdotal evidence and more formal evaluations in a limited number of countries suggest that changes in traditional eating patterns and a growing reliance on new types of foods are major drivers of these transitions. However, data on global patterns of dietary habits, as well as differences by population characteristics are not well established. An empirical assessment of dietary intakes is needed for evidence-based policy-making to address global health challengespeer-reviewe

A global database of food and nutrient consumption

In every region of the world, poor diet is a leading cause of both malnutrition and chronic diseases including diabetes, cardiovascular diseases and specific cancers. In 2013, 38.3 million deaths occurred due to chronic diseases globally (70% of all deaths), with most of these deaths occurring in developing countries. Anecdotal evidence and more formal evaluations in a limited number of countries suggest that changes in traditional eating patterns and a growing reliance on new types of foods are major drivers of these transitions. However, data on global patterns of dietary habits, as well as differences by population characteristics are not well established. An empirical assessment of dietary intakes is needed for evidence-based policy-making to address global health challengespeer-reviewe