Lisa Tully1, Eugenia Gianos2, Anish Vani1, Yu Guo3, Revathi Balakrishnan1, Arthur Schwartzbard2, James Slater1, Richard Stein2, James Underberg2, Howard Weintraub2, Edward Fisher2, Jeffrey S Berger4. 1. Department of Medicine, Division of Cardiology, New York University School of Medicine, New York, NY. 2. Department of Medicine, Division of Cardiology, New York University School of Medicine, New York, NY; Center for the Prevention of Cardiovascular Disease, Marc and Ruti Bell Program in Vascular Biology, New York University School of Medicine, New York, NY. 3. Department of Population Health, Division of Biostatistics, New York University Langone Medical Center, New York, NY. 4. Department of Medicine, Division of Cardiology, New York University School of Medicine, New York, NY; Center for the Prevention of Cardiovascular Disease, Marc and Ruti Bell Program in Vascular Biology, New York University School of Medicine, New York, NY. Electronic address: jeffrey.berger@nyumc.org.
Abstract
BACKGROUND: The American Heart Association recommends targeting 7 cardiovascular (CV) health metrics to reduce morbidity and mortality. Control of these targets in patients undergoing CV intervention is uncertain. METHODS: We prospectively studied patients undergoing elective percutaneous coronary or peripheral intervention from November 2010 to May 2012. We recorded data on patient demographics, clinical characteristics, and social history. Risk factor control was categorized as ideal, intermediate, or poor according to the 7 American Heart Association-defined CV health metrics (smoking status, body mass index, physical activity, diet, cholesterol, blood pressure, and metabolic control). Linear regression model was used to evaluate the association between baseline characteristics and poor CV health. RESULTS: Among 830 consecutive patients enrolled, mean age is 67.3 ± 10.8 years, 74.2% are male, and 62.1% are white. The adequacy of achievement of ideal CV health is suboptimal in our cohort; the mean number of ideal CV metrics is 2.15 ± 1.06. Less than 1 in 10 (9.7%) met ≥4 ideal CV health metrics. After multivariate analysis, male sex (P = .04), nonwhite race (P = .01), prior coronary artery disease (P < .01), prior peripheral arterial disease (P < .01), and history of depression (P = .01) were significantly associated with poor CV health. CONCLUSIONS: Among patients referred for elective CV intervention, achievement of ideal CV health is poor. Elective interventions represent an opportunity to identify and target CV health for risk factor control and secondary prevention.
BACKGROUND: The American Heart Association recommends targeting 7 cardiovascular (CV) health metrics to reduce morbidity and mortality. Control of these targets in patients undergoing CV intervention is uncertain. METHODS: We prospectively studied patients undergoing elective percutaneous coronary or peripheral intervention from November 2010 to May 2012. We recorded data on patient demographics, clinical characteristics, and social history. Risk factor control was categorized as ideal, intermediate, or poor according to the 7 American Heart Association-defined CV health metrics (smoking status, body mass index, physical activity, diet, cholesterol, blood pressure, and metabolic control). Linear regression model was used to evaluate the association between baseline characteristics and poor CV health. RESULTS: Among 830 consecutive patients enrolled, mean age is 67.3 ± 10.8 years, 74.2% are male, and 62.1% are white. The adequacy of achievement of ideal CV health is suboptimal in our cohort; the mean number of ideal CV metrics is 2.15 ± 1.06. Less than 1 in 10 (9.7%) met ≥4 ideal CV health metrics. After multivariate analysis, male sex (P = .04), nonwhite race (P = .01), prior coronary artery disease (P < .01), prior peripheral arterial disease (P < .01), and history of depression (P = .01) were significantly associated with poor CV health. CONCLUSIONS: Among patients referred for elective CV intervention, achievement of ideal CV health is poor. Elective interventions represent an opportunity to identify and target CV health for risk factor control and secondary prevention.
Authors: Michael S Garshick; Georgeta D Vaidean; Anish Vani; James A Underberg; Jonathan D Newman; Jeffrey S Berger; Edward A Fisher; Eugenia Gianos Journal: Cardiology Date: 2019-05-10 Impact factor: 1.869
Authors: Eugenia Gianos; Antoinette Schoenthaler; Yu Guo; Judy Zhong; Howard Weintraub; Arthur Schwartzbard; James Underberg; Michael Schloss; Jonathan D Newman; Sean Heffron; Edward A Fisher; Jeffrey S Berger Journal: Am Heart J Date: 2018-01-09 Impact factor: 4.749
Authors: Morten Krogh Christiansen; Jesper Møller Jensen; Anders Krogh Brøndberg; Hans Erik Bøtker; Henrik Kjærulf Jensen Journal: Vasc Health Risk Manag Date: 2016-05-25