Mariam Kashani1, Arn Eliasson, Marina Vernalis, Linda Costa, Mary Terhaar. 1. Mariam Kashani, DNP, CRNP Doctor of Nursing Program (DNP) Student, School of Nursing, Johns Hopkins University, Baltimore; Director, Clinical Operations, Integrative Cardiac Health Project, Walter Reed National Military Medical Center, Bethesda, Maryland. Arn Eliasson, MD Senior Physician Research Consultant, Integrative Cardiac Health Project, Walter Reed National Military Medical Center, Bethesda, Maryland. Marina Vernalis, DO Executive Medical Director, Integrative Cardiac Health Project, Walter Reed National Military Medical Center, Bethesda, Maryland. Linda Costa, PhD, RN Associate Professor, School of Nursing, Johns Hopkins University, Baltimore, Maryland. Mary Terhaar, DNSc, RN Director, DNP Program, and Associate Professor, School of Nursing, Johns Hopkins University, Baltimore, Maryland.
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the number one killer in the United States. Although the causes of CVD are multifactorial, including genetic and environmental influences, it is largely a preventable disease. The cornerstone of CVD prevention is accuracy in risk prediction to identify patients who will benefit from interventions aimed at reducing risk. Nurse practitioners commonly perform CVD risk assessments and are well positioned to impact preventive therapy. Cardiovascular disease risk scoring systems currently in use substantially underestimate risk in large part because these do not include family history of premature CVD as a high-risk factor. PURPOSE: We sought to examine the state of evidence for the use of family history as a predictor in CVD risk stratification. CONCLUSIONS: A comprehensive literature search using the Medical Subject Headings terms of family history of CVD, family history of premature CVD, risk assessment, and risk estimation displayed 416 articles; a review of the titles and subsequent evaluation of the articles eliminated 392 references, leaving 24 for review. By incorporating family history in risk assessment, categorization of CVD risk improves substantially. The evidence demonstrates that family history is an independent contributor to risk appraisal and unequivocally supports its incorporation to improve accuracy in global CVD risk estimation. CLINICAL IMPLICATIONS: Underestimation of CVD risk leaves patients and providers misinformed, promoting the ongoing epidemic of chronic disease. Translating this evidence into practice by establishing a clinical algorithm that incorporates family history into risk prediction will standardize CVD risk assessment, improve the identification of high-risk patients, and provide the indicated aggressive care to prevent CVD.
BACKGROUND:Cardiovascular disease (CVD) is the number one killer in the United States. Although the causes of CVD are multifactorial, including genetic and environmental influences, it is largely a preventable disease. The cornerstone of CVD prevention is accuracy in risk prediction to identify patients who will benefit from interventions aimed at reducing risk. Nurse practitioners commonly perform CVD risk assessments and are well positioned to impact preventive therapy. Cardiovascular disease risk scoring systems currently in use substantially underestimate risk in large part because these do not include family history of premature CVD as a high-risk factor. PURPOSE: We sought to examine the state of evidence for the use of family history as a predictor in CVD risk stratification. CONCLUSIONS: A comprehensive literature search using the Medical Subject Headings terms of family history of CVD, family history of premature CVD, risk assessment, and risk estimation displayed 416 articles; a review of the titles and subsequent evaluation of the articles eliminated 392 references, leaving 24 for review. By incorporating family history in risk assessment, categorization of CVD risk improves substantially. The evidence demonstrates that family history is an independent contributor to risk appraisal and unequivocally supports its incorporation to improve accuracy in global CVD risk estimation. CLINICAL IMPLICATIONS: Underestimation of CVD risk leaves patients and providers misinformed, promoting the ongoing epidemic of chronic disease. Translating this evidence into practice by establishing a clinical algorithm that incorporates family history into risk prediction will standardize CVD risk assessment, improve the identification of high-risk patients, and provide the indicated aggressive care to prevent CVD.
Authors: David C Goff; Donald M Lloyd-Jones; Glen Bennett; Sean Coady; Ralph B D'Agostino; Raymond Gibbons; Philip Greenland; Daniel T Lackland; Daniel Levy; Christopher J O'Donnell; Jennifer G Robinson; J Sanford Schwartz; Susan T Shero; Sidney C Smith; Paul Sorlie; Neil J Stone; Peter W F Wilson; Harmon S Jordan; Lev Nevo; Janusz Wnek; Jeffrey L Anderson; Jonathan L Halperin; Nancy M Albert; Biykem Bozkurt; Ralph G Brindis; Lesley H Curtis; David DeMets; Judith S Hochman; Richard J Kovacs; E Magnus Ohman; Susan J Pressler; Frank W Sellke; Win-Kuang Shen; Sidney C Smith; Gordon F Tomaselli Journal: Circulation Date: 2013-11-12 Impact factor: 29.690
Authors: David C Goff; Donald M Lloyd-Jones; Glen Bennett; Sean Coady; Ralph B D'Agostino; Raymond Gibbons; Philip Greenland; Daniel T Lackland; Daniel Levy; Christopher J O'Donnell; Jennifer G Robinson; J Sanford Schwartz; Susan T Shero; Sidney C Smith; Paul Sorlie; Neil J Stone; Peter W F Wilson Journal: J Am Coll Cardiol Date: 2013-11-12 Impact factor: 24.094
Authors: Pierre Meneton; Cédric Lemogne; Eléonore Herquelot; Sébastien Bonenfant; Martin G Larson; Ramachandran S Vasan; Joël Ménard; Marcel Goldberg; Marie Zins Journal: PLoS One Date: 2016-09-06 Impact factor: 3.240