Portal de Periódicos da CAPES

Problems on the Pathway From Risk Assessment to Risk Reduction

1998; Lippincott Williams & Wilkins; Volume: 97; Issue: 18 Linguagem: Inglês

10.1161/01.cir.97.18.1761

1524-4539

Philip Greenland, Scott M. Grundy, Richard C. Pasternak, Claude Lenfant,

Risk and Safety Analysis

HomeCirculationVol. 97, No. 18Problems on the Pathway From Risk Assessment to Risk Reduction Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBProblems on the Pathway From Risk Assessment to Risk Reduction Philip Greenland, Scott Grundy, Richard C. Pasternak and Claude Lenfant Philip GreenlandPhilip Greenland From the Department of Preventive Medicine, Northwestern University Medical School (P.G.), Chicago, Ill; Center for Human Nutrition, University of Texas Southwestern Medical Center at Dallas (S.G.); Cardiovascular Division, Departments of Medicine, Cardiac Unit, Massachusetts General Hospital (R.C.P.), Boston, Mass; and National Heart, Lung, and Blood Institute (C.L.), Bethesda, Md. , Scott GrundyScott Grundy From the Department of Preventive Medicine, Northwestern University Medical School (P.G.), Chicago, Ill; Center for Human Nutrition, University of Texas Southwestern Medical Center at Dallas (S.G.); Cardiovascular Division, Departments of Medicine, Cardiac Unit, Massachusetts General Hospital (R.C.P.), Boston, Mass; and National Heart, Lung, and Blood Institute (C.L.), Bethesda, Md. , Richard C. PasternakRichard C. Pasternak From the Department of Preventive Medicine, Northwestern University Medical School (P.G.), Chicago, Ill; Center for Human Nutrition, University of Texas Southwestern Medical Center at Dallas (S.G.); Cardiovascular Division, Departments of Medicine, Cardiac Unit, Massachusetts General Hospital (R.C.P.), Boston, Mass; and National Heart, Lung, and Blood Institute (C.L.), Bethesda, Md. and Claude LenfantClaude Lenfant From the Department of Preventive Medicine, Northwestern University Medical School (P.G.), Chicago, Ill; Center for Human Nutrition, University of Texas Southwestern Medical Center at Dallas (S.G.); Cardiovascular Division, Departments of Medicine, Cardiac Unit, Massachusetts General Hospital (R.C.P.), Boston, Mass; and National Heart, Lung, and Blood Institute (C.L.), Bethesda, Md. Originally published12 May 1998https://doi.org/10.1161/01.CIR.97.18.1761Circulation. 1998;97:1761–1762Assessment of risk and reduction of risk are well-accepted responsibilities of the physician. The pathway from assessment of risk to reduction of risk basically involves three steps: (1) measurement of risk factors and collection of clinical data relevant to patient risk; (2) interpretation of risk-related data with estimation of risk in absolute terms (eg, risk of an event per year) as well as relative terms (ie, low, intermediate, or high compared with others of the same age and sex); and (3) on the basis of risk estimation results, intervention to minimize disease risk or to prevent risk factor development in the future. Although the process seems reasonably straightforward, problems occur at each step that weaken the link between risk assessment and risk reduction. Such problems occur in assessment of CVD risk estimation and reduction just as in most other areas of medical practice in spite of the availability of excellent data relating to CVD risk estimation from the Framingham Heart Study and other similar data sets.1Periodic measurement of CVD risk factors in healthy people (step 1) is routinely recommended by the AHA, the ACC, and the NHLBI, in addition to other authorities on disease prevention.234 Blood lipid measurements, blood pressure readings, age, sex, cigarette smoking and diabetic status, ECG findings, and other risk predictors can be recorded or measured in the office setting and can be entered into risk assessment algorithms.15 Unfortunately, studies show that physicians frequently fail to collect these simple and well-accepted data elements in the course of usual medical care.6 Even such basic clinical assessments as the blood cholesterol level, blood pressure, and cigarette smoking status are not routinely obtained,6 and lack of such data obviously impedes risk assessment.Interpretation of risk factor data (step 2) also poses problems. Over the past three decades, cardiovascular risk profiles and risk instruments have been available from long-term observational studies.5 Such tools were expected to improve the ease and quality of risk-related medical decisions by making risk assessment more quantitative. Indeed, a 1976 Framingham Heart Study publication7 stated: "The 10 percent of persons identified with the use of this [risk assessment] function as at highest risk accounted for about one fifth of the 8 year incidence of coronary heart disease and about one third of the 8 year incidence of atherothrombotic brain infarction, hypertensive heart disease and intermittent claudication. Hence, the function provides an economic and efficient method of identifying persons at high cardiovascular risk who need preventive treatment and persons at low risk who need not be alarmed about one moderately elevated risk characteristic."In this issue of Circulation, an updated version of the Framingham Risk Prediction Score and a new clinically usable instrument are published1 along with an extensive commentary from the AHA Task Force on Risk Reduction.8 The new Framingham Risk Score uses recently adopted clinical cut points from NCEP2 and from the JNC-VI.3 The commentary8 revisits the concept that risk scores such as those from Framingham should prove useful to healthcare professionals engaged in risk factor reductions for individual patients and might assist in selection of specific therapies.The goal of targeting risk-reducing treatments to persons most likely to benefit is an appropriate one. Promotion of this goal was the focus of the ACC's 27th Bethesda Conference: matching the intensity of risk factor management with the hazard for coronary disease events.9 An advanced risk prediction instrument was prepared as a part of that report, but there is little evidence that it has received attention outside of the conference. Despite the availability of risk estimation tools such as those developed over many years from Framingham, physicians cannot readily interpret the results (step 2 of the process). Risk scores can be based on coronary morbidity as well as mortality, and there is no general agreement which to use or whether to use a combination of the two. Newer risk factors, such as lipoprotein(a), homocysteine, LDL particle size, and thrombotic markers, are not included in the available risk equations. It is uncertain whether such new markers would improve risk assessment and treatment selection. Risk scores, including the Framingham Score, may or may not be accurate risk predictors in populations that differ from those in which the original data were collected, eg, lower-risk populations such as Asians or Latin Americans. Some risk assessment tools, including the Framingham Risk Score, find no increase in coronary heart disease risk for women with increasing age from 55 to 75 years, a finding that is not in accord with other epidemiological data sets.1011 Thus, risk interpretation is also problematic.The third step in risk reduction is intervention, and problems exist here as well. Survey data show that physicians frequently fail to achieve target levels for optimal risk reduction of hypercholesterolemic or hypertensive patients.1213 Even when the risk factors have been measured properly, patients have been diagnosed appropriately, and interventions have been initiated, problems with patient compliance or lack of appropriate follow-up can lead to inadequate risk factor control. It is not surprising that multiple risk factors pose a particular challenge in clinical care when single risk factors are often not treated to target levels that are well accepted and widely disseminated, such as those of the NCEP and JNC-VI.Because they are aware of the many problems on the pathway from risk assessment to risk reduction, the AHA, ACC, and NHLBI plan to undertake a review of available cardiovascular risk assessment methods to advise clinicians how to improve risk assessment and risk reduction processes in clinical practice. Data such as those from Framingham1 will be exceedingly important in informing the deliberations of these three policy-forming groups. At least three steps are necessary before any risk assessment tool can achieve widespread use: (1) development of a tool that incorporates all or most measures of risk that are already widely available; (2) validation of the usefulness of such a tool in clinical practices; and (3) discovery of ways to improve risk measurement and incorporate risk assessment more readily into a busy clinician's daily routine. It is hoped that risk assessment tools such as the Framingham Risk Score can soon be made widely available, will be clinically confirmed as accurate, will be found by clinicians to be acceptable and convenient to use, and will ultimately improve the quality of patient care.Selected Abbreviations and AcronymsACC=American College of CardiologyAHA=American Heart AssociationCVD=cardiovascular diseaseJNC-VI=Joint National Committee on Hypertension Detection, Treatment, and ControlNCEP=National Cholesterol Education ProgramNHLBI=National Heart, Lung, and Blood InstituteThe opinions expressed in this editorial are not necessarily those of the editors or of the American Heart Association.(Circulation. 1998;97:1761-1762.)FootnotesCorrespondence to Philip Greenland, MD, Department of Preventive Medicine, Northwestern University Medical School, 680 N Lake Shore Dr, Suite 1102, Chicago, IL 60611. E-mail [email protected] References 1 Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation.1998; 97:1837–1847.CrossrefMedlineGoogle Scholar2 Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Summary of the second report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel II). JAMA.1993; 269:3015–3023.CrossrefMedlineGoogle Scholar3 Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Arch Intern Med.1997; 157:2413–2446.CrossrefMedlineGoogle Scholar4 Report of the US Preventive Services Task Force. Guide to Clinical Preventive Services. 2nd ed. Baltimore, Md: Williams & Wilkins; 1996.Google Scholar5 Anderson KM, Odell PM, Wilson PWF, Kannel WB. Cardiovascular disease risk profiles. Am Heart J.1991; 121:293–298.CrossrefMedlineGoogle Scholar6 Kottke TE, Solberg LI, Brekke ML, Cabrera A, Marquez MA. Delivery rates for preventive services in 44 Midwestern clinics. Mayo Clin Proc.1997; 72:515–523.CrossrefMedlineGoogle Scholar7 Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol.1976; 38:46–51.CrossrefMedlineGoogle Scholar8 Task Force on Risk Reduction of the American Heart Association. Primary prevention of coronary heart disease: guidance from Framingham. Circulation.1998; 97:1876–1887.CrossrefMedlineGoogle Scholar9 27th Bethesda Conference. Matching the intensity of risk factor management with the hazard for coronary disease events. J Am Coll Cardiol.1996; 27:957–1047.MedlineGoogle Scholar10 Simon A, Megnien JJ, Levenson J. Coronary risk estimation and treatment of hypercholesterolemia. Circulation.1997; 96:2449–2452.CrossrefMedlineGoogle Scholar11 Lowe L, Greenland P, Ruth KJ, Dyer AR, Stamler R, Stamler J. Impact of major cardiovascular disease risk factors, particularly in combination, on 22-year mortality in women and men. Arch Intern Med. In press.Google Scholar12 Schucker B, Wittes JT, Santanello NC, Weber SJ, McGoldrick D, Donato K, Levy A, Rifkind BM. Change in cholesterol awareness and action: results from national physician and public surveys. Arch Intern Med.1991; 151:666–673.CrossrefMedlineGoogle Scholar13 Burt VL, Cutler JA, Higgins M, Horan MJ, Labarthe D, Whelton P, Brown C, Roccella EJ. Trends in the prevalence, awareness, treatment, and control of hypertension in the adult US population: data from the health examination surveys, 1960 to 1991. Hypertension.1995; 26:60–69.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Espíndola-Fernández D, Aristizábal Ocampo D, Gallo-Villegas J, Mesa Vieira C, Zuluaga Caicedo N and Múnera Palacio M (2017) Efectos tempranos de un programa integral de prevención cardiovascular guiado por el riesgo de aterosclerosis sobre la edad vascular, Revista Colombiana de Cardiología, 10.1016/j.rccar.2017.01.006, 24:5, (488-495), Online publication date: 1-Sep-2017. Greenland P (2007) Comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond' by M. J. Pencina, R. B. D'Agostino Sr, R. B. D'Agostino Jr, R. S. Vasan,Statistics in Medicine (DOI: 10.1002/sim.2929), Statistics in Medicine, 10.1002/sim.2976, 27:2, (188-190), Online publication date: 30-Jan-2008. Chung C, Avalos I, Raggi P and Stein C (2007) Atherosclerosis and inflammation: insights from rheumatoid arthritis, Clinical Rheumatology, 10.1007/s10067-007-0548-7, 26:8, (1228-1233), Online publication date: 1-Aug-2007. Filippella M, Lillaz E, Ciccarelli A, Giardina S, Massimetti E, Navaretta F, Antico A, Veronesi M, Lombardi G, Colao A, Ghigo E, Benso A and Doveri G (2014) Ankle brachial pressure index usefulness as predictor factor for coronary heart disease in diabetic patients, Journal of Endocrinological Investigation, 10.1007/BF03350808, 30:9, (721-725), Online publication date: 1-Oct-2007. Jovičić S, Ignjatović S and Majkić-Singh N Comparison of Two Different Methods for Cardiovascular Risk Assessment: Framingham Risk Score and Score System, Journal of Medical Biochemistry, 10.2478/v10011-007-0012-x, 26:2, (94-97) Frolkis J Innovative Models for the Delivery Preventive Cardiovascular Care Preventive Cardiology, 10.1007/978-1-59745-096-6_17, (325-338) Hâncu N and Cerghizan A (2003) Global Approach to Cardiovascular Risk in Type 2 Diabetic Persons Cardiovascular Risk in Type 2 Diabetes Mellitus, 10.1007/978-3-642-59352-9_21, (240-276), . Pasternak R, Abrams J, Greenland P, Smaha L, Wilson P and Houston-Miller N (2003) Task force #1—identification of coronary heart disease risk: is there a detection gap?, Journal of the American College of Cardiology, 10.1016/S0735-1097(03)00358-9, 41:11, (1863-1874), Online publication date: 1-Jun-2003. O'Donnell C and Kannel W (2002) Epidemiology of Atherosclerotic Vascular Disease Pan Vascular Medicine, 10.1007/978-3-642-56225-9_1, (3-16), . Gleichmann U (2002) Gesundheitspolitische Aspekte und Ausblick 75 Jahre, 10.1007/978-3-642-18236-5_14, (435-455), . Pasternak R (2002) 2001 National Cholesterol Education Program (NCEP) Guidelines on the Detection, Evaluation and Treatment of Elevated Cholesterol in Adults: Adult Treatment Panel III (ATP III), ACC Current Journal Review, 10.1016/S1062-1458(02)00670-0, 11:4, (37-45), Online publication date: 1-Jul-2002. Sonmez K, Akcay A, Akcakoyun M, Demir D, Elonu O, Pala S, Duran N, Gencbay M, Degertekin M and Turan F (2002) , Journal of Cardiovascular Risk, 10.1097/00043798-200208000-00004, 9:4, (199-205), Online publication date: 1-Aug-2002. SUKA M, SUGIMORI H and YOSHIDA K (2001) Application of the Updated Framingham Risk Score to Japanese Men., Hypertension Research, 10.1291/hypres.24.685, 24:6, (685-689), . McClaran J, Kaufman D, Toombs M, Beardall S, Levy I and Chockalingam A (2001) From Death and Disability to Patient Empowerment: An Interprofessional Partnership to Achieve Cardiovascular Health in Canada, Canadian Journal of Public Health, 10.1007/BF03404970, 92:4, (I3-I9), Online publication date: 1-Jul-2001. Souza E, Leite N, Radominski R, Rodriguez-Añez C, Correia M and Omeiri S (2000) Reabilitação cardiovascular: custo-benefício, Revista Brasileira de Medicina do Esporte, 10.1590/S1517-86922000000400006, 6:4, (145-154), Online publication date: 1-Aug-2000. L'Italien G, Ford I, Norrie J, LaPuerta P, Ehreth J, Jackson J and Shepherd J (2000) The cardiovascular event reduction tool (CERT)—a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS), The American Journal of Cardiology, 10.1016/S0002-9149(99)00847-4, 85:6, (720-724), Online publication date: 1-Mar-2000. Pasternak R (1999) Adjusting therapy to cardiovascular risk status, The American Journal of Medicine, 10.1016/S0002-9343(99)00151-5, 107:2, (31-33), Online publication date: 1-Aug-1999. Liao Y, McGee D, Cooper R and Sutkowski M (1999) How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts, American Heart Journal, 10.1016/S0002-8703(99)70407-2, 137:5, (837-845), Online publication date: 1-May-1999. Liesbon P and Amsterdam E (1999) Prevention of coronary heart disease Part I. Primary prevention, Disease-a-Month, 10.1016/S0011-5029(99)90016-0, 45:12, (497-571), Online publication date: 1-Dec-1999. Mosca L (1999) Absolute risk assessment in the clinical setting, The American Journal of Medicine, 10.1016/S0002-9343(99)00135-7, 107:2, (7-9), Online publication date: 1-Aug-1999. Liao Y, McGee D and Cooper R (1999) Prediction of coronary heart disease mortality in blacks and whites: pooled data from two national cohorts, The American Journal of Cardiology, 10.1016/S0002-9149(99)00187-3, 84:1, (31-36), Online publication date: 1-Jul-1999. May 12, 1998Vol 97, Issue 18 Advertisement Article InformationMetrics Copyright © 1998 by American Heart Associationhttps://doi.org/10.1161/01.CIR.97.18.1761 Originally publishedMay 12, 1998 Keywordsrisk factorsEditorialscoronary diseasePDF download Advertisement