Background: Current guidelines recommend that decisions to start preventative therapy for cardiovascular disease (CVD) should be based on absolute risk; however, current risk equations are based on single measurements of risk factors. We aimed to assess whether two measurements of blood pressure and lipids improves the prediction of cardiovascular risk compared to one measurement.
Methods and results: We used sex-specific Cox proportional hazards models to evaluate the risk of first CVD event in 2385 participants of the Framingham Offspring Study attending both the second and third visits. We estimated the effects on risk prediction of using the average of two measurements of blood pressure, total cholesterol, and HDL cholesterol compared to using one measurement of the risk factors. We found that these risk factors were each markedly more predictive of CVD when the average of two measurements was used rather than one measurement and age was less predictive of CVD. There were small improvements in the overall model fit, discrimination, and calibration. Reclassification also showed small improvements across the risk spectrum (net reclassification information, NRI, for women 3.0%, 95% CI −0.9 to 24.8%; NRI for men 4.0%, 95% CI −2.2 to 14.1%) and possibly greater improvements for intermediate-risk individuals (NRI for women 32.3%, 95% CI −21.9 to 46.8%; NRI for men 16.0%, 95% CI −3.3 to 43%).
Conclusions: Averaging two measurements of blood pressure and lipids results in marked increases in the predictiveness of these risk factors and smaller improvements in the overall prediction of cardiovascular risk including reclassification.
National Vascular Disease Prevention Alliance. Guidelines for the assessment of absolute cardiovascular disease risk. 2009.
2.
New Zealand Guideline Group. The assessment and management of cardiovascular risk. Available at: www.nzgg.org.nz [2003, consulted March 2009].
3.
Task Force. European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Eur J Cardiovasc Prev Rehab2007; 14(Supplement 2): E1–E40.
4.
Joint British Societies. JBS 2: Joint British Societies’ guidelines on prevention of cardiovascular disease in clinical practice. Heart2005; 91(Suppl V): v1–v52.
5.
D'AgostinoRBVasanRSPencinaMJWolfPACobainMMassaroJM. General cardiovascular risk profile for use in primary care The Framingham Heart Study. Circulation2008; 117(6): 743–753.
6.
JacksonRLawesCBennetDMilneRRodersA. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk. Lancet2005; 365(9457): 434–441.
7.
BellKJLIrwigLCraigJCMacaskillP. Use of randomized trials to decide when to monitor response to new treatment. BMJ2008; 336(7640): 361–365.
8.
BellKJLHayenAMacaskillPCraigJCNealBCIrwigL. Mixed models showed no need for initial response monitoring after starting anti-hypertensive therapy. J Clin Epidemiol2009; 62(6): 650–659.
9.
GlasziouPPIrwigLHeritierSSimesJTonkinA. the LIPID Study Investigators.. Monitoring cholesterol levels: measurement error or true change?Ann Intern Med2008; 148(9): 656–661.
10.
ReynoldsTMTwomeyPWierzbickiAS. Accuracy of cardiovascular risk estimation for primary prevention in patients without diabetes. J Cardiovasc Risk2002; 9(4): 183–190.
11.
Hippisley-CoxJCouplandCVinogradovaYRobsonJMayMBrindleP. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ2007; 335(7611): 136–136.
12.
WoodwardMBrindlePTunstall-PedoeH. Sign Group on Risk Estimation. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart2007; 93(2): 172–176.
13.
MacMahonSPetoRCollinsRGodwinJCutlerJSorlieP. Blood pressure, stroke, and coronary heart disease: Part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet1990; 335(8692): 765–774.
14.
VasanRSMassaroJMWilsonPWFSeshadriSWolfPALevyD. Antecedent blood pressure and risk of cardiovascular disease.The Framingham Heart Study. Circulation2002; 105(1): 48–53.
15.
RothwellPM. Limitations of the usual blood-pressure hypothesis and importance of variability, instability, and episodic hypertension. Lancet2010; 375(9718): 938–948.
16.
RothwellPMHowardSCDolanEO'BrienEDobsonJEDahlöfB. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet2010; 375(9718): 895–905.
17.
CupplesLAD'AgostinoRBSection 34: some risk factors related to the annual incidence of cardiovascular disease and death in pooled repeated biennial measurements. In: KannelWBWolfPAGarrisonRJ (eds). Framingham Heart Study: 30 year follow-up, Bethesda: US Department of Health and Human Services, 1987.
18.
McGeechanKMacaskillPIrwigLLiewGWongTY. Assessing new biomarkers and predictive models for use in clinical practice. Arch Intern Med2008; 168(21): 2304–2310.
19.
McGeechanKLiewGMacaskillPIrwigLKleinRKleinBEK. Prediction of incident stroke events based on retinal vessel caliber: a systematic review and individual-participant meta-analysis. Am J Epidemiol2009; 170(11): 1323–1332.
20.
MelanderONewton-ChehCAlmegrenPHedbladBBerglundGEngstromG. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA2009; 302(1): 49–57.
21.
WilsonPWPencinaMJacquesPSelhubJD'AgostinoRSO'DonnellCJ. C-Reactive Protein and reclassification of cardiovascular risk in the Framingham Heart Study. Circ Cardiovasc Qual Outcomes2008; 1(2): 92–97.
22.
PolonskyTMcClellandRTJorgensenNWBildDEBurkeGLGuerciAD. Coronary Artery Calcium Score and risk classification for coronary heart disease prediction. JAMA2010; 303(16): 1610–1616.
23.
KimPKEinsteinAJde GonzalezAB. Coronary Artery Calcification Screening. Estimated radiation dose and cancer risk. Arch Intern Med2009; 169(13): 1188–1194.
24.
DiamondGAKaulS. The things to come of SHAPE: cost and effectiveness of cardiovascular prevention. Am J Cardiol2007; 99(7): 1013–1015.
25.
IoannidisJPATzoulakiI. What makes a good predictor?: The evidence applied to Coronary Artery Calcium Score. JAMA2010; 303(16): 1646–1647.
26.
MarshallT. Measuring blood pressure: the importance of understanding variation. Rev Bras Hipertens2005; 12(2): 75–82.
27.
KeenanKHayenANealBIrwigL. Long term monitoring in patients receiving treatment to lower blood pressure: analysis of data from placebo controlled randomised controlled trial. BMJ2009; 338: b1492–b1492.
28.
BellKJLHayenAMacaskillPCraigJCNealBCFoxKM. Monitoring initial response to angiotensin converting enzyme inhibitor based regimens: an individual patient data meta-analysis from randomised placebo controlled trials. Hypertension2010; 56(3): 533–539.
29.
TakahashiOGlasziouPPPereraRShimboTSuwaJHiramatsuS. Lipid re-screening: what is the best measure and interval?Heart2010; 96: 448–452.
30.
National Heart Foundation of Australia (National Blood Pressure and Vascular Disease Advisory Committee). Guide to management of hypertension 2008. Updated August 2009. Web version. 2009.
31.
Chobanian AV, Bakris GR, Black HL, Cushman WC, Green LA, Izzo Jr JL, Jones DW, Materson BJ, Oparil S, Wright Jr JT, Roccella EJ, the National High Blood Pressure Education Program Coordinating Committee. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42(6): 1206–1252.
32.
Task Force for the Management of Arterial Hypertension of the ESH and of the ESC. 2007 Guidelines for the Management of Arterial Hypertension. Journal of Hypertension 2007; 25(9): 1105–1187.
