Secondary tests for stratification of risk for atherosclerosis

References

• Wierzbicki AS. New directions in cardiovascular risk assessment: the role of secondary risk stratification markers. Int J Clin Pract 2012;66:622-30
   PubMed Web of Science ®Google Scholar
• Tunstall-Pedoe H. Cardiovascular risk and risk scores: ASSIGN, Framingham, QRISK and others: how to choose. Heart 2011;97:442-4
   PubMed Web of Science ®Google Scholar
• Sheridan S, Pignone M, Mulrow C. Framingham-based tools to calculate the global risk of coronary heart disease: a systematic review of tools for clinicians. J Gen Intern Med 2003;18:1039-52
   PubMed Web of Science ®Google Scholar
• Hippisley-Cox J, Coupland C, Robson J, et al. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010;341:c6624
   PubMedGoogle Scholar
• Jackson R, Marshall R, Kerr A, et al. QRISK or Framingham for predicting cardiovascular risk? BMJ 2009;339:b2673
   PubMedGoogle Scholar
• Reynolds TM, Twomey P, Wierzbicki AS. Accuracy of cardiovascular risk estimation in patients without diabetes. J Cardiovasc Risk 2002;9:183-90
   PubMedGoogle Scholar
• Marshall T. The effect of blood pressure and cholesterol variability on the precision of Framingham cardiovascular risk estimation: a simulation study. J Hum Hypertens 2010;24:631-8
   PubMed Web of Science ®Google Scholar
• Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012;98:683-90
   PubMed Web of Science ®Google Scholar
• Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012;98:691-8
   PubMed Web of Science ®Google Scholar
• Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models. Biom J 2011;53:237-58
   PubMed Web of Science ®Google Scholar
• Pepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol 2011;173:1327-35
   PubMed Web of Science ®Google Scholar
• Mallett S, Halligan S, Thompson M, et al. Interpreting diagnostic accuracy studies for patient care. BMJ 2012;345:e3999
   PubMed Web of Science ®Google Scholar
• Helfand M, Buckley DI, Freeman M, et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med 2009;151:496-507
   PubMed Web of Science ®Google Scholar
• Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008;118:2243-51
   PubMed Web of Science ®Google Scholar
• Blankenberg S, Zeller T, Saarela O, et al. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project. Circulation 2010;121:2388-97
   PubMed Web of Science ®Google Scholar
• Saunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation 2011;123:1367-76
   PubMed Web of Science ®Google Scholar
• Nolan N, Tee L, Vijayakumar S, et al. Analytical validation of a coronary heart disease risk assessment multi-analyte proteomic test. Expert Opin Med Diag 2013;7:127-36
   PubMedGoogle Scholar
• Hughes MF, Saarela O, Blankenberg S, et al. A multiple biomarker risk score for guiding clinical decisions using a decision curve approach. Eur J Prev Cardiol 2012 Aug;19:874-84
   Google Scholar
• Cross DS, McCarthy CA, Hytopoulos E, et al. Improved coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts. Curr Med Res Opin 2012;28:1819-30
   PubMed Web of Science ®Google Scholar
• D'Agostino RB Sr, Grundy S, Sullivan LM, et al. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:180-7
   PubMed Web of Science ®Google Scholar
• Cai T, Zheng Y. Evaluating prognostic accuracy of biomarkers in nested case–control studies. Biostatistics 2012;13:89-100
   PubMed Web of Science ®Google Scholar
• Wright D, Bradbury I, Malone F, et al. Cross-trimester repeated measures testing for Down's syndrome screening: an assessment. Health Technol Assess 2010;14:1-80
   Web of Science ®Google Scholar
• Kavousi M, Elias-Smale S, Rutten JH, et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med 2012;156:438-44
   PubMed Web of Science ®Google Scholar
• Blankenberg S, McQueen MJ, Smieja M, et al. Comparative impact of multiple biomarkers and N-Terminal pro-brain natriuretic peptide in the context of conventional risk factors for the prediction of recurrent cardiovascular events in the Heart Outcomes Prevention Evaluation (HOPE) Study. Circulation 2006;114:201-8
   PubMed Web of Science ®Google Scholar
• Wannamethee SG, Welsh P, Lowe GD, et al. N-terminal pro-brain natriuretic Peptide is a more useful predictor of cardiovascular disease risk than C-reactive protein in older men with and without pre-existing cardiovascular disease. J Am Coll Cardiol 2011;58:56-64
   PubMed Web of Science ®Google Scholar
• Welsh P, Doolin O, Willeit P, et al. N-terminal pro-B-type natriuretic peptide and the prediction of primary cardiovascular events: results from 15-year follow-up of WOSCOPS. Eur Heart J. 2013 Feb;34:443-50
   Google Scholar
• Tzoulaki I, Siontis KC, Ioannidis JP. Prognostic effect size of cardiovascular biomarkers in datasets from observational studies versus randomised trials: meta-epidemiology study. BMJ 2011;343:d6829
   PubMedGoogle Scholar