A multiple biomarker risk score for guiding clinical decisions using a decision curve approach

European Journal of Preventive Cardiology

Volumn 19, Issue 4, 2012, Pages 874-884

  Hughes, Maria F ^a   Saarela, Olli ^b   Blankenberg, Stefan ^c   Zeller, Tanja ^c   Havulinna, Aki S ^b   Kuulasmaa, Kari ^b   Yarnell, John ^a   Schnabel, Renate B ^c   Tiret, Laurence ^d   Salomaa, Veikko ^b   Evans, Alun ^a   Kee, Frank ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b NATIONAL PUBLIC HEALTH INSTITUTE   (Finland)

^c JOHANNES GUTENBERG UNIVERSITY   (Germany)

^d INSERM   (France)

Author keywords

Cardiovascular prediction; clinical utility; decision curve; discrimination statistic; multiple biomarker score

Indexed keywords

AMINO TERMINAL PRO BRAIN NATRIURETIC PEPTIDE; C REACTIVE PROTEIN; CHOLESTEROL; TROPONIN I; BIOLOGICAL MARKER; BRAIN NATRIURETIC PEPTIDE; PEPTIDE FRAGMENT; PRO BRAIN NATRIURETIC PEPTIDE (1 76); PRO-BRAIN NATRIURETIC PEPTIDE (1-76);

ARTICLE; BLOOD PRESSURE; BRAIN ISCHEMIA; CARDIOVASCULAR RISK; CLINICAL DECISION MAKING; FEMALE; HEART DISEASE; HIGH RISK POPULATION; HUMAN; MAJOR CLINICAL STUDY; MALE; PRACTICE GUIDELINE; PREDICTIVE VALUE; PRIORITY JOURNAL; SCORING SYSTEM; ADULT; AGE; BLOOD; CARDIOVASCULAR DISEASE; CLINICAL TRIAL; COMPARATIVE STUDY; DECISION SUPPORT SYSTEM; EUROPE; MIDDLE AGED; MULTICENTER STUDY; PATHOPHYSIOLOGY; PROGNOSIS; PROSPECTIVE STUDY; RISK ASSESSMENT; RISK FACTOR; SEX DIFFERENCE;

ADULT; AGE FACTORS; BIOLOGICAL MARKERS; BLOOD PRESSURE; C-REACTIVE PROTEIN; CARDIOVASCULAR DISEASES; CHOLESTEROL; DECISION SUPPORT TECHNIQUES; EUROPE; FEMALE; HUMANS; MALE; MIDDLE AGED; NATRIURETIC PEPTIDE, BRAIN; PEPTIDE FRAGMENTS; PREDICTIVE VALUE OF TESTS; PROGNOSIS; PROSPECTIVE STUDIES; RISK ASSESSMENT; RISK FACTORS; SEX FACTORS; TROPONIN I;

EID: 84871705284     PISSN: 20474873     EISSN: None     Source Type: Journal    
DOI: 10.1177/1741826711417341     Document Type: Article

References (47)

1. Cook N, Ridker P. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009 ; 150: 795-8021
2. CooperANhereraLCalvertNO'FlynnNTurnbullNRobsonJ. Clinical guidelines and evidence review for lipid modification: cardiovascular risk assessment and the primary and secondary prevention of cardiovascular disease.London: National Collaborating Centre for Primary Care and Royal College of General Practitioners, 2001. http://www.nice.org.uk/nicemedia/live/11982/40742/40742. pdf, (consulted June 2011).
3. 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
4. Assmann G, Cullen P, Schulte H. The Munster Heart Study (PROCAM) a results of follow up at 8 years. Eur Heart J. 1998 ; 19: A2 - A11
5. Empana JP, Duchimetriere P, Arveiler D, Ferrieres J, Evans AE, Ruidavets JB, et al. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. Eur Heart J. 2003 ; 24: 1903-1911
6. Panagiotakos DB, Stvrinos V. Methodological issues in cardiovascular epidemiology; the risk of determining absolute risk through statistical models. Vasc Health Risk Manag. 2006 ; 2: 309-315
7. Koenig W. Update on integrated biomarkers for assessment of long-term risk of cardiovascular complications in initially healthy subjects and patients with manifest atherosclerosis. Ann Med. 2009 ; 41: 332-343
8. Romanens M, Ackermann F, Spence JD, Darioli R, Rodondi N, Corti R, et al. Improvement of cardiovascular risk prediction; time to review current knowledge, debates, and fundamentals on how to assess test characteristics. Eur J Cardio Prev Rehab. 2010 ; 17: 18-23
9. Ridker PM, Paynter NP, Rifai N, Gazino JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008 ; 118: 2243-2251
10. Folsom AR, Chambless LE, Ballantyne CM, Coresh J, Heiss G, Wu KK, et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch Intern Med. 2006 ; 166: 1368-1373
11. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006 ; 355 (25). 2631-2639
12. Kim HC, Greenland P, Rossouw JE, Manson JE, Cochrane BB, Lasser NL, et al. Multimarker prediction of coronary heart disease risk, the Women's Health Initiative. J Am Coll Cardiol. 2010 ; 55: 2080-2091
13. Hughes M, Kee F, Salomaa V. Clinical utility of multiple biomarker panels for cardiovascular disease risk prediction. Curr Cardiovasc Risk Rep. 2011 ; 5: 165-173
14. Persell SD, Zei C, Cameron KA, Zielinski M, Lloyd-Jones DM. Potential use of 10-year and lifetime coronary risk information for preventative cardiology prescribing decisions. Arch Intern Med. 2010 ; 170: 470-476
15. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models, a framework for traditional and novel measures. Epidemiology. 2010 ; 121: 128-138
16. Altman D, Vergouwe Y, Royston P, Moons K. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009 ; 338: 1432-1435
17. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, et al. Criteria for evaluation of novel markers of cardiovascular risk, A scientific statement from the American Heart Association expert panel on subclinical atherosclerotic diseases and emerging risk factors and the stroke council. Circulation. 2010 ; 119: 2408-2416
18. Vickers A, Elkin E. Decision curve analysis: A novel method for evaluating prediction models. Med Decis Making. 2006 ; 26: 565-574
19. Blankenberg S, Zeller T, Saarela O, Havulinna AS, Kee F, Tunstall-Pedoe H, et al. Contribution of thirty biomarkers to ten-year cardiovascular risk prediction in two population cohorts. Circulation. 2010 ; 121 (22). 2388-2397
20. Evans A, Saalomaa V, Kulathinal S, Asplund K, Cambien F, Ferrario M, et al. MORGAM (an international pooling of cardiovascular cohorts). Int J Epidemiol. 2005 ; 34: 21-27
21. KulathinalSNiemeläMKuulasmaaKcontributors from participating centres for the MORGAM project. Description of MORGAM cohorts. National Institute for Health and Welfare and the MORGAM Project investigators, 2005. http:/www.ktl.fi/publications/morgam/cohorts/index.html, URN:NBN:fi-fe20051214 (2005), (consulted May 2011).
22. Vartianen E, Laatikainen T, Peltonen M, Juolevi A, Mannisto S, Sundvall J, et al. Thirty five year trends in cardiovascular risk factors in Finland. Int J Epidemiol. 2010 ; 39: 504-518
23. Yarnell JW. The PRIME study: classical risk factors do not explain the severalfold difference in risk of coronary heart disease between France and Northern Ireland. Prospective Epidemiological Study of Myocardial Infarction. QJM. 1998 ; 91: 667-676
24. Rubin DB Multiple Imputation for Nonresponse in Surveys. New York: Wiley ; 1987 :
25. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS a Bayesian modelling framework: concepts, structure and extensibility. Statistics and Computing. 2000 ; 10 (4). 325-337
26. Moons KGM, Donders RART, Stijnen T, Harrell FE. Using the outcome for imputation of missing predictor values was preferred. J. Clin. Epidemiol. 2006 ; 59: 1092-1101
27. Kalbfleisch JD, Prentice RL The Statistical Analysis of Failure Time Data. 2 nd ed. New York: Wiley, 2002, pp. 252-252.
28. Vickers AJ, Cronin AM, Elkin E, Gonen M. Extension to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. Med Inform Decis Making. 2008 ; 53 (8). 1-17
29. Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemas MJ, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001 ; 54: 774-781
30. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2010. http://www.r-project.org (2010, consulted October 2010).
31. Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G, Engstrom G, et al. Novel and conventional biomarkers for the prediction of incident cardiovascular events in the community. JAMA. 2009 ; 302 (1). 49-57
32. Wang TJ. Multiple biomarkers for predicting cardiovascular events, lessons learned (editorial comment). J. Am Col Cardiol. 2010 ; 55 (19). 2092-2095
33. de Lemos JA, Rohatgi A. Separating the contenders from the pretenders, competitive high throughput biomarker screening in large population-based studies (editorial). Circulation. 2010 ; 121 (22). 2381-2383
34. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007 ; 115: 928-935
35. Zethelius B, Berglund L, Sundström J, Ingelsson E, Basu S, Larsson A, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med. 2008 ; 358 (20). 2107-2116
36. Ridker PM, Danielson E, Fonseca FAH, Genest J, Gotto AM, Kastelein JJP, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008 ; 359: 2195-2207
37. de Lorgeril M, Salen P, Abramson J, Dodin S, Hamazaki T, Kostucki W, et al. Cholesterol lowering, cardiovascular diseases, and the Rosuvastatin-JUPITER controversy, a critical appraisal. Arch Intern Med. 2010 ; 170 (12). 1032-1036
38. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010 ; 375: 132-140
39. Salomaa V, Havulinna A, Saarela O, Zeller T, Jousilahti P, Jula A, et al. Thirty-one novel biomarkers as predictors for clinically incident diabetes. PloS One. 2010 ; 5: 1-8
40. Lee KK, Cipriano LE, Owens DK, Go AS, Hlatky MA. Cost effectiveness of using high sensitivity C-reactive protein to identify intermediate and low cardiovascular risk individuals for statin therapy. Circulation. 2010 ; 122: 1478-1487
41. Saatar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJM, et al. Statins and risk of incident diabetes: A collaborative meta-analysis of randomised statin trials. Lancet. 2010 ; 373: 735-742
42. Karp I, Behlouli H, Lelorier J, Pilote L. Statins and cancer risk. Am J Med. 2008 ; 121: 302-309
43. Schnell-Inderst P, Schwarzer R, Gohler A, Grandi N, Grobein K, Stollenwerk B, et al. Prognostic value, clinical effectiveness, and cost-effectiveness of high sensitivity C-reactive protein as a marker for major cardiac events in asymptomatic individuals: A health technology assessment report. Int J Tech Ass Health Care. 2010 ; 26: 30-39
44. Ridker PM, Cannon CP, Morrow D, Rifai N, Rose LM, McCabe CH, et al. C-reactive protein levels and outcomes after statin therapy. N Eng J Med. 2005 ; 352: 20-28
45. Ridker PM, Rifai N, Clearfield M, Downs JR, Weis SE, Miles JS, et al. Measurement of C-reactive protein for the targeting of statin therapy in the primary prevention of acute coronary events. N Eng J. Med. 2001 ; 344: 1959-1965
46. McMurray JJ, Kjekshus J, Gullestad L, Dunselman P, Hjmarson A, Wedel H, et al. Effects of statin therapy according to plasma high sensitivity C-reactive protein concentration in the Controlled Rosuvastatin Mutlinational Trial in Heart Failure (CORONA); a retrospective analysis. Circulation. 2009 ; 120: 2188-2196
47. Saunders JT, Nambi V, de Lemos JA, Chambless LE, Virani SS, Boerwinkle E, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in the Communities Study. Circulation. 2011 ; 123: 1367-1376