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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

Published online by Cambridge University Press:  08 January 2010

Petra Schnell-Inderst ,
Ruth Schwarzer ,
Alexander Göhler ,
Norma Grandi ,
Kristin Grabein ,
Björn Stollenwerk ,
Jennifer Manne ,
Volker Klauss ,
Uwe Siebert  and
Jürgen Wasem
Petra Schnell-Inderst
Affiliation:
University for Health Sciences, Medical Informatics and Technology and University of Duisburg-Essen
Ruth Schwarzer
Affiliation:
University for Health Sciences, Medical Informatics and Technology
Alexander Göhler
Affiliation:
University for Health Sciences, Medical Informatics and Technology and Massachusetts General Hospital, Harvard Medical School
Norma Grandi
Affiliation:
University for Health Sciences, Medical Informatics and Technology
Kristin Grabein
Affiliation:
University of Duisburg–Essen
Björn Stollenwerk
Affiliation:
Helmholtz Zentrum München and University for Health Sciences, Medical Informatics and Technology
Jennifer Manne
Affiliation:
Harvard School of Public Health
Volker Klauss
Affiliation:
University Hospital of Munich
Uwe Siebert
Affiliation:
University for Health Sciences, Medical Informatics and Technology and Harvard School of Public Health and Massachusetts General Hospital, Harvard Medical School
Jürgen Wasem
Affiliation:
University of Duisburg–Essen
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Abstract

Objectives: The aim of this study was to compare the predictive value, clinical effectiveness, and cost-effectiveness of high-sensitivity C-reactive protein (hs-CRP)-screening in addition to traditional risk factor screening in apparently healthy persons as a means of preventing coronary artery disease.

Methods and Results: The systematic review was performed according to internationally recognized methods. Seven studies on risk prediction, one clinical decision-analytic modeling study, and three decision-analytic cost-effectiveness studies were included. The adjusted relative risk of high hs-CRP-level ranged from 0.7 to 2.47 (p < .05 in four of seven studies). Adding hs-CRP to the prediction models increased the areas under the curve by 0.00 to 0.027. Based on the clinical decision analysis, both individuals with elevated hs-CRP-levels and those with hyperlipidemia have a similar gain in life expectancy following statin therapy. One high-quality economic modeling study suggests favorable incremental cost-effectiveness ratios for persons with elevated hs-CRP and higher risk. However, many model parameters were based on limited evidence.

Conclusions: Adding hs-CRP to traditional risk factors improves risk prediction, but the clinical relevance and cost-effectiveness of this improvement remain unclear.

Keywords

Coronary artery diseaseRisk predictionHigh-sensitivity C-reactive proteinSystematic reviewCost-effectivenessHealth technology assessment
Type
ASSESSMENTS
Information
International Journal of Technology Assessment in Health Care , Volume 26 , Issue 1 , January 2010 , pp. 30 - 39
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

1. Allender, S, Scarborough, P, Peto, V, et al. European cardiovascular disease statistics 2008. 2008 edition. In: Network, EH, ed. Oxford, UK: British Heart Foundation Health Promotion Research Group. Department of Public Health, University of Oxford and Health Economics Research Centre, Department of Public Health, University of Oxford, 2008;1–113. http://www.ehnheart.org/files/statistics%202008%20web-161229A.pdf (accessed September 29, 2008).Google Scholar
2. Altman, D. Systematic reviews of evaluations of prognostic variables. In: Egger, M, Smith, GD, Altman, DG, editor. London: BMJ Publishing Group; 2001.CrossRefGoogle ScholarPubMed
3. Blake, GJ, Ridker, PM, Kuntz, KM. Projected life-expectancy gains with statin therapy for individuals with elevated C-reactive protein levels. J Am Coll Cardiol. 2002;40:49–55.Google Scholar
4. Blake, GJ, Ridker, PM, Kuntz, KM. Potential cost-effectiveness of C-reactive protein screening followed by targeted statin therapy for the primary prevention of cardiovascular disease among patients without overt hyperlipidemia. American Journal of Medicine. 2003;114:485–94.CrossRefGoogle ScholarPubMed
5. Cook, NR. 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 et al., Statistics in Medicine (DOI: 10.1002/sim2929). Stat Med. 2008;27:191–195.Google Scholar
6. Cook, NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115:928–935.Google Scholar
7. Cook, NR, Buring, JE, Ridker, PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med. 2006;145:21–9.CrossRefGoogle ScholarPubMed
8. Danesh, J, Collins, R, Appleby, P, et al. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA. 1998;279:1477–1482.Google Scholar
9. Danesh, J, Wheeler, JG, Hirschfield, GM, et al. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med. 2004;350:1387–97.CrossRefGoogle Scholar
10. Danesh, J, Whincup, P, Walker, M, et al. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. BMJ. 2000;321:199–204.CrossRefGoogle ScholarPubMed
11. Ess, SM, Szucs, TD. Medical-economical aspects of high sensitivity C-reactive protein assay for the prediction of coronary heart disease. An analysis in Germany and Italy. Ital Heart J. 2001;2:181–8.Google Scholar
12. Ess, SM, Szucs, TD. Economical aspects of high-sensitivity C-reactive protein as predictor of coronary heart disease: An analysis in France, Spain and Switzerland. HeartDrug. 2002;2:61–8.Google Scholar
13. Folsom, AR, Chambless, LE, Ballantyne, CM, 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;166:1368–73.CrossRefGoogle ScholarPubMed
14. German Scientific Working Group Technology Assessment for Health Care. “Toolkit” Informationsmaterial für Verfasser von HTA-Berichten. Hannover. Stand 2000. 2000Google Scholar
15. Graham, I, Atar, D, Borch-Johnsen, K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Fourth Joint Task Force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Eur J Cardiovasc Prev Rehabil. 2007;14 (Suppl 2):E1–E40.Google Scholar
16. Greenland, P, Smith, SC Jr, Grundy, SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests. Circulation. 2001;104:1863–1867.CrossRefGoogle ScholarPubMed
17. Harrell, FE. Regression modeling strategies. 1st ed. New York: Springer; 2001.CrossRefGoogle Scholar
18. Hlatky, MA. Expanding the orbit of primary prevention – Moving beyond JUPITER. N Engl J Med. 2008;359:2280–2282.Google Scholar
19. Kaul, A. C-reactive protein for diagnosis and screening of coronary artery disease (Structured abstract). Health Technology Advisory Committee (HTAC). 2002. http://www.crd.york.ac.uk/CRDWeb/ShowRecord.asp?View=Full&ID=32003000446.Google Scholar
20. Klee, G, Jaffa, A, Lakatua, D. Biochemical markers of cardiovascular disease risk (Structured abstract). Institute for Clinical Systems Improvement (ICSI). 2003. http://www.icsi.org/guidelines_and_more/technology_assessment_reports/technology_assessment_reports_-active/biochemical__markers_of_cardiovascular_disease_risk.html.Google Scholar
21. Koenig, W, Lowel, H, Baumert, J, et al. C-reactive protein modulates risk prediction based on the Framingham Score: implications for future risk assessment: results from a large cohort study in southern Germany. Circulation. 2004;109:1349–53.CrossRefGoogle ScholarPubMed
22. Libby, P, Ridker, PM. Inflammation and atherosclerosis: Role of C-reactive protein in risk assessment. Am J Med. 2004;116:9–16.Google Scholar
23. Lloyd-Jones, DM, Liu, K, Tian, L, et al. Narrative review: Assessment of C-reactive protein in risk prediction for cardiovascular disease. Ann Intern Med. 2006;145:35–42.Google Scholar
24. McGeechan, K, Macaskill, P, Irwig, L, et al. Assessing new biomarkers and predictive Models for use in clinical practice. A clinician's guide. Arch Intern Med. 2008;168:2304–2310.CrossRefGoogle ScholarPubMed
25. Myers, GL, Rifai, N, Tracy, RP, et al. CDC/AHA Workshop on markers of inflammation and cardiovascular disease: Application to clinical and public health practice: Report from the Laboratory Science Discussion Group. Circulation. 2004;110:e545–e549.Google Scholar
26. OECD Health Data: Gross product purchasing power parities 2007 http://www.oecd.org/linklist/0,2678,en_2649_34357_2734617_1_1_1_1,00.html (30.4.2007).Google Scholar
27. Olsen, MH, Hansen, TW, Christensen, MK, et al. N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population. Eur Heart J. 2007;28:1374–1381.CrossRefGoogle Scholar
28. Pearson, TA, Mensah, GA, Alexander, RW, et al. Markers of inflammation and cardiovascular disease: Application to clinical and public health practice: A statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107:499–511.Google Scholar
29. Pencina, MJ, D'Agostino, RB Sr, D'Agostino, RB Jr, et al. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172; discussion 207–212.CrossRefGoogle ScholarPubMed
30. Randolph, AG, Guyatt, GH, Calvin, JE, Doig, G, Richardson, WS, Scott, W. Understanding articles describing clinical prediction tools. Crit Care Med. 1998;26: 1603–12.CrossRefGoogle ScholarPubMed
31. Ridker, PM. C-reactive protein and the prediction of cardiovascular events among those at intermediate risk: moving an inflammatory hypothesis toward consensus. J Am Coll Cardiol. 2007;49:2129–2138.Google Scholar
32. Ridker, PM, Danielson, E, Fonseca, FAH, et al. Rosuvastatin to prevent vascular events in men and women with elevated c-reactive protein. N Engl J Med. 2008;359:2195–2207.CrossRefGoogle ScholarPubMed
33. Ridker, PM, Rifai, N, Clearfield, M, et al. Measurement of C-reactive protein for the targeting of statin therapy in the primary prevention of acute coronary events. N Engl J Med. 2001;344:1959–1965.CrossRefGoogle ScholarPubMed
34. Ridker, PM, Rifai, N, Pfeffer, MA, et al. Inflammation, pravastatin, and the risk of coronary events after myocardial infarction in patients with average cholesterol levels. Cholesterol and Recurrent Events (CARE) Investigators. Circulation. 1998;98:839–844.CrossRefGoogle ScholarPubMed
35. Ridker, PM, Rifai, N, Rose, L, et al. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med. 2002;347:1557–65.CrossRefGoogle ScholarPubMed
36. Shah, T, Casas, JP, Cooper, JA, et al. Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts. Int J Epidemiol. 2009;38:217–223.CrossRefGoogle ScholarPubMed
37. Siebert, U. When should decision-analytic modeling be used in the economic evaluation of health care? Eur J Health Econom. 2003;4:143–150.Google Scholar
38. Siebert, U, Behrend, C, Mühlberger, N, Wasem, J, Greiner, W, v.d.Schulenburg, J-M, Welte, R, Leidl, R, editors. Entwicklung eines Kriterienkataloges zur Beschreibung und Bewertung ökonomischer Evaluationsstudien in Deutschland. Baden-Baden: Nomos; 1999.Google Scholar
39. Statistisches Bundesamt Deutschland: Allgemeiner Verbraucherpreisindex 2007 http://www.destatis.de/indicators/d/vpi101ad.htm (30.4.2007).Google Scholar
40. St-Pierre, AC, Cantin, B, Bergeron, J, et al. Inflammatory markers and long-term risk of ischemic heart disease in men A 13-year follow-up of the Quebec Cardiovascular Study. Atherosclerosis. 2005;182:315–21.Google Scholar
41. Van Der Meer, IM, de Maat, MP, Kiliaan, AJ, et al. The value of C-reactive protein in cardiovascular risk prediction: the Rotterdam Study. Arch Intern Med. 2003 163:1323–8.CrossRefGoogle ScholarPubMed
42. Williams, C, Brunskill, S, Altman, D, Briggs, A, Campbell, H, Clarke, M, Glanville, J, Gray, A, Harris, A, Johnston, K, Lodge, M. Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy. Health Technology Assessment 2006;10.Google Scholar
43. Wilson, PW, Nam, BH, Pencina, M, et al. C-reactive protein and risk of cardiovascular disease in men and women from the Framingham Heart Study. Arch Intern Med. 2005 Nov 28;165 (21):2473–8.CrossRefGoogle ScholarPubMed
44. Wilson, PW, Pencina, M, Jacques, P, et al. C-reactive protein and reclassification of cardiovascular risk in the Framingham Heart Study. Circ Cardiovasc Qual Outcomes. 2008;1:92–97.Google Scholar
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