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Family history and the risk of coronary heart disease: Comparing predictive models

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Abstract

Family history is commonly used when evaluating coronary heart disease (CHD) risk yet it is usually treated as a simple binary variable according to the occurrence or non-occurrence of disease. This definition however fails to consider the potential components of a family history which may in fact exert different degrees of influence on the overall risk profile. The purpose of this paper is to compare different predictive models for CHD which incorporate family history as either a binary variable or different types of family risk indices in terms of their predictive ability. Models for estimating CHD risk were constructed based on usual risk factors and different family history variables. This construction was accomplished using logistic regression and RECursive Partition and AMalgamation (RECPAM) trees. Our analyses demonstrate the importance of using more sophisticated definitions of family history variables compared to a simple binary approach since this leads to a significant improvement in the predictive ability of CHD risk models.

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References

  1. MacDonald S, Joffres MR, Stachenko S, Horlick L, Fodor G. Multiple cardiovascular disease risk factors in Canadian adults. CMAJ 1992; 146(11): 2021–2029.

    Google Scholar 

  2. Gomel M, Oldenburg B, Simpson JM, Owen N. Worksite cardiovascular risk reduction: A randomized trial of health risk assessment, education, counselling, and incentives. Am J Public Health 1993; 83(9): 1231–1238.

    Google Scholar 

  3. Anderson KM, Odell P, Wilson P, Kannel W. Cardiovascular disease risk profiles. Am Heart J 1991; 121(1): 293–298.

    Google Scholar 

  4. Wolf PA, D'Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: A risk pro.le from the Framingham study. Stroke 1991; 22(3): 312–318.

    Google Scholar 

  5. Schildkraut JM, Myers RH, Cupples LA, Kiely DK, Kannel WB. Coronary risk associated with age and sex of parental heart disease in the Framingham study. Am J Cardiol 1989; 64(10): 555–559.

    Google Scholar 

  6. Myers RH, Kiely DK, Cupples LA, Kannel WB. Parental history is an independent risk factor for coronary artery disease: The Framingham study. Am Heart J 1990; 120(4): 963–969.

    Google Scholar 

  7. Anderson KM, Wilson PWF, Odell PM, Kannel WB. An updated coronary risk profile. Circulation 1991; 83(1): 356–362.

    Google Scholar 

  8. Khoury MJ, James LM. Population and familial relative risks of disease associated with environmental factors in the presence of gene-environmental interaction. Am J Epidemiol 1993; 137(11): 1241–1250.

    Google Scholar 

  9. Jousilahti P, Puska P, Vartiainen E, Pekkanen J, Tuomilehto J. Parental history of premature heart disease: An independent risk factor of myocardial infarction. J Clin Epidemiol 1996; 49(5): 497–503.

    Google Scholar 

  10. Ciruzzi M, Schargrodsky H, Rozlosnik J, et al. Frequency of family history of acute myocardial infarction in patients with acute myocardial infarction. Am J Cardiol 1997; 80(2): 122–127.

    Google Scholar 

  11. Sesso HD, Lee IM, Gaziano JM, Rexrode KM, Glynn RJ, Buring JE. Maternal and paternal history of myocardial infarction and risk of cardiovascular disease in men and women. Circulation 2001; 104: 393–398.

    Google Scholar 

  12. Eaton CB, Bostom AG, Yanek L, et al. Family history and premature coronary heart disease. J Am Board Fam Pract 1996; 9(5): 312–318.

    Google Scholar 

  13. Hunt SC, Williams RR, Barlow GK. A comparison of positive family history definitions for defining risk of future disease. J Chronic Dis 1986; 39(10): 809–821.

    Google Scholar 

  14. Pankow JS, Folsom AR, Province MA, et al. Family history of coronary heart disease and hemostatic variables in middle-aged adults. Thromb Haemost 1997; 77(1): 87–93.

    Google Scholar 

  15. Schwartz AG, Boehmke M, Moll PP. Family risk index as a measure of familial heterogeneity of cancer risk. Am J Epidemiol 1988; 128(3): 524–535.

    Google Scholar 

  16. Santé Québec, Daveluy C, Chénard L, Levasseur M, Émond A. Et votre coeur, ça va? Rapport de l'enquête québécoise sur la santé cardiovasculaire 1990, Montré al, ministè re de la santéet des services sociaux, gouvernement du québec, 1994.

  17. Rose GA. The diagnosis of ischaemic heart pain and intermittent claudication in field surveys. Bull World Health Organ 1962; 27: 645–658.

    Google Scholar 

  18. Diamond GA. A clinically relevant classi.cation of chest discomfort. J Am Coll Cardiol 1983; 1: 574–575.

    Google Scholar 

  19. Lampe PC, Walker M, Lennon LT, Whincup PH, Ebrahim S. Validity of a self-reported history of doctordiagnosed angina. J Clin Epidemiol 1999; 52(1): 73–81.

    Google Scholar 

  20. Bergmann MM, Byers T, Freedman DS, Mokdad A. Validity of self-reported diagnoses leading to hospitalization: A comparison of self-reports with hospital records in a prospective study of American adults. Am J Epidemiol 1998; 147(10): 969–977.

    Google Scholar 

  21. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees, Belmont. California: Waldsworth International Group, 1984.

    Google Scholar 

  22. Ciampi A, Chang CH, Hogg S, McKinney S. Recursive partition: A versatile method for exploratory data analysis in biostatistics in I.B. McNeill and G.J. Umphrey (eds). Biostatistics 1987; 5: 23–50.

  23. Ciampi A, Lawless FJ, McKinney MS, Singhal K. Regression and recursive partition strategies in the analysis of medical survival data. J Clin Epidemiol 1988; 41: 121–137.

    Google Scholar 

  24. SAS System release 6.12. Cary, NC, USA: SAS Institute Inc., 1997.

  25. Akaike H. A new look at the statistical model identi-fication. IEEE Trans. Automatic Control AC 1974; 19: 716–723.

    Google Scholar 

  26. McCullagh P, Nelder JA. Generalized Linear Models. 2nd ed. London: Chapman &Hall, 1989.

    Google Scholar 

  27. Harrell FE, Lee KL, Mark DB. Tutorial in biostatistics. Multivariate prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361–387.

    Google Scholar 

  28. Delong E, Delong D, Clarke-Pearson D. Comparing the area under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44: 837–845.

    Google Scholar 

  29. Kalbfleisch JG. Probability and Statistical Inference. Volume 2: Statistical Inference. 2nd ed. New-York: Springer-Verlag, 1985.

    Google Scholar 

  30. Sokal RR, Rohlf JF. Biometry, 3rd ed. New York: Freeman, 1995.

    Google Scholar 

  31. Armitage P, David HA. Advances in Biometry. New York: John Wiley &Sons, 1996.

    Google Scholar 

  32. Montgommery DC, Peck EA. Introduction to Linear Regression Analysis. New York: John Wiley &Sons, 1992.

    Google Scholar 

  33. Pohjola-Sintonen S, Rissanen A, Liskola P, Luomanmaki K. Family history as a risk factor of coronary heart disease in patients under 60 years of age. Eur Heart J 1998; 19(2): 235–239.

    Google Scholar 

  34. Niyonsenga T, Xhignesse M, Courteau J, Ciampi A, Lussier-Cacan S, Roy M. Desarollo de una escala de riesgo para evaluar el riesgo actual de cardiopatia coronaria empleando variables de la historia familiar (Development of a family risk scale to evaluate current coronary heart disease risk using family history variables). Cardiovasc Risk Factors 2000; 9(1): 30–42.

    Google Scholar 

  35. Kennel WB and Schatzkin A. Risk factor Analysis. Prog Cardiovasc Dis 1983; 26(4): 309–332.

    Google Scholar 

  36. Bensen JT, Liese AD, Rushing JT, et al. Accuracy of proband reported family history: The NHLBI family history study. Genet Epidemiol 1999; 17(2): 141–150.

    Google Scholar 

  37. Frohlich J, Fodor G, McPherson R, Genest J, Langner N. Rationale for and outline of the recommendations of the Working Group on hypercholesterolemia and other dyslipidemias: Interim report. Can J Cardiol 1998; 14(A): 17A–21A.

    Google Scholar 

  38. Little RJ, Rubin DB. Statistical Analysis With Missing Data. New-York: Wiley & Sons, 1987.

    Google Scholar 

  39. Niyonsenga T. Response probability estimation. J Statistical Planning Inference 1997; 59: 111–126.

    Google Scholar 

  40. Montgomery Douglas C and Peck Elizabeth A. Introduction to Linear Regression Analysis. 2nd ed. New York: Wiley and Sons, 1992.

    Google Scholar 

  41. Marubini E and Valsecchi MG. Analysing Survival Data from Clinical Trials and Observational Studies. New York: Wiley and Sons, 1995.

    Google Scholar 

  42. Charlson ME, Ales KL, Simon R and MacKenzie CR. Why predictive indexes perform less well in validation studies: Is it magic or methods? Arch Int Med 1987; 147: 2155–2161.

    Google Scholar 

  43. Nagelkerke NJD. A note on the general de.nition of the coe.cient of determination. Biometrika 1991; 78: 691–692.

    Google Scholar 

  44. Armitage P and Colton T. Encyclopedia of Biostatistics, Volume 6. New York: Wiley and Sons, 1998.

    Google Scholar 

  45. Lawless JF. Statistical Models and Methods for Lifetime Data. New York: J Wiley, 1992.

    Google Scholar 

  46. Maritz JS, Lwin T. Empirical Bayes Methods, 2nd ed. London: Chapman &Hall, 1989.

    Google Scholar 

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Ciampi, A., Courteau, J., Niyonsenga, T. et al. Family history and the risk of coronary heart disease: Comparing predictive models. Eur J Epidemiol 17, 609–620 (2001). https://doi.org/10.1023/A:1015587428172

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