Skip to main content
Annals of Noninvasive Electrocardiology logoLink to Annals of Noninvasive Electrocardiology
. 2006 Oct 27;6(2):107–116. doi: 10.1111/j.1542-474X.2001.tb00094.x

Computerized ST Depression Analysis Improves Prediction of All‐Cause and Cardiovascular Mortality: The Strong Heart Study

Peter M Okin 1,, Richard B Devereux 1, Jan A Kors 2, Gerard van Herpen 2, Richard S Crow 3, Richard R Fabsitz 4, Barbara V Howard 5
PMCID: PMC7027664  PMID: 11333167

Abstract

Background: Nonspecific ST depression assessed by standard visual Minnesota coding (MC) has been demonstrated to predict risk. Although computer analysis has been applied to digital ECGs for MC, the prognostic value of computerized MC and computerized ST depression analyses have not been examined in relation to standard visual MC.

Methods: The predictive value of nonspecific ST depression as determined by visual and computerized MC codes 4.2 or 4.3 was compared with computer‐measured ST depression ≧ 50 μV in 2,127 American Indian participants in the first Strong Heart Study examination. Computerized MC and ST depression were determined using separate computerized‐ECG analysis programs and visual MC was performed by an experienced ECG core laboratory.

Results: The prevalence of MC 4.2 or 4.3 by computer was higher than by visual analysis (6.4 vs 4.4%, P < 0.001). After mean follow‐up of 3.7 ± 0.9 years, there were 73 cardiovascular deaths and 227 deaths from all causes. In univariate Cox analyses, visual MC (relative risk [RR] 4.8,95% confidence interval [CIJ 2.6–9.1), computerized MC (RR 6.0, 95% Cl 3.5–10.3), and computer‐measured ST depression (RR 7.6, 95% CI 4.5–12.9) were all significant predictors of cardiovascular death. In separate multivariate Cox regression analyses that included age, sex, diabetes, HDL and LDL cholesterol, body mass index, systolic and diastolic blood pressure, microalbuminuria, smoking, and the presence of coronary heart disease, computerized MC (RR 3.0, 95% Cl 1.6–5.6) and computer‐measured ST depression (RR 3.1, 95% Cl 1.7–5.7), but not visual MC, remained significant predictors of cardiovascular mortality. When both computerized MC and computer‐measured ST depression were entered into the multivariate Cox regression, each variable provided independent risk stratification (RR 2.1, 95% Cl 1.0–4.4, and RR 2.1, 95% CI 1.0–4.4, respectively). Similarly, computerized MC and computer‐measured ST depression, but not visual MC, were independent predictors of all‐cause mortality after controlling for standard risk factors.

Conclusions: Computer analysis of the ECG, using computerized MC and computer‐measured ST depression, provides independent and additive risk stratification for cardiovascular and all‐cause mortality, and improves risk stratification compared with visual MC. These findings support the use of routine computer analysis of ST depression on the rest ECG for assessment of risk and suggest that computerized MC can replace visual MC for this purpose. A.N.E. 2001;6(2):107–116

Keywords: electrocardiogram, computerized, Minnesota code, ST depression

REFERENCES

  • 1. Prineas RJ, Crow RS, Blackburn H. The Minnesota Code Manual of Electrocardiographic Findings. Standard and Procedures for Measurement and Classification. John Wright, PSG Inc, Boston , 1982. [Google Scholar]
  • 2. Blackburn H, Keys A, Simonson E, et al. The electrocardiogram in population studies: A classification system. Circulation 1960;21:1160–1175. [DOI] [PubMed] [Google Scholar]
  • 3. Opiik AJ, Dorogy M, Devereux RB, et al. Major electrocardiographic abnormalities among American Indians aged 45–74 years (The Strong Heart Study). Am J Cardiol 1996;78:1400–1405. [DOI] [PubMed] [Google Scholar]
  • 4. Bartel A, Heyden S, Tyroler HA, et al. Electrocardiographs predictors of coronary heart disease. Arch Intern Med 1971;128:929–937. [PubMed] [Google Scholar]
  • 5. The Coronary Drug Project Research Group. Prognostic importance of the electrocardiogram after myocardial infarction. Ann Intern Med 1972;77:677–689. [DOI] [PubMed] [Google Scholar]
  • 6. Cullen K, Stenhouse NS, Wearne KL, et al. Electrocardiograms and 13 year cardiovascular mortality in Busselton study. Br Heart J 1982;47:209–212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Kannel WB, Anderson K, McGee DL, et al. Nonspecific electrocardiograpics abnormality as a predictor of coronary heart disease. Am Heart J 1987;113:370–376. [DOI] [PubMed] [Google Scholar]
  • 8. Kreger BE, Cupples LA, Kannel WB. The electrocadiogram in prediction of sudden death: Framingham Study experience. Am Heart J 1987;113:377–382. [DOI] [PubMed] [Google Scholar]
  • 9. Liao Y, Liu K, Dyer A, et al. Sex differential in the relationship of electrocardiographic ST‐T abnormalities to risk of coronary death: 11.5 year follow‐up findings of the Chicago. Heart Association Detection Project in Industry. Circulation 1987;75: 347–352. [DOI] [PubMed] [Google Scholar]
  • 10. Liao Y, Liu K, Dyer A, et al. Major and minor electrocardiographic abnormalities and risk of death from coronary heart disease, cardiovascular diseases and all causes in men and women. J Am Coll Cardiol 1988;12:1494–1500. [DOI] [PubMed] [Google Scholar]
  • 11. de Bacquer D, Martins Pereira LS, de Backer G, et al. The predictive value of electrocardiographic abnormalities for total and cardiovascular disease mortality in men and women. Eur Heart J 1994;15:1604–1610. [DOI] [PubMed] [Google Scholar]
  • 12. Dekker JM, Schouten EG, Kloorwijk P, et al. ST segment and T wave characteristics as indicators of coronary heart disease risk: The Zutphen Study. J Am Coll Cardiol 1995;25:1321–1326. [DOI] [PubMed] [Google Scholar]
  • 13. Sigurdsson E, Sigfusson N, Sigvaldason H, et al. Silent ST‐T changes in an epidemiologic cohort study. A marker of hypertension or coronary heart disease or both: The Reykjavik Study. J Am Coll Cardiol 1996;27: 1140–1147. [DOI] [PubMed] [Google Scholar]
  • 14. Daviglus ML, Liao Y, Greenland F, et al. Association of nonspecific minor ST‐T abnormalities with cardiovascular mortality: the Chicago Western Electric Study. JAMA 1999;281:530–536. [DOI] [PubMed] [Google Scholar]
  • 15. West of Scotland Coronary Prevention Group . West of Scotland Coronary Prevention Study: identification of high‐risk groups and comparison with other cardiovascular intervention trials. The Lancet 1996;348:1339–1342. [PubMed] [Google Scholar]
  • 16. Crow RS, Prineas RJ, Hannan PJ, et al. Prognostic associations of Minnesota code serial electrocardiographic change classification with coronary heart disease mortality in the Multiple Risk Factor Intervention Trial. Am J Cardiol 1997;80:138–144. [DOI] [PubMed] [Google Scholar]
  • 17. Macfarlane PW, Latif S. Automated serial ECG comparison based on the Minnesota code. J Electrocardiol 1996;29(Suppl):29–34. [DOI] [PubMed] [Google Scholar]
  • 18. de Bruyne MC, Kors JA, Visentin S, et al. Reproducibility of computerized ECG measurements and coding in a nonhospitalized elderly population. J Electrocardiol 1998;31:189–195. [DOI] [PubMed] [Google Scholar]
  • 19. Tuinstra CL, Rautaharju PM, Prineas RJ, et al. The performance of three visual coding procedures and three computer programs in classification of electrocardiograms according to the Minnesota code. J Electrocardiol 1982;15: 345–350. [DOI] [PubMed] [Google Scholar]
  • 20. Kors JA, Van Herpen G, Wu J, et al. Validation of a new computer program for Minnesota coding. J Electrocardiol 1996;29(Suppl):83–88. [DOI] [PubMed] [Google Scholar]
  • 21. Savage DD, Rautaharju PM, Bailey JJ, et al. The emerging prominence of computer electrocardiography in large population‐based surveys. J Electrocardiol 1987;20:48–52. [PubMed] [Google Scholar]
  • 22. Willems JL, Arnaud F, van Bemmel JH, et al, For the Common Standards for Quantitative Electrocardiography (CSE) Working Party. A reference data base for multilead electrocardiographic computer measurement programs. J Am Coll Cardiol 1987;10:1313–1321. [DOI] [PubMed] [Google Scholar]
  • 23. Willems JL, Abreu‐Lima C, Arnaud P, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med 1991;325:1767–1773. [DOI] [PubMed] [Google Scholar]
  • 24. Van Bemmel JH, Kors JA, Van Herpen G. Methodology of the modular ECG analysis system MEANS. Methods Inf Med 1990;29:346–353. [PubMed] [Google Scholar]
  • 25. Lee ET, Welty TK, Fabsitz R, et al. The Strong Heart Study. A study of cardiovascular disease in American Indians: design and methods. Am J Epidemiol 1990;132: 1141–1155. [DOI] [PubMed] [Google Scholar]
  • 26. Okin PM, Devereux RB, Howard BV, et al. Assessment of QT interval and QT dispersion for prediction of all‐cause and cardiovascular mortality: The Strong Heart Study. Circulation 2000;101:61–66. [DOI] [PubMed] [Google Scholar]
  • 27. Rose GA. The diagnosis of ischemic heart pain and intermittent claudication in field surveys. Bull WHO 1962;27: 645–658. [PMC free article] [PubMed] [Google Scholar]
  • 28. Howard BV, Lee ET, Cowan LD, et al. Coronary heart disease prevalence and its relation to risk factor in American Indians: The Strong Heart Study. Am J Epidemiol 1995;142:254–268. [DOI] [PubMed] [Google Scholar]
  • 29. Lee ET, Cowan LD, Sievers M, et al. All‐cause mortality and cardiovascular disease mortality in three American Indian populations, aged 45–74 years, 1984–1988: The Strong Heart Study. Am J Epidemiol 1998;147:995–1008. [DOI] [PubMed] [Google Scholar]
  • 30. Howard BV, Lee ET, Cowan LD, et al. The rising tide of cardiovascular disease in American Indians: The Strong Heart Study. Circulation 1999;99:2389–2395. [DOI] [PubMed] [Google Scholar]
  • 31. Kaplan E, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958;53:457–481. [Google Scholar]
  • 32. Cox DR. Regression models and life tables. J R Stat Soc 1972;34(series B):187–220. [Google Scholar]
  • 33. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. New York , John Wiley & Sons Inc., 1980, pp. 101–103; 199–201. [Google Scholar]
  • 34. Machin D, Gardner MJ. Calculating confidence intervals for survival time analyses. Br Med J (Clin Res Ed) 1988;296: 1369–1371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Okin PM, Kligfield P. Effect of precision of ST segment measurement on identification and quantification of coronary artery disease by the ST/HR index. J Electrocardiol 1991;24(Suppl):62–67. [DOI] [PubMed] [Google Scholar]
  • 36. Lauer MS, Blackstone EH, Young JB, et al. Cause of death in clinical research: Time for a reassessment J Am Coll Cardiol 1999;34:618–620. [DOI] [PubMed] [Google Scholar]

Articles from Annals of Noninvasive Electrocardiology are provided here courtesy of International Society for Holter and Noninvasive Electrocardiology, Inc. and Wiley Periodicals, Inc.

RESOURCES