Skip to main content

Advertisement

SpringerLink
Log in

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Peterson S, Rayner M (2002) Coronary heart disease statistics: 2002 edition, British Heart Foundation

  2. Wu A (Ed) (1998) Cardiac markers, pathology and labratory medicine. Humana Press, Totowa, NJ

  3. Goldman L, Cook E, Brand D, Lee T, Rouan G, Weisberg M, Acampora D, Stasiulewicz C, Walshon J, Terranova G, Gottlieb L, Kobernick L, Goldstein-Wayne B, Copen D, Daley K, Brandt A, Jones D, Mellors J, Jakubowski R (1998) A computer protocol to predict myocardial infarction in emergency department patients with chest pain. New Engl J Med 318(13):797–803

    Google Scholar 

  4. Goldman L, Weinberg M, Weisberg M, Olshen R, Cook E, Sargent R, Lams G, Dennis C, Wilson C, Deckelbaum L, Fineberg H, Stiratelli R (1982) A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. New Engl J Med 307(10):588–596

    Google Scholar 

  5. Selker H, Beshansky J, Griffith J, Aufderheide T, Ballin D, Bernard S, Crespo S, Feldman J, Fish S, Gibler W, Kiez D, McNutt R, Moulton A, Ornato J, Podrid P, Pope J, Salem D, Sayre M, Woolard R (1998) Use of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia: a multicenter, controlled clinical trial. Ann Intern Med 129(1):845–855

    Google Scholar 

  6. Fesmire F, Hughes A, Fody E, Jackson A, Fesmire C, Gilbert M, Stout P, Wojcik J, Wharton D, Creel J (2002) The Eulanger chest pain evaluation protocol: a one-year experience with serial 12-lead ECG monitoring, two-hour delta serum marker measurements, and selective nuclear stress testing to identify and exclude acute coronary syndromes. Ann Emerg Med 40(6):584–594

    Article  Google Scholar 

  7. Pozen M, D’Agostino R, Selker H, Sytkowski P, Hood W (1984) A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. New Engl J Med 310(20):1273–1278

    Google Scholar 

  8. Tierney W, Roth B, Psaty B, McHenry R, Fitzgerald J, Stump D, Anderson K, Ryder K, McDonald C, Smith D (1985) Predictors or myocardial infarction in emergency room patients. Crit Care Med 13(7):526–531

    Article  Google Scholar 

  9. Harrison R, Marshall S, Kennedy R (1992) Minimum-risk decisions in the management of suspected heart attack: an application of the Boltzmann perceptron network. In: Rogers S (Ed) Proceedings of SPIE. Applications of Artificial Neural Networks III, vol 1709, pp 1701–1082

  10. Fraser H, Pugh R, Kennedy R, Ross P, Harrison R (1994) A comparison of back propagation and radial basis functions in the diagnosis of myocardial infarction. In: Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Health Care, pp 76–84

  11. Ellenuis J, Groth T, Lindahl B, Wallentin L (1997) Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis. Clin Chem 43(10):1919–1925

    Google Scholar 

  12. Groth T, Ellenius J (2002) Provision of decision support for acute myocardial infarction. United States Patent 6:443–889

    Google Scholar 

  13. Bishop C (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

  14. de Dombal F (1996) Medical informatics: the essentials, Butterworth-Heinemann, Linacre House. Jordan Hill, Oxford, UK

    Google Scholar 

  15. Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operator characteristic (ROC) curve. Radiology 143:29–36

    Google Scholar 

  16. Hilden J, Habbema J, Bjerregaard B (1978) The measurement of performance in probabilistic diagnosis: II. Trustworthiness of exact values of the diagnostic probabilities. Methods Inform Med 17(4):227–237

    Google Scholar 

  17. Hilden J, Habbema J, Bjerregaard B (1978) The measurement of performance in probabilistic diagnosis: III. Methods based on continuous functions of diagnostic probabilities. Methods Inform Med 17(4):238–246

    Google Scholar 

  18. Habbema J, Hilden J, Bjerregaard B (1978) The measurement of performance in probabilistic diagnosis: I. The problem, descriptive tools, and measures based on classification matricies. Methods Inform Med 17(4):217–226

    Google Scholar 

  19. Habbema J, Hilden J (1981) The measurement of performance in probabilistic diagnosis: IV. Utility considerations in therapeutics and prognostics. Methods Inform Med 20(2):80–96

    Google Scholar 

  20. Habbema J, Hilden J, Bjerregaard B (1981) The measurement of performance in probabilistic diagnosis: V. General recommendations. Methods Inform Med 20(2):97–100

    Google Scholar 

  21. Huang P, Harris C, Nixon M (1998) Comparing different template features for recognising people by their gait. In: Proceedings of the Ninth British Machine Vision Conference, vol 2, pp 639–648

  22. Nabney I (2002) Netlab: algorithms for pattern recognition, advances in pattern recognition. Springer, Berlin

  23. Inc., TM, Matlab version 6.1 release 12.1, Software (2001)

  24. Armstrong G, Petry C, Wagner G, Wu A (1999) Reflex algorithm for early and cost effective diagnosis of myocardial infarctions suitable for automated diagnostic platforms. European Patent Application number: 99110216.1

  25. Magee D, Boyle R (1998) Improving class seperation in principal component analysis using delta analysis, research Report: School of Comnputer Studies. University of Leeds

  26. Fesmire F, Percy R, Bardoner J, Wharton D, Calhoun F (1998) Serial creatinine kinase (CK) MB testing during the emergency department evaluation of chest pain: utility of a 2-hour delta CK-MB of +1.6 ng/ml. Am Heart J 136(2):237–244

    Article  Google Scholar 

Download references

Acknowledgments

The first author would like to the Department for Employment and Learning, Northern Ireland, for providing a Ph.D. studentship, and Randox Laboratories for financial support and provision of data.

Author information

Authors and Affiliations

  1. Intelligent Systems and Control Group, Electrical and Electronic Engineering, Queen’s University Belfast, Belfast, BT9 5AH, UK

    Paul C. Wilson & George W. Irwin

  2. Randox Laboratories Ltd., 55 Diamond Road, Crumlin, Co. Antrim, BT29 4QY, UK

    John V. Lamont

  3. Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Robert F. Harrison

Authors
  1. Paul C. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George W. Irwin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John V. Lamont
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert F. Harrison
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert F. Harrison.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wilson, P.C., Irwin, G.W., Lamont, J.V. et al. Probabilistic classification of acute myocardial infarction from multiple cardiac markers. Pattern Anal Applic 12, 321–333 (2009). https://doi.org/10.1007/s10044-008-0126-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-008-0126-x

Keywords

Search

Navigation