Skip to main content
SpringerLink
Log in

An Exploratory Study Using Data Envelopment Analysis to Assess Neurotrauma Patients in the Intensive Care Unit

  • Published:
Health Care Management Science Aims and scope Submit manuscript

Abstract

Patients with severe head trauma were studied retrospectively to determine if Data Envelopment Analysis (DEA) could successfully model patients early in their stay in an intensive care unit. Variables examined were cerebral perfusion pressure, body temperature, mean arterial pressure, serum osmolarity and pCO2. Unlike regression-based models that focus on mean values for the group, DEA evaluates each patient individually calculating an “efficiency” score based on a patient's ability to maximize output for a given set of physiologic inputs. Patients with high efficiency scores were found to have a better chance of making a full recovery than similarly injured patients that were inefficient. This approach needs further study but may offer physicians the opportunity to improve patient outcome subject to the manipulation of individual variables from the results of a DEA model rather than aiming for normal or average physiologic values.

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

Similar content being viewed by others

References

  1. A.I. Ali, IDEAS Help Manual, Version 6.02, IDEAS 6 (1 Consulting, Amherst, 1997).

  2. Anonymous, The Brain Trauma Foundation: The American Association of Neurological Surgeons, The Joint Section of Neurotrauma and Critical Care, Guidelines for cerebral perfusion pressure, Journal of Neurotrauma 17 (2000) 507–511.

    Google Scholar 

  3. A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European Journal of Operations Research 2 (1978) 429–444.

    Google Scholar 

  4. R.M. Chestnut, L.F. Marshall, M.R. Klauber et al., The role of secondary brain injury in determining outcome fore severe head injury, Journal of Trauma 34 (1993) 216–222.

    Google Scholar 

  5. J.A. Chilingerian and D. Sherman, Managing physicians' efficiency and effectiveness in providing hospital services, Health Services Management Research 3 (1990) 1.

    Google Scholar 

  6. G.L. Clifton, E.R. Miller, S.C. Choi et al., Lack of effect of induction of hypothermia after acute brain injury, New England Journal of Medicine 344 (2001) 556–563.

    Google Scholar 

  7. W.W. Cooper, L.M. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA Software (Kluwer Academic Publishers, Boston, 2000).

    Google Scholar 

  8. D.A. Draper, I. Solti and Y.A. Ozcan, Characteristics of health maintenance organizations and their influence on efficiency, Health Services Management Research 13 (2000) 40–56.

    Google Scholar 

  9. M.J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society A 120 (1957) 253–290.

    Google Scholar 

  10. T.L. Higgins, Severity of illness indices and outcome prediction: Development and evaluation, in: Textbook of Critical Care Medicine, 4th edn., eds. A. Grenvik, S. Ayers, P. Holbrook and W. Shoemaker (W.B. Saunders Company, Philadelphia, 2000).

    Google Scholar 

  11. S.M. Hollenberg and J.E. Parillo, Contemporary issues in critical care medicine, Journal of the American Medical Association 25 (1996) 1799–1781.

    Google Scholar 

  12. C. Hukkelhoven, M. Eijkemans and E.W. Steyerberg, Correspondence to predicting survival using simple clinical variables: A case study in traumatic brain injury, Journal of Neurology, Neurosurgery and Psychiatry 68 (2000) 396–397.

    Google Scholar 

  13. R. Jacobs, Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis, Health Care Management Science 4 (2001) 103–115.

    Google Scholar 

  14. S. Joshi, Augmenting critical perfusion pressure. When is enough too much? Journal of Neurosurgical Anesthesiology 8 (1996) 249–252.

    Google Scholar 

  15. N. Juul, G.F. Morris, S.B. Marshall et al., Intracranial hypertension and cerebral perfusion pressure: Influence on neurological deterioration and outcome in severe head injury, Journal of Neurosurgery 92 (2000) 1–6.

    Google Scholar 

  16. W.A. Knaus, D.P. Wagners, E.A. Draper et al., The APACHE III prognostic systems: Risk prediction of hospital mortality for critically ill hospitalized adults, Chest 100 (1991) 1619–1636.

    Google Scholar 

  17. J.R. Le Gall and S. Lemeshow, A new simplified acute physiology score (SAPS II) based on a European/North American multi center study, Journal of the American Medical Association 270 (1993) 2957– 2963.

    Google Scholar 

  18. S. Lemeshow, D. Teres et al., Mortality probability models (MPM II) based on an international cohort of intensive care patients, Journal of the American Medical Association 270 (1993) 2478–2486.

    Google Scholar 

  19. I.C.F. Lewin, The Cost of Disorders of the Brain (The National Foundation for the Brain, Washington, 1992).

    Google Scholar 

  20. D.W. Marion and T.P. Spiegel, Changes in the management of severe traumatic brain injury, 1991–1997, Critical Care Medicine 28 (2000) 16–18.

    Google Scholar 

  21. NIH Consensus Development Panel on Rehabilitation of Persons with Traumatic Brain Injury, Rehabilitation of persons with traumatic brain injury, Journal of the American Medical Association 282 (1999) 974– 983.

    Google Scholar 

  22. J.J. O'Connor, E.F. Robertson and P. De Maupertuis, http://www. groups.dcs.st-and.ac.uk/~history/mathematicians.html (December 2, 2000).

  23. P.A. Patel and B.J. Grant, Application of mortality prediction systems to individual intensive care units, Intensive Care Medicine 25 (1999) 977–982.

    Google Scholar 

  24. Physics time line to 1799, http://www.weburbia.com/pg/hist1.htm (December 2, 2000).

  25. T. Price, L. Miller and M. de Scossa, The Glasgow coma scale in intensive care: A study, Nursing in Critical Care 5 (2000) 170–173.

    Google Scholar 

  26. L.M. Seiford and R.M. Thrall, Recent developments in DEA: The mathematical approach to frontier analysis, Journal of Econometrics 46 (1990) 7–38.

    Google Scholar 

  27. D.F. Signnorini, P.J. Andrews, P.A. Jones et al., Predicting survival using simple clinical variables: A case study in traumatic brain injury, Journal of Neurology, Neurosurgery and Psychiatry 66 (1999) 20–25.

    Google Scholar 

  28. G. Teasdale and B. Jennet, Assessment of coma and impaired consciousness: A practical scale, Lancet 2 (1974) 81–84.

    Google Scholar 

  29. E. Thanassoulis, A comparison of regression analysis and data envelopment analysis as alternative methods for performance assessments, Journal of the Operational Research Society 44 (1993) 1129–1144.

    Google Scholar 

  30. D.J. Thurman, C. Alverson, K.A. Dunn, J. Guerrero and J.E. Sniezek, Traumatic brain injury in the United States: A public health perspective, Journal of Head Trauma Rehabilitation 14 (1999) 602–615.

    Google Scholar 

  31. M.J. Vassar, F.R. Lewis Jr., J.A. Chambers et al., Prediction of outcome in intensive care unit trauma patients: A multicenter study of acute physiology and chronic health evaluation (APACHE), trauma and injury severity score (TRISS), and a 24-hour intensive care unit (ICU) point system, Journal of Trauma-Injury Infection and Critical Care 47 (1999) 324–329.

    Google Scholar 

  32. A. Young and S. Willats, Controversies in management of acute brain trauma, Lancet 353 (1998) 164–166.

    Google Scholar 

  33. J.E. Zimmerman, D.P. Wagner, E.A. Draper et al., Evaluation of acute physiology and chronic health evaluation III prediction of hospitality in an independent database, Critical Care Medicine 28 (1998) 1317–1326.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, USA

    Brian Harris Nathanson & Richard J. Giglio

  2. Adult Critical Care Service, Baystate Medical Center, Springfield, MA, 01199, USA

    Thomas L. Higgins

  3. Department of Surgery, Baystate Medical Center, Springfield, MA, 01199, USA

    Imtiaz A. Munshi & Jay S. Steingrub

  4. Medical Intensive Care Unit, Baystate Medical Center, Springfield, MA, 01199, USA

    Imtiaz A. Munshi

Authors
  1. Brian Harris Nathanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas L. Higgins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard J. Giglio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Imtiaz A. Munshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jay S. Steingrub
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nathanson, B.H., Higgins, T.L., Giglio, R.J. et al. An Exploratory Study Using Data Envelopment Analysis to Assess Neurotrauma Patients in the Intensive Care Unit. Health Care Management Science 6, 43–55 (2003). https://doi.org/10.1023/A:1021912320922

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1021912320922

Search

Navigation