Skip to main content

Advertisement

SpringerLink
Log in

Predicting Long-Term Outcome After Traumatic Brain Injury Using Repeated Measurements of Glasgow Coma Scale and Data Mining Methods

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Previous studies have identified some clinical parameters for predicting long-term functional recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were combined with serial Glasgow Coma Scale (GCS) scores and clinical and laboratory parameters to predict 6-month functional outcome and mortality in patients with TBI. Data of consecutive adult patients presenting at a trauma center with moderate-to-severe head injury were retrospectively analyzed. Clinical parameters including serial GCS measurements at emergency department, 7th day, and 14th day and laboratory data were included for analysis (n = 115). We employed artificial neural network (ANN), naïve Bayes (NB), decision tree, and logistic regression to predict mortality and functional outcomes at 6 months after TBI. Favorable functional outcome was achieved by 34.8 % of the patients, and overall 6-month mortality was 25.2 %. For 6-month functional outcome prediction, ANN was the best model, with an area under the receiver operating characteristic curve (AUC) of 96.13 %, sensitivity of 83.50 %, and specificity of 89.73 %. The best predictive model for mortality was NB with AUC of 91.14 %, sensitivity of 81.17 %, and specificity of 90.65 %. Sensitivity analysis demonstrated GCS measurements on the 7th and 14th day and difference between emergency room and 14th day GCS score as the most influential attributes both in mortality and functional outcome prediction models. Analysis of serial GCS measurements using data mining methods provided additional predictive information in relation to 6-month mortality and functional outcome in patients with moderate-to-severe TBI.

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

Similar content being viewed by others

References

  1. Traumatic brain injury: Time to end the silence. Lancet Neurol. 9(4):331, 2010. doi:10.1016/S1474-4422(10)70069-7.

  2. Ghajar, J., Traumatic brain injury. Lancet 356(9233):923–929, 2000. doi:10.1016/S0140-6736(00)02689-1.

    Article  Google Scholar 

  3. Maas, A. I., Stocchetti, N., and Bullock, R., Moderate and severe traumatic brain injury in adults. Lancet Neurol. 7(8):728–741, 2008. doi:10.1016/S1474-4422(08)70164-9.

    Article  Google Scholar 

  4. Roozenbeek, B., Chiu, Y. L., Lingsma, H. F., Gerber, L. M., Steyerberg, E. W., Ghajar, J., and Maas, A. I., Predicting 14-day mortality after severe traumatic brain injury: Application of the IMPACT models in the brain trauma foundation TBI-trac(R) New York State database. J. Neurotrauma 29(7):1306–1312, 2012. doi:10.1089/neu.2011.1988.

    Article  Google Scholar 

  5. Perez, R., Costa, U., Torrent, M., Solana, J., Opisso, E., Caceres, C., Tormos, J. M., Medina, J., and Gomez, E. J., Upper limb portable motion analysis system based on inertial technology for neurorehabilitation purposes. Sensors (Basel) 10(12):10733–10751, 2010. doi:10.3390/s101210733.

    Article  Google Scholar 

  6. Silver, J. M., McAllister, T. W., and Yudofsky, S. C., Textbook of traumatic brain injury, 2nd edition. American Psychiatric Pub, Washington, DC, 2011.

    Book  Google Scholar 

  7. Brown, A. W., Malec, J. F., McClelland, R. L., Diehl, N. N., Englander, J., and Cifu, D. X., Clinical elements that predict outcome after traumatic brain injury: A prospective multicenter recursive partitioning (decision-tree) analysis. J. Neurotrauma 22(10):1040–1051, 2005. doi:10.1089/neu.2005.22.1040.

    Article  Google Scholar 

  8. Lingsma, H. F., Roozenbeek, B., Steyerberg, E. W., Murray, G. D., and Maas, A. I., Early prognosis in traumatic brain injury: From prophecies to predictions. Lancet Neurol. 9(5):543–554, 2010. doi:10.1016/S1474-4422(10)70065-X.

    Article  Google Scholar 

  9. Eftekhar, B., Mohammad, K., Ardebili, H. E., Ghodsi, M., and Ketabchi, E., Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med. Inf. Decis. Making 5:3, 2005. doi:10.1186/1472-6947-5-3.

    Article  Google Scholar 

  10. Wyatt, J. C., and Altman, D. G., Prognostic models: Clinically useful or quickly forgotten? Brit. M. J. 311:1539–1541, 1995.

    Article  Google Scholar 

  11. Rovlias, A., and Kotsou, S., Classification and regression tree for prediction of outcome after severe head injury using simple clinical and laboratory variables. J. Neurotrauma 21(7):886–893, 2004. doi:10.1089/0897715041526249.

    Article  Google Scholar 

  12. Quigley, M. R., Vidovich, D., Cantella, D., Wilberger, J. E., Maroon, J. C., and Diamond, D., Defining the limits of survivorship after very severe head injury. J. Trauma 42(1):7–10, 1997.

    Article  Google Scholar 

  13. Schreiber, M. A., Aoki, N., Scott, B. G., and Beck, J. R., Determinants of mortality in patients with severe blunt head injury. Arch. Surg. 137(3):285–290, 2002.

    Article  Google Scholar 

  14. Rimel, R. W., Jane, J. A., and Edlich, R. F., An injury severity scale for comprehensive management of central nervous system trauma. JACEP 8(2):64–67, 1979.

    Article  Google Scholar 

  15. Knaus, W. A., Draper, E. A., Wagner, D. P., and Zimmerman, J. E., APACHE II: A severity of disease classification system. Crit. Care Med. 13(10):818–829, 1985.

    Article  Google Scholar 

  16. Teasdale, G., and Jennett, B., Assessment of coma and impaired consciousness. A practical scale. Lancet 2(7872):81–84, 1974.

    Article  Google Scholar 

  17. Collaborators, M. C. T., Perel, P., Arango, M., Clayton, T., Edwards, P., Komolafe, E., Poccock, S., Roberts, I., Shakur, H., Steyerberg, E., and Yutthakasemsunt, S., Predicting outcome after traumatic brain injury: Practical prognostic models based on large cohort of international patients. BMJ 336(7641):425–429, 2008. doi:10.1136/bmj.39461.643438.25.

    Article  Google Scholar 

  18. Roozenbeek, B., Lingsma, H. F., Lecky, F. E., Lu, J., Weir, J., Butcher, I., McHugh, G. S., Murray, G. D., Perel, P., Maas, A. I., Steyerberg, E. W., and International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury Study G, Corticosteroid Randomisation After Significant Head Injury Trial C, Trauma A, Research N, Prediction of outcome after moderate and severe traumatic brain injury: External validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation after Significant Head injury (CRASH) prognostic models. Crit. Care Med. 40(5):1609–1617, 2012. doi:10.1097/CCM.0b013e31824519ce.

    Article  Google Scholar 

  19. Duncan, R., and Thakore, S., Decreased Glasgow Coma Scale score does not mandate endotracheal intubation in the emergency department. J. Emerg. Med. 37(4):451–455, 2009. doi:10.1016/j.jemermed.2008.11.026.

    Article  Google Scholar 

  20. Jain, S., Dharap, S. B., and Gore, M. A., Early prediction of outcome in very severe closed head injury. Injury 39(5):598–603, 2008. doi:10.1016/j.injury.2007.06.003.

    Article  Google Scholar 

  21. Marmarou, A., Lu, J., Butcher, I., McHugh, G. S., Murray, G. D., Steyerberg, E. W., Mushkudiani, N. A., Choi, S., and Maas, A. I., Prognostic value of the Glasgow Coma Scale and pupil reactivity in traumatic brain injury assessed pre-hospital and on enrollment: An IMPACT analysis. J. Neurotrauma 24(2):270–280, 2007. doi:10.1089/neu.2006.0029.

    Article  Google Scholar 

  22. Steyerberg, E. W., Mushkudiani, N., Perel, P., Butcher, I., Lu, J., McHugh, G. S., Murray, G. D., Marmarou, A., Roberts, I., Habbema, J. D., and Maas, A. I., Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics. PLoS Med. 5(8):e165, 2008. doi:10.1371/journal.pmed.0050165. discussion e165.

    Article  Google Scholar 

  23. Stevens, R. D., and Sutter, R., Prognosis in severe brain injury. Crit. Care Med. 41(4):1104–1123, 2013. doi:10.1097/CCM.0b013e318287ee79.

    Article  Google Scholar 

  24. Galanaud, D., Perlbarg, V., Gupta, R., Stevens, R. D., Sanchez, P., Tollard, E., de Champfleur, N. M., Dinkel, J., Faivre, S., Soto-Ares, G., Veber, B., Cottenceau, V., Masson, F., Tourdias, T., Andre, E., Audibert, G., Schmitt, E., Ibarrola, D., Dailler, F., Vanhaudenhuyse, A., Tshibanda, L., Payen, J. F., Le Bas, J. F., Krainik, A., Bruder, N., Girard, N., Laureys, S., Benali, H., Puybasset, L., and Neuro Imaging for Coma E, Recovery C, Assessment of white matter injury and outcome in severe brain trauma: A prospective multicenter cohort. Anesthesiology 117(6):1300–1310, 2012. doi:10.1097/ALN.0b013e3182755558.

    Article  Google Scholar 

  25. Han, J., and Kamber, M., Data mining: concepts and techniques. Diane Cerra, San Francisco, CA, 2006.

    Google Scholar 

  26. Bellazzi, R., and Zupan, B., Predictive data mining in clinical medicine: Current issues and guidelines. Int. J. Med. Inform. 77(2):81–97, 2008. doi:10.1016/j.ijmedinf.2006.11.006.

    Article  Google Scholar 

  27. Meisel, S., and Mattfeld, D., Synergies of operations research and data mining. Eur. J. Oper. Res. 206(1):1–10, 2010. doi:10.1016/j.ejor.2009.10.017.

    Article  MATH  Google Scholar 

  28. Ramesh, A. N., Kambhampati, C., Monson, J. R., and Drew, P. J., Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 86(5):334–338, 2004. doi:10.1308/147870804290.

    Article  Google Scholar 

  29. Ting, H., Mai, Y. T., Hsu, H. C., Wu, H. C., and Tseng, M. H., Decision tree based diagnostic system for moderate to severe obstructive sleep apnea. J. Med. Syst. 38(9):94, 2014. doi:10.1007/s10916-014-0094-1.

    Article  Google Scholar 

  30. Keltch, B., Lin, Y., and Bayrak, C., Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients. J. Med. Syst. 38(8):60, 2014. doi:10.1007/s10916-014-0060-y.

    Article  Google Scholar 

  31. Sen, B., Peker, M., Cavusoglu, A., and Celebi, F. V., A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 38(3):18, 2014. doi:10.1007/s10916-014-0018-0.

    Article  Google Scholar 

  32. Maas, A. I., Hukkelhoven, C. W., Marshall, L. F., and Steyerberg, E. W., Prediction of outcome in traumatic brain injury with computed tomographic characteristics: A comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery 57(6):1173–1182, 2005. discussion 1173–1182.

    Article  Google Scholar 

  33. Segal, M. E., Goodman, P. H., Goldstein, R., Hauck, W., Whyte, J., Graham, J. W., Polansky, M., and Hammond, F. M., The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury. J. Head Trauma Rehabil. 21(4):298–314, 2006.

    Article  Google Scholar 

  34. Lee, S. Y., Kim, S. S., Kim, C. H., Park, S. W., Park, J. H., and Yeo, M., Prediction of outcome after traumatic brain injury using clinical and neuroimaging variables. J. Clin. Neurol. 8(3):224–229, 2012. doi:10.3988/jcn.2012.8.3.224.

    Article  Google Scholar 

  35. Jennett, B., Teasdale, G., Galbraith, S., Pickard, J., Grant, H., Braakman, R., Avezaat, C., Maas, A., Minderhoud, J., Vecht, C. J., Heiden, J., Small, R., Caton, W., and Kurze, T., Severe head injuries in three countries. J. Neurol. Neurosurg. Psychiatry 40(3):291–298, 1977.

    Article  Google Scholar 

  36. Jennett, B., Snoek, J., Bond, M. R., and Brooks, N., Disability after severe head injury: Observations on the use of the Glasgow Outcome Scale. J. Neurol. Neurosurg. Psychiatry 44(4):285–293, 1981.

    Article  Google Scholar 

  37. Overgaard, J., Hvid-Hansen, O., Land, A. M., Pedersen, K. K., Christensen, S., Haase, J., Hein, O., and Tweed, W. A., Prognosis after head injury based on early clinical examination. Lancet 2(7830):631–635, 1973.

    Article  Google Scholar 

  38. Becker, D. P., Miller, J. D., Ward, J. D., Greenberg, R. P., Young, H. F., and Sakalas, R., The outcome from severe head injury with early diagnosis and intensive management. J. Neurosurg. 47(4):491–502, 1977. doi:10.3171/jns.1977.47.4.0491.

    Article  Google Scholar 

  39. Carlsson, C. A., von Essen, C., and Lofgren, J., Factors affecting the clinical corse of patients with severe head injuries. 1. Influence of biological factors. 2. Significance of posttraumatic coma. J. Neurosurg. 29(3):242–251, 1968. doi:10.3171/jns.1968.29.3.0242.

    Article  Google Scholar 

  40. Bowers, S. A., and Marshall, L. F., Outcome in 200 consecutive cases of severe head injury treated in San Diego County: A prospective analysis. Neurosurgery 6(3):237–242, 1980.

    Article  Google Scholar 

  41. Levati, A., Farina, M. L., Vecchi, G., Rossanda, M., and Marrubini, M. B., Prognosis of severe head injuries. J. Neurosurg. 57(6):779–783, 1982. doi:10.3171/jns.1982.57.6.0779.

    Article  Google Scholar 

  42. Mazumdar, M., and Glassman, J. R., Categorizing a prognostic variable: Review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat. Med. 19(1):113–132, 2000.

    Article  Google Scholar 

  43. Shrout, P. E., and Fleiss, J. L., Intraclass correlations: Uses in assessing rater reliability. Psychol. Bull. 86(2):420–428, 1979.

    Article  Google Scholar 

  44. Lin, C. C., Ou, Y. K., Chen, S. H., Liu, Y. C., and Lin, J., Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture. Injury 41(8):869–873, 2010. doi:10.1016/j.injury.2010.04.023.

    Article  Google Scholar 

  45. Patterson, D. W., Artificial neural networks: theory and applications. Prentice Hall, Singapore, New York, 1996.

    MATH  Google Scholar 

  46. Quinlan, J. R., C4.5: programs for machine learning. Morgan Kaufmann, San Francisco, 1993.

    Google Scholar 

  47. Breiman, L., Classification and regression trees. Wadsworth statistics/probability series. Wadsworth International Group, Belmont, Calif., 1984.

    Google Scholar 

  48. Podgorelec, V., Kokol, P., Stiglic, B., and Rozman, I., Decision trees: An overview and their use in medicine. J. Med. Syst., Kluwer Academic/Plenum Press 26(5):445–463, 2002.

    Google Scholar 

  49. Quinlan, J. R., Induction of decision trees. Mach. Learn. 1:81–106, 1986.

    Google Scholar 

  50. Polat, K., Sahan, S., and Gunes, S., A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Syst. Appl. 31(2):264–269, 2006. doi:10.1016/j.eswa.2005.09.019.

    Article  Google Scholar 

  51. Bengio, Y., and Grandvalet, Y., No unbiased estimator of the variance of K-fold cross-validation. J. Mach. Learn. Res. 5:1089–1105, 2004.

    MATH  MathSciNet  Google Scholar 

  52. Stone, M., Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. 36(1):111–147, 1974.

    MATH  Google Scholar 

  53. Efron, B., and Tibshirani, R., Improvements on cross-validation: The. 632+ bootstrap method. J. Am. Stat. Assoc. 92(438):548–560, 1997.

    MATH  MathSciNet  Google Scholar 

  54. Frawley, W. J., Paitetsky-Shapiro, G., and Matheus, C. J. Advances in knowledge discovery and data mining. In: Fayyad, U. M. (Ed.), From Data Mining to Knowledge Discovery: An Overview. AAAI Press/The MIT Press, p 661–620, 1996.

  55. Breiman, L., Friedman, J., Olshen, R., and Stone, C., Classification and regression trees. Wadsworth, Belmont, CA, 1984.

    MATH  Google Scholar 

  56. Dreiseitl, S., and Ohno-Machado, L., Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform. 35(5–6):352–359, 2002.

    Article  Google Scholar 

  57. Altman, D. G., and Bland, J. M., Diagnostic tests 3: Receiver operating characteristic plots. BMJ 309(6948):188, 1994.

    Article  Google Scholar 

  58. Akobeng, A. K., Understanding diagnostic tests 1: Sensitivity, specificity and predictive values. Acta Paediatr. 96(3):338–341, 2007. doi:10.1111/j.1651-2227.2006.00180.x.

    Article  Google Scholar 

  59. Code, C., Rowley, D., and Kertesz, A., Predicting recovery from aphasia with connectionist networks: Preliminary comparisons with multiple regression. Cortex 30(3):527–532, 1994.

    Article  Google Scholar 

  60. Narayan, R. K., Greenberg, R. P., Miller, J. D., Enas, G. G., Choi, S. C., Kishore, P. R., Selhorst, J. B., Lutz, H. A., 3rd, and Becker, D. P., Improved confidence of outcome prediction in severe head injury. A comparative analysis of the clinical examination, multimodality evoked potentials, CT scanning, and intracranial pressure. J. Neurosurg. 54(6):751–762, 1981. doi:10.3171/jns.1981.54.6.0751.

    Article  Google Scholar 

  61. Hoffmann, M., Lefering, R., Rueger, J. M., Kolb, J. P., Izbicki, J. R., Ruecker, A. H., Rupprecht, M., Lehmann, W., and Trauma Registry of the German Society for Trauma S, Pupil evaluation in addition to Glasgow Coma Scale components in prediction of traumatic brain injury and mortality. Br. J. Surg. 99(Suppl 1):122–130, 2012. doi:10.1002/bjs.7707.

    Article  Google Scholar 

  62. Signorini, D. F., Andrews, P. J., Jones, P. A., Wardlaw, J. M., and Miller, J. D., Predicting survival using simple clinical variables: A case study in traumatic brain injury. J. Neurol. Neurosurg. Psychiatry 66(1):20–25, 1999.

    Article  Google Scholar 

  63. Lesko, M. M., Jenks, T., O’Brien, S. J., Childs, C., Bouamra, O., Woodford, M., and Lecky, F., Comparing model performance for survival prediction using total Glasgow Coma Scale and its components in traumatic brain injury. J. Neurotrauma 30(1):17–22, 2013. doi:10.1089/neu.2012.2438.

    Article  Google Scholar 

  64. Nelson, D. W., Rudehill, A., MacCallum, R. M., Holst, A., Wanecek, M., Weitzberg, E., and Bellander, B. M., Multivariate outcome prediction in traumatic brain injury with focus on laboratory values. J. Neurotrauma 29(17):2613–2624, 2012. doi:10.1089/neu.2012.2468.

    Article  Google Scholar 

  65. Leitgeb, J., Mauritz, W., Brazinova, A., Majdan, M., Janciak, I., Wilbacher, I., and Rusnak, M., Glasgow Coma Scale score at intensive care unit discharge predicts the 1-year outcome of patients with severe traumatic brain injury. Eur. J. Trauma Emerg. Surg. 39(3):285–292, 2013. doi:10.1007/s00068-013-0269-3.

    Article  Google Scholar 

  66. Deeks, J. J., and Altman, D. G., Diagnostic tests 4: Likelihood ratios. BMJ 329:168–169, 2004.

    Article  Google Scholar 

  67. Dujardin, B., Ende, J. V., Gompel, A. V., Unger, J.-P., and Stuyft, P. V. D., Likelihood ratios: A real improvement for clinical decision making? Eur. J. Epidemiol. 10:29–36, 1994.

    Article  Google Scholar 

  68. Gill, C. J., Sabin, L., and Schmid, C. H., Why clinicians are natural Bayesians. BMJ 330(7499):1080–1083, 2005.

    Article  Google Scholar 

  69. Akobeng, A. K., Understanding diagnostic tests 2: Likelihood ratios, pre- and post-test probabilities and their use in clinical practice. Acta Paediatr. 96(4):487–491, 2007. doi:10.1111/j.1651-2227.2006.00179.x.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliou City, Yun-Lin county, 640, Taiwan

    Hsueh-Yi Lu

  2. Department of Nursing, National Taiwan University Hospital, Yun-Lin branch, Yun-Lin county, 640, Taiwan

    Tzu-Chi Li

  3. Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital and College of Medicine, No.7, Chungshan S. Rd., Zhongzheng Dist., Taipei City, 100, Taiwan

    Yong-Kwang Tu, Jui-Chang Tsai & Lu-Ting Kuo

  4. Department of Surgery, National Taiwan University Hospital, Taipei, 100, Taiwan

    Hong-Shiee Lai

Authors
  1. Hsueh-Yi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tzu-Chi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong-Kwang Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jui-Chang Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong-Shiee Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lu-Ting Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu-Ting Kuo.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, HY., Li, TC., Tu, YK. et al. Predicting Long-Term Outcome After Traumatic Brain Injury Using Repeated Measurements of Glasgow Coma Scale and Data Mining Methods. J Med Syst 39, 14 (2015). https://doi.org/10.1007/s10916-014-0187-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-014-0187-x

Keywords

Search

Navigation