Machine learning and prediction of traumatic brain injury mortality

Imagem de Miniatura
Arquivos
art_SANTOS_Machine_learning_and_prediction_of_traumatic_brain_injury_2022.PDF (2.76 MB)
Citações na Scopus
0
Tipo de produção
bookPart
Data de publicação
2022
DOI
Scopus
Título da Revista
ISSN da Revista
Título do Volume
Editora
ELSEVIER INC.
Autores
SANTOS, J. G. R. P. dos
Citação
dos Santos, J. G. R. P.; Paiva, W. S.. Machine learning and prediction of traumatic brain injury mortality. In: . DIAGNOSIS AND TREATMENT OF TRAUMATIC BRAIN INJURY: THE NEUROSCIENCE OF TRAUMATIC BRAIN INJURY: ELSEVIER INC., 2022. p.327-338.
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Traumatic brain injury is a “pandemic” disease, with alarming numbers each year around the world that accounts for significant healthcare, socioeconomic, and psychological issues in developing countries. The main issue is associated with the disability of affected subjects and reduction of the quality of life, work capacity, and life expectancy. Ability to estimate prognosis at admission is an important decision-making factor. Creating a model for traumatic brain injury evaluation is desired by many researchers. Classical models are based on two important trials: International Mission for Prognosis and Analysis of Clinical Trials and Corticosteroid randomization after significant head injury trial. Each one investigated more than 10,000 patients and this database helped build these two important models. Both are based on admission characteristics, while some other models were created based on clinical courses. Construction of a model involves a rigid protocol: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement recommendations for the reporting of prognostic models. The correct statistical tests must be correctly chosen to select appropriate variables. Machine learning for traumatic brain injury is an elaborate, methodological process that can be used as an example to build similar models for other neurological diseases. © 2022 Elsevier Inc. All rights reserved.
Palavras-chave
Machine learning, Mortality, Prognostic, Traumatic brain injury
URI
https://observatorio.fm.usp.br/handle/OPI/51252
Referências
  1. Amorim, R.L., Oliveira, L.M., Malbouisson, L.M., Nagumo, M.M., Simoes, M., Miranda, L., Paiva, W.S., Prediction of early TBI mortality using a machine learning approach in a LMIC population (2020) Frontiers in Neurology, 10, p. 1366
  2. Andriessen, T.M., Horn, J., Franschman, G., van der Naalt, J., Haitsma, I., Jacobs, B., Vos, P.E., Epidemiology, severity classification, and outcome of moderate and severe traumatic brain injury: A prospective multicenter study (2011) Journal of Neurotrauma, 28 (10), pp. 2019-2031
  3. Bratton, S.L., Chestnut, R.M., Ghajar, J., McConnell Hammond, F.F., Harris, O.A., Hartl, R., Manley, G.T., Wright, D.W., Guidelines for the management of severe traumatic brain injury. VIII. Intracranial pressure thresholds (2007) Journal of Neurotrauma, 24, pp. S55-S58
  4. Brennan, P.M., Murray, G.D., Teasdale, G.M., Simplifying the use of prognostic information in traumatic brain injury. Part 1: The GCS-pupils score: An extended index of clinical severity (2018) Journal of Neurosurgery, 128 (6), pp. 1612-1620
  5. Bullock, R., Chesnut, R.M., Clifton, G., Ghajar, J., Marion, D.W., Narayan, R.K., Wilberger, J.W., Guidelines for the management of severe head injury. Brain Trauma Foundation (1996) European Journal of Emergency Medicine: Official Journal of the European Society for Emergency Medicine, 3 (2), pp. 109-127
  6. Carter, B.G., Butt, W., Review of the use of somatosensory evoked potentials in the prediction of outcome after severe brain injury (2001) Critical Care Medicine, 29 (1), pp. 178-186
  7. Cassidy, J.D., Carroll, L.J., Peloso, P.M., Borg, J., von Holst, H., Holm, L., Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury (2004) Journal of Rehabilitation Medicine, pp. 28-60
  8. Chesnut, R.M., Marshall, L.F., Klauber, M.R., Blunt, B.A., Baldwin, N., Eisenberg, H.M., Foulkes, M.A., The role of secondary brain injury in determining outcome from severe head injury (1993) The Journal of Trauma, 34 (2), pp. 216-222
  9. Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015) Journal of Clinical Epidemiology, 68 (2), pp. 134-143
  10. Demetriades, D., Murray, J., Martin, M., Velmahos, G., Salim, A., Alo, K., Rhee, P., Pedestrians injured by automobiles: Relationship of age to injury type and severity (2004) Journal of the American College of Surgeons, 199 (3), pp. 382-387
  11. Dutton, R.P., Stansbury, L.G., Leone, S., Kramer, E., Hess, J.R., Scalea, T.M., Trauma mortality in mature trauma systems: Are we doing better? An analysis of trauma mortality patterns, 1997-2008 (2010) The Journal of Trauma, 69 (3), pp. 620-626
  12. Faul, M., Xu, L., Wald, M.M., Coronado, V.G., Traumatic brain injury in the United States: Emergency department visits, hospitalizations and deaths 2002-2006 (2010), Atlanta: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention
  13. Gennarelli, T.A., Champion, H.R., Copes, W.S., Sacco, W.J., Comparison of mortality, morbidity, and severity of 59,713 head injured patients with 114,447 patients with extracranial injuries (1994) The Journal of Trauma, 37 (6), pp. 962-968
  14. Huang, S.J., Hong, W.C., Han, Y.Y., Chen, Y.S., Wen, C.S., Tsan, Y.S., Tu, Y.K., Clinical outcome of severe head injury in different protocol-driven therapies (2007) Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia, 14 (5), pp. 449-454
  15. Hukkelhoven, C.W., Steyerberg, E.W., Habbema, J.D., Farace, E., Marmarou, A., Murray, G.D., Maas, A.I., Predicting outcome after traumatic brain injury: Development and validation of a prognostic score based on admission characteristics (2005) Journal of Neurotrauma, 22 (10), pp. 1025-1039
  16. King, J.T., Jr., Carlier, P.M., Marion, D.W., Early Glasgow outcome scale scores predict long-term functional outcome in patients with severe traumatic brain injury (2005) Journal of Neurotrauma, 22 (9), pp. 947-954
  17. Lannoo, E., Van Rietvelde, F., Colardyn, F., Lemmerling, M., Vandekerckhove, T., Jannes, C., De Soete, G., Early predictors of mortality and morbidity after severe closed head injury (2000) Journal of Neurotrauma, 17 (5), pp. 403-414
  18. Lingsma, H.F., Roozenbeek, B., Steyerberg, E.W., Murray, G.D., Maas, A.I., Early prognosis in traumatic brain injury: From prophecies to predictions (2010) The Lancet. Neurology, 9 (5), pp. 543-554
  19. Maas, A.I., Hukkelhoven, C.W., Marshall, L.F., 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 (2005) Neurosurgery, 57 (6), pp. 1173-1182
  20. Majdan, M., Plancikova, D., Brazinova, A., Rusnak, M., Nieboer, D., Feigin, V., Maas, A., Epidemiology of traumatic brain injuries in Europe: A cross-sectional analysis (2016) The Lancet Public Health, 1 (2), pp. e76-e83
  21. Marshall, L.F., Becker, D.P., Bowers, S.A., Cayard, C., Eisenberg, H., Gross, C.R., Warren, J., The national traumatic coma data bank. Part 1: Design, purpose, goals, and results (1983) Journal of Neurosurgery, 59 (2), pp. 276-284
  22. Marshall, L.F., Marshall, S.B., Klauber, M.R., Clark, M.V.B., Eisenberg, H.M., Jane, J.A., Foulkes, M.A., A new classification of head injury based on computerized tomography (1991) Journal of Neurosurgery, 75, pp. S14-S20. , https://thejns.org/view/journals/j-neurosurg/75/Supplement/article-pS14.xml, Accessed date: 16, 2021, Retrieved from
  23. Matsuo, K., Aihara, H., Nakai, T., Morishita, A., Tohma, Y., Kohmura, E., Machine learning to predict in-hospital morbidity and mortality after traumatic brain injury (2020) Journal of Neurotrauma, 37 (1), pp. 202-210
  24. Moore, L., Lavoie, A., Camden, S., Le Sage, N., Sampalis, J.S., Bergeron, E., Abdous, B., Statistical validation of the Glasgow Coma Score (2006) The Journal of Trauma, 60 (6), pp. 1238-1244
  25. Perel, P., Arango, M., Clayton, T., Edwards, P., Komolafe, E., Yutthakasemsunt, S., Predicting outcome after traumatic brain injury: Practical prognostic models based on large cohort of international patients (2008) BMJ (Clinical research ed.), 336 (7641), pp. 425-429
  26. Murray, G.D., Teasdale, G.M., Braakman, R., Cohadon, F., Dearden, M., Iannotti, F., Unterberg, A., The European Brain Injury Consortium survey of head injuries (1999) Acta Neurochirurgica, 141 (3), pp. 223-236
  27. Myburgh, J.A., Cooper, D.J., Finfer, S.R., Venkatesh, B., Jones, D., Higgins, A., Epidemiology and 12-month outcomes from traumatic brain injury in Australia and New Zealand (2008) The Journal of Trauma, 64 (4), pp. 854-862
  28. Ono, J., Yamaura, A., Kubota, M., Okimura, Y., Isobe, K., Outcome prediction in severe head injury: Analyses of clinical prognostic factors (2001) Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia, 8 (2), pp. 120-123
  29. Perel, P., Edwards, P., Wentz, R., Roberts, I., Systematic review of prognostic models in traumatic brain injury (2006) BMC Medical Informatics and Decision Making, 6, p. 38
  30. Prasad, K., The Glasgow Coma Scale: A critical appraisal of its clinimetric properties (1996) Journal of Clinical Epidemiology, 49 (7), pp. 755-763
  31. Roozenbeek, B., Chiu, Y.L., Lingsma, H.F., Gerber, L.M., Steyerberg, E.W., Ghajar, J., Maas, A.I., Predicting 14-day mortality after severe traumatic brain injury: Application of the IMPACT models in the brain trauma foundation TBI-trac® New York State database (2012) Journal of Neurotrauma, 29 (7), pp. 1306-1312
  32. Schnakers, C., Ledoux, D., Majerus, S., Damas, P., Damas, F., Lambermont, B., Laureys, S., Diagnostic and prognostic use of bispectral index in coma, vegetative state and related disorders (2008) Brain Injury, 22 (12), pp. 926-931
  33. Settervall, C.H.C., Sousa, R.M.C., Silva, S.C.F., In-hospital mortality and the Glasgow Coma Scale in the first 72 hours after traumatic brain injury (2011) Revista Latino-Americana de Enfermagem, 19 (6), pp. 1337-1343
  34. Stevens, R.D., Sutter, R., Prognosis in severe brain injury (2013) Critical Care Medicine, 41 (4), pp. 1104-1123
  35. Steyerberg, E., Clinical prediction models (2009) Statistics for biology and health
  36. Steyerberg, E.W., Mushkudiani, N., Perel, P., Butcher, I., Lu, J., McHugh, G.S., Maas, A.I., Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics (2008) PLoS Medicine, 5 (8). , e165
  37. Stocchetti, N., Carbonara, M., Citerio, G., Ercole, A., Skrifvars, M.B., Smielewski, P., Menon, D.K., Severe traumatic brain injury: Targeted management in the intensive care unit (2017) The Lancet. Neurology, 16 (6), pp. 452-464
  38. Stocchetti, N., Pagan, F., Calappi, E., Canavesi, K., Beretta, L., Citerio, G., Colombo, A., Inaccurate early assessment of neurological severity in head injury (2004) Journal of Neurotrauma, 21 (9), pp. 1131-1140
  39. Taylor, C.A., Bell, J.M., Breiding, M.J., Xu, L., Traumatic brain injury-related emergency department visits, hospitalizations, and deaths-United States, 2007 and 2013 (2017) Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. 2002), 66 (9), pp. 1-16
  40. Vik, A., Nag, T., Fredriksli, O.A., Skandsen, T., Moen, K.G., Schirmer-Mikalsen, K., Manley, G.T., Relationship of “dose” of intracranial hypertension to outcome in severe traumatic brain injury (2008) Journal of Neurosurgery, 109 (4), pp. 678-684

Coleções
Livros e Capítulos de Livros - HC/ICHC
Livros e Capítulos de Livros - LIM/45