Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

Imagem de Miniatura
Arquivos
art_VIEIRA_Prognostic_Models_in_Severe_Traumatic_Brain_Injury_A_2022.PDF (1.2 MB)
Citações na Scopus
6
Tipo de produção
article
Data de publicação
2022
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
HUMANA PRESS INC
Autores
VIEIRA, Rita de Cassia Almeida
SILVEIRA, Juliana Cristina Pereira
SOUZA, Camila Pedroso Estevam de
SANTANA-SANTOS, Eduesley
SOUSA, Regina Marcia Cardoso de
Citação
NEUROCRITICAL CARE, v.37, n.3, p.790-805, 2022
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
This review aimed to analyze the results of investigations that performed external validation or that compared prognostic models to identify the models and their variations that showed the best performance in predicting mortality, survival, and unfavorable outcome after severe traumatic brain injury. Pubmed, Embase, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, TROVE, and Open Grey databases were searched. A total of 1616 studies were identified and screened, and 15 studies were subsequently included for analysis after applying the selection criteria. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models were the most externally validated among studies of severe traumatic brain injury. The results of the review showed that most publications encountered an area under the curve >= 0.70. The area under the curve meta-analysis showed similarity between the CRASH and IMPACT models and their variations for predicting mortality and unfavorable outcomes. Calibration results showed that the variations of CRASH and IMPACT models demonstrated adequate calibration in most studies for both outcomes, but without a clear indication of uncertainties in the evaluations of these models. Based on the results of this meta-analysis, the choice of prognostic models for clinical application may depend on the availability of predictors, characteristics of the population, and trauma care services.
Palavras-chave
Brain injury, Outcome assessment, Predictive modeling, Prognostic modeling
URI
https://observatorio.fm.usp.br/handle/OPI/50814
Referências
  1. Alba AC, 2017, JAMA-J AM MED ASSOC, V318, P1377, DOI 10.1001/jama.2017.12126
  2. Baztan JJ, 2017, GERIATR GERONTOL INT, V17, P664, DOI 10.1111/ggi.12925
  3. BOYD CR, 1987, J TRAUMA, V27, P370, DOI 10.1097/00005373-198704000-00005
  4. Capizzi A, 2020, MED CLIN N AM, V104, P213, DOI 10.1016/j.mcna.2019.11.001
  5. CHAMPION HR, 1989, J TRAUMA, V29, P623, DOI 10.1097/00005373-198905000-00017
  6. CHAMPION HR, 1990, J TRAUMA, V30, P1356, DOI 10.1097/00005373-199011000-00008
  7. Charry JD, 2017, WORLD NEUROSURG, V101, P554, DOI 10.1016/j.wneu.2017.02.051
  8. Collins GS, 2015, CIRCULATION, V131, P211, DOI [10.1016/j.eururo.2014.11.025, 10.7326/M14-0697, 10.1016/j.jclinepi.2014.11.010, 10.1186/s12916-014-0241-z, 10.1038/bjc.2014.639, 10.1161/CIRCULATIONAHA.114.014508, 10.7326/M14-0698, 10.1002/bjs.9736, 10.1136/bmj.g7594]
  9. Cremer OL, 2006, J TRAUMA, V61, P1484, DOI 10.1097/01.ta.0000195981.63776.ba
  10. Crowson CS, 2016, STAT METHODS MED RES, V25, P1692, DOI 10.1177/0962280213497434
  11. Debray TPA, 2019, STAT METHODS MED RES, V28, P2768, DOI 10.1177/0962280218785504
  12. Debray TPA, 2017, BMJ-BRIT MED J, V356, DOI 10.1136/bmj.i6460
  13. Dijkland SA, 2021, J NEUROTRAUM, V38, P1377, DOI 10.1089/neu.2020.7300
  14. Dijkland SA, 2019, J NEUROTRAUM, DOI 10.1089/neu.2019.6401
  15. Domingues Cristiane de Alencar, 2011, Rev. esc. enferm. USP, V45, P1353
  16. Dullaert M, 2020, WORLD NEUROSURG, V137, pE159, DOI 10.1016/j.wneu.2020.01.118
  17. Fischler L, 2007, EUR J ANAESTH, V24, P676, DOI 10.1017/S026502150700021X
  18. Gomez PA, 2014, J NEUROSURG, V121, P1314, DOI 10.3171/2014.7.JNS131874
  19. Han JL, 2014, J NEUROTRAUM, V31, P1146, DOI 10.1089/neu.2013.3003
  20. Helmrich IRAR, 2021, J NEUROTRAUM, V38, P2502, DOI 10.1089/neu.2019.6708
  21. Letsinger J, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0183552
  22. Maeda Y, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0221791
  23. Moher David, 2009, Open Med, V3, pe123
  24. Najem D, 2018, BIOCHEM CELL BIOL, V96, P391, DOI 10.1139/bcb-2016-0160
  25. Olivecrona M, 2013, J CLIN NEUROSCI, V20, P996, DOI 10.1016/j.jocn.2012.09.015
  26. Olivecrona M, 2012, ACTA NEUROCHIR, V154, P1567, DOI 10.1007/s00701-012-1351-z
  27. PAGE MJ, 2021, BMJ-BRIT MED J, V372, DOI [DOI 10.1136/BMJ.N71, 10.1136/bmj.n160, DOI 10.1136/BMJ.N160]
  28. Panczykowski DM, 2012, J NEUROTRAUM, V29, P47, DOI 10.1089/neu.2010.1482
  29. Pargaonkar R, 2019, J CLIN NEUROSCI, V66, P100, DOI 10.1016/j.jocn.2019.05.011
  30. Perel P, 2008, BMJ-BRIT MED J, V336, P425, DOI 10.1136/bmj.39461.643438.25
  31. Perel Pablo, 2006, BMC Med Inform Decis Mak, V6, P38, DOI 10.1186/1472-6947-6-38
  32. Roozenbeek B, 2012, CRIT CARE MED, V40, P1609, DOI 10.1097/CCM.0b013e31824519ce
  33. Roozenbeek B, 2012, J NEUROTRAUM, V29, P1306, DOI 10.1089/neu.2011.1988
  34. Ruscio J, 2008, PSYCHOL METHODS, V13, P19, DOI 10.1037/1082-989X.13.1.19
  35. Sandsmark DK, 2016, CURR NEUROL NEUROSCI, V16, DOI 10.1007/s11910-016-0654-5
  36. Steyerberg EW, 2008, PLOS MED, V5, P1251, DOI 10.1371/journal.pmed.0050165
  37. Sun H, 2016, J NEUROTRAUM, V33, P1535, DOI 10.1089/neu.2015.4164
  38. Tohira H, 2012, SCAND J TRAUMA RESUS, V20, DOI 10.1186/1757-7241-20-63
  39. van Calster B, 2019, BMC MED, V17, DOI 10.1186/s12916-019-1466-7
  40. Vergouwe Y, 2005, J CLIN EPIDEMIOL, V58, P475, DOI 10.1016/j.jclinepi.2004.06.017
  41. Wan XY, 2017, WORLD NEUROSURG, V103, P584, DOI 10.1016/j.wneu.2017.04.069
  42. Wolff RF, 2019, ANN INTERN MED, V170, P51, DOI 10.7326/M18-1376
  43. Wongchareon K, 2020, INJURY PREV, V26, P546, DOI 10.1136/injuryprev-2019-043466

Coleções
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - LIM/45