• DOI:10.3171/2014.7.JNS131874
• Corpus ID: 37248191

Validation of a prognostic score for early mortality in severe head injury cases.

@article{Gmez2014ValidationOA,
  title={Validation of a prognostic score for early mortality in severe head injury cases.},
  author={Pedro A. G{\'o}mez and Javier de-la-Cruz and David Lora and Luis Jimenez-Roldan and Gregorio Rodr{\'i}guez-Boto and Rosa Sarabia and Juan Sahuquillo and Roberto Lastra and Jes{\'u}s Morera and Eglis Lazo and Jaime Dom{\'i}nguez and Javier Iba{\~n}ez and Marta Brell and Adolfo de-la-Lama and Ramiro D. Lobato and Alfonso Lagares},
  journal={Journal of neurosurgery},
  year={2014},
  volume={121 6},
  pages={
          1314-22
        }
}

• P. Gómez, J. de-la-Cruz, +13 authors A. Lagares
• Published 19 September 2014
• Medicine
• Journal of neurosurgery

OBJECT
Traumatic brain injury (TBI) represents a large health and economic burden. Because of the inability of previous randomized controlled trials (RCTs) on TBI to demonstrate the expected benefit of reducing unfavorable outcomes, the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in TBI) and CRASH (Corticosteroid Randomisation After Significant Head Injury) studies provided new methods for performing prognostic studies of TBI. This study aimed to develop and externally validate a prognostic model for early death (within 48 hours). The secondary aim was to identify patients who were more likely to succumb to an early death to limit their inclusion in RCTs and to improve the efficiency of RCTs.

METHODS
The derivation cohort was recruited at 1 center, Hospital 12 de Octubre, Madrid (1990-2003, 925 patients). The validation cohort was recruited in 2004-2006 from 7 study centers (374 patients). The eligible patients had suffered closed severe TBIs. The study outcome was early death (within 48 hours post-TBI). The predictors were selected using logistic regression modeling with bootstrapping techniques, and a penalized reduction was used. A risk score was developed based on the regression coefficients of the variables included in the final model.

RESULTS
In the validation set, the final model showed a predictive ability of 50% (Nagelkerke R(2)), with an area under the receiver operating characteristic curve of 89% and an acceptable calibration (goodness-of-fit test, p = 0.32). The final model included 7 variables, and it was used to develop a risk score with a range from 0 to 20 points. Age provided 0, 1, 2, or 3 points depending on the age group; motor score provided 0 points, 2 (untestable), or 3 (no response); pupillary reactivity, 0, 2 (1 pupil reacted), or 6 (no pupil reacted); shock, 0 (no) or 2 (yes); subarachnoid hemorrhage, 0 or 1 (severe deposit); cisternal status, 0 or 3 (compressed/absent); and epidural hematoma, 0 (yes) or 2 (no). Based on the risk of early death estimated with the model, 4 risk of early death groups were established: low risk, sum score 0-3 (< 1% predicted mortality); moderate risk, sum score 4-8 (predicted mortality between 1% and 10%); high risk, sum score 9-12 (probability of early death between 10% and 50%); and very high risk, sum score 13-20 (early mortality probability > 50%). This score could be used for selecting patients for clinical studies. For example, if patients with very high risk scores were excluded from our study sample, the patients included (eligibility score < 13) would represent 80% of the original sample and only 23% of the patients who died early.

CONCLUSIONS
The combination of Glasgow Coma Scale score, CT scanning results, and secondary insult data into a prognostic score improved the prediction of early death and the classification of TBI patients.