The Glasgow Coma Scale (GCS) has served as an assessment tool in head trauma and as a measure of physiologic derangement in outcome models (e.g., TRISS and Acute Physiology and Chronic Health Evaluation), but it has not been rigorously examined as a predictor of outcome.
Using a large trauma data set (National Trauma Data Bank, N = 204,181), we compared the predictive power (pseudo R2, receiver operating characteristic [ROC]) and calibration of the GCS to its components.
The GCS is actually a collection of 120 different combinations of its 3 predictors grouped into 12 different scores by simple addition (motor [m] + verbal [v] + eye [e] = GCS score). Problematically, different combinations summing to a single GCS score may actually have very different mortalities. For example, the GCS score of 4 can represent any of three mve combinations: 2/1/1 (survival = 0.52), 1/2/1 (survival = 0.73), or 1/1/2 (survival = 0.81). In addition, the relationship between GCS score and survival is not linear, and furthermore, a logistic model based on GCS score is poorly calibrated even after fractional polynomial transformation. The m component of the GCS, by contrast, is not only linearly related to survival, but preserves almost all the predictive power of the GCS (ROC(GCS) = 0.89, ROC(m) = 0.87; pseudo R2(GCS) = 0.42, pseudo R2(m) = 0.40) and has a better calibrated logistic model.
Because the motor component of the GCS contains virtually all the information of the GCS itself, can be measured in intubated patients, and is much better behaved statistically than the GCS, we believe that the motor component of the GCS should replace the GCS in outcome prediction models. Because the m component is nonlinear in the log odds of survival, however, it should be mathematically transformed before its inclusion in broader outcome prediction models.
"The preresuscitation GCS is still one of the important determinants in predicting mortality of TBI patients. The presedation GCS, is a clinical, simple, and practical tool to predict mortality in neurosurgical and head trauma patients. In fact, it has been the gold standard for effective assessment in these patients against, which the other grading systems are usually compared. "
[Show abstract][Hide abstract] ABSTRACT: Aim of Study:We aim to assess and to compare the predicting power for in-hospital mortality (IHM) of the Acute Physiology and Chronic Health Evaluation-II (APACHE-II) and the Simplified Acute Physiology Score-II (SAPS-II) for traumatic brain injury (TBI).Patients and Methods:This retrospective cohort study was conducted during a period of 2 years and 9 months in a Moroccan intensive care unit. Data were collected during the first 24 h of each admission. The clinical and laboratory parameters were analyzed and used as per each scoring system to calculate the scores. Univariate and multivariate analyses through regression logistic models were performed, to predict IHM after moderate and severe TBIs. Areas under the receiver operating characteristic curves (AUROC), specificities and sensitivities were determined and also compared.Results:A total of 225 patients were enrolled. The observed IHM was 51.5%. The univariate analysis showed that the initial Glasgow coma scale (GCS) was lower in nonsurviving patients (mean GCS = 6) than the survivors (mean GCS = 9) with a statistically significant difference (P = 0.0024). The APACHE-II and the SAPS-II of the nonsurviving patients were higher than those of the survivors (respectively 20.4 ± 6.8 and 31.2 ± 13.6 for nonsurvivors vs. 15.7 ± 5.4 and 22.7 ± 10.3 for survivors) with a statistically significant difference (P = 0.0032 for APACHE-II and P = 0.0045 for SAPS-II). Multivariate analysis: APACHE-II was superior for predicting IHM (AUROC = 0.92).Conclusion:The APACHE-II is an interesting tool to predict IHM of head injury patients. This is particularly relevant in Morocco, where TBI is a greater public health problem than in many other countries.
Indian Journal of Critical Care Medicine 06/2014; 18(6):369-75. DOI:10.4103/0972-5229.133895
"Severe TBI was defined as best GCS motor score of 1–3 at 24 h after injury. These methods of classifying injury severity have been used in previous work (Healey et al., 2003; MacKenzie et al., 2006). "
[Show abstract][Hide abstract] ABSTRACT: This study aimed to examine the prevalence and trajectory of sleep disturbances and their associated risk factors in children up to 24 months following a traumatic brain injury (TBI). In addition, the longitudinal association between sleep disturbances and children's functional outcomes was assessed. This was a prospective study of a cohort of children with TBI and a comparison cohort of children with orthopedic injury (OI). Parental reports of pre-injury sleep disturbances were compared to reports of post-injury changes at 3, 12, and 24 months. Risk factors for sleep disturbances were examined, including severity of TBI, presence of psychosocial problems, and pain. Sleep disturbances were also examined as a predictor of children's functional outcomes in the areas of adaptive behavior skills and activity participation. Both cohorts (children with TBI and OI) displayed increased sleep disturbances after injury. However, children with TBI experienced higher severity and more prolonged duration of sleep disturbances compared to children with OI. Risk factors for disturbed sleep included mild TBI, psychosocial problems, and frequent pain. Sleep disturbances emerged as significant predictors of poorer functional outcomes in children with moderate or severe TBI. Children with TBI experienced persistent sleep disturbances over 24 months. Findings suggest a potential negative impact of disturbed sleep on children's functional outcomes, highlighting the need for further research on sleep in children with TBI.
Journal of neurotrauma 01/2012; 29(1):154-61. DOI:10.1089/neu.2011.2126 · 3.71 Impact Factor
"The groups were 10 for the C statistics and between eight and ten for the H statistics, depending on the range of predictions (the cut-points for the predicted probability of death were 10%, 20%... etc.). Because it is recognized that a standard and agreed measure of calibration does not exist , we present calibration also by calibration curves. For simplicity, only curves of equally sized groups for simple and complete models with NISS and NISS+num_inj are shown. "
[Show abstract][Hide abstract] ABSTRACT: Injury scoring is important to formulate prognoses for trauma patients. Although scores based on empirical estimation allow for better prediction, those based on expert consensus, e.g. the New Injury Severity Score (NISS) are widely used. We describe how the addition of a variable quantifying the number of injuries improves the ability of NISS to predict mortality.
We analyzed 2488 injury cases included into the trauma registry of the Italian region Emilia-Romagna in 2006-2008 and assessed the ability of NISS alone, NISS plus number of injuries, and the maximum Abbreviated Injury Scale (AIS) to predict in-hospital mortality. Hierarchical logistic regression was used. We measured discrimination through the C statistics, and calibration through Hosmer-Lemeshow statistics, Akaike's information criterion (AIC) and calibration curves.
The best discrimination and calibration resulted from the model with NISS plus number of injuries, followed by NISS alone and then by the maximum AIS (C statistics 0.775, 0.755, and 0.729, respectively; AIC 1602, 1635, and 1712, respectively). The predictive ability of all the models improved after inclusion of age, gender, mechanism of injury, and the motor component of Glasgow Coma Scale (C statistics 0.889, 0.898, and 0.901; AIC 1234, 1174, and 1167). The model with NISS plus number of injuries still showed the best performances, this time with borderline statistical significance.
In NISS, the same weight is assigned to the three worst injuries, although the contribution of the second and third to the probability of death is smaller than that of the worst one. An improvement of the predictive ability of NISS can be obtained adjusting for the number of injuries.
Scandinavian Journal of Trauma Resuscitation and Emergency Medicine 04/2011; 19:26. DOI:10.1186/1757-7241-19-26 · 2.03 Impact Factor
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