Volume 48, Issue 9, Pages (September 2017)

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Volume 48, Issue 9, Pages 1932-1943 (September 2017) Differential effects of the Glasgow Coma Scale Score and its Components: An analysis of 54,069 patients with traumatic brain injury Florence C.M. Reith, Hester F. Lingsma, Belinda J. Gabbe, Fiona E. Lecky, Ian Roberts, Andrew I.R. Maas Injury Volume 48, Issue 9, Pages 1932-1943 (September 2017) DOI: 10.1016/j.injury.2017.05.038 Copyright © 2017 Elsevier Ltd Terms and Conditions

Fig. 1 Frequency diagrams of sum scores differentiated for each database. Injury 2017 48, 1932-1943DOI: (10.1016/j.injury.2017.05.038) Copyright © 2017 Elsevier Ltd Terms and Conditions

Fig. 2 Interplay of the GCS components and the sum score. Floor effects of the motor, verbal and eye components are reached at sum scores 3, 7 and 8, respectively. Ceiling effects are reached at sum score 13, 15 and 14, respectively. The motor score plateau phase is at sum score 7–12. Injury 2017 48, 1932-1943DOI: (10.1016/j.injury.2017.05.038) Copyright © 2017 Elsevier Ltd Terms and Conditions

Fig. 3 Frequency of different GCS component profiles with identical sum scores (blue) and their accompanying case fatality rates (red). Blue bars: showing the total amount of patients (N) for each specific component-profile on a logarithmic scale. The component-profiles with identical sum scores are clustered. Component-profiles were excluded from analysis if N<at least 5 expected deaths. Red line: showing the case fatality rate (%) for each component-profile group. *p<0.05, indicates a significant difference in case fatality rates between different component-profiles with identical sum scores. Horizontal brown dots: showing the mean weighted case fatality rate (%) for each sum score. Injury 2017 48, 1932-1943DOI: (10.1016/j.injury.2017.05.038) Copyright © 2017 Elsevier Ltd Terms and Conditions

Fig. 4 The relation of the sum score and GCS components with logit CFR. Results of logistic regression analyses with accompanying 95% confidence intervals indicated by the polygons around the solid lines. Results are differentiated by data set. Non-linear relations on a logistic scale were revealed if the polynominal regression model showed a significant higher goodness of fit compared to the linear model (p<0.01). Injury 2017 48, 1932-1943DOI: (10.1016/j.injury.2017.05.038) Copyright © 2017 Elsevier Ltd Terms and Conditions

Fig. 5 Prognostic value of the GCS components and sum score expressed as Nagelkerke’s R2 values. Results are differentiated by data set. Prognostic values are expressed as Nagelkerke’s R2 values derived from logistic regression models in the data sets. In case of non-linear relations (as revealed by previous analysis), quadratic terms were included in the regression models. The open bars give the R2 values for the unadjusted association of each GCS component or sum score with mortality. The R2 for E+M+V-model is derived from multivariate regression models. The hatched bars give the partial R2 values (i.e. the uncorrelated prognostic value) of the components adjusting for the other two components. Injury 2017 48, 1932-1943DOI: (10.1016/j.injury.2017.05.038) Copyright © 2017 Elsevier Ltd Terms and Conditions