BACKGROUND: To validate the accuracy of the Revised Trauma Score (RTS) and its components for predicting in-hospital mortality. METHODS: Analyses were based on 22,388 patients from the trauma registries of three urban Level I trauma centers in the province of Quebec, Canada. The accuracy of RTS coded variables for the Glasgow Coma Score (GCSc), Systolic Blood Pressure (SBPc), and Respiratory Rate (RRc) for predicting mortality was evaluated in logistic regression models with measures of discrimination and model fit and compared with Fractional Polynomial (FP) transformations of each component. RESULTS: RTS coded variables were associated with sparse data distributions and did not accurately represent the relation of GCS, SBP, and RR to mortality. FP models were always associated with significantly better discrimination (all p < 0.00001) and model fit. Survival probability estimates generated by the model with FP transformations were significantly different to those generated by the model with RTS-coded variables. CONCLUSIONS: The RTS in its present form does not accurately describe the relation of GCS, SBP, and RR to mortality. FP transformation would improve the accuracy of predicted survival probabilities used for performance evaluation and may improve control of confounding caused by of physiologic severity case mix in trauma research.
BACKGROUND: To validate the accuracy of the Revised Trauma Score (RTS) and its components for predicting in-hospital mortality. METHODS: Analyses were based on 22,388 patients from the trauma registries of three urban Level I trauma centers in the province of Quebec, Canada. The accuracy of RTS coded variables for the Glasgow Coma Score (GCSc), Systolic Blood Pressure (SBPc), and Respiratory Rate (RRc) for predicting mortality was evaluated in logistic regression models with measures of discrimination and model fit and compared with Fractional Polynomial (FP) transformations of each component. RESULTS: RTS coded variables were associated with sparse data distributions and did not accurately represent the relation of GCS, SBP, and RR to mortality. FP models were always associated with significantly better discrimination (all p < 0.00001) and model fit. Survival probability estimates generated by the model with FP transformations were significantly different to those generated by the model with RTS-coded variables. CONCLUSIONS: The RTS in its present form does not accurately describe the relation of GCS, SBP, and RR to mortality. FP transformation would improve the accuracy of predicted survival probabilities used for performance evaluation and may improve control of confounding caused by of physiologic severity case mix in trauma research.
Authors: Geoffrey A Anderson; Jordan Bohnen; Richard Spence; Lenka Ilcisin; Karim Ladha; David Chang Journal: World J Surg Date: 2018-09 Impact factor: 3.352
Authors: Ida Füglistaler-Montali; Corinna Attenberger; Philipp Füglistaler; Augustinus L Jacob; Felix Amsler; Thomas Gross Journal: World J Surg Date: 2009-11 Impact factor: 3.352