Comparison of artificial intelligence techniques with UKTRISS for estimating probability of survival after trauma

Background: The development of TRISS was principally a search for variables that correlated with outcome. It is not known, however, if linear statisti cal models provide optimal results. Artificial intelligence techniques can answer this question and also determine the most important predictor variab les. Methods: An artificial neural network, using 16 anatomic and physiologic pr edictor variables, was compared with the latest United Kingdom version of T RISS model. Results: Both methods were 89.6% correct, but TRISS was significantly bette r by the area under the receiver operating characteristic curve (0.941 vs. 0.921, p < 0.001), The artificial neural network, however, was better calib rated to the test data (Hosmer-Lemeshow statistic, 58.3 vs. 105.4), Head in jury, age, and chest injury were the most important predictors by linear or nonlinear methods, whereas respiration rate, heart rate, and systolic bloo d pressure were underused, Conclusion: Prediction using linear statistics is adequate but not optimal. Only half the predictors have important predictive value, fewer still when using linear classification. The strongest predictors swamp any nonlineari ty observed in other variables.