A new approach to probability of survival scoring for trauma quality assurance.

This study examined the application of an artificial intelligence technique, the neural network (NET), in predicting probability of survival (Ps) for patients with penetrating trauma. A NET is a computer construct that can detect complex patterns within a data set. A NET must be "trained" by supplying a series of input patterns and the corresponding expected output (e.g., survival). Once trained, the NET can recall the proper outputs for a specific set of inputs. It can also extrapolate correct outputs for patterns never before encountered. A neural network was trained on Revised Trauma Score, Injury Severity Score, age, and survival data contained in 3500 of 8300 state registry records of all patients with penetrating trauma reported in Pennsylvania from 1987 through 1990. The remaining 4800 records were analyzed by TRISS, ASCOT, and the trained NET. Sensitivity (accuracy of predicting death) and specificity (accuracy of predicting survival) were 0.840 and 0.985 for TRISS, 0.842 and 0.985 for ASCOT, and 0.904 and 0.972 for the neural network. This represents a decrease in the number of improperly classified ("unexpected") deaths, from 73 for TRISS and 72 for ASCOT, to 44 for the neural network. The increased sensitivity was statistically significant by Chi-square analysis. The NET for penetrating trauma provided a more sensitive but less specific technique for calculating Ps than did either TRISS or ASCOT. This translated into a 40% reduction in the number of deaths requiring review, and the potential for more efficient use of quality assurance resources.