Abstract
Two of the important predictors of mortality for trauma patients are the Glasgow Coma Scale and the respiratory rate. However, for intubated patients, the verbal response component of the Glasgow Coma Scale and the respiratory rate cannot be accurately obtained. This study extends previous work that attempts to predict mortality accurately for intubated patients without using verbal response and respiratory rate. The New York State Trauma Registry was used to identify 1994 and 1995 victims of motor vehicle crashes (MVCs). For the subset of patients who were not intubated, we developed two statistical models to predict mortality: one did not contain verbal response or respiratory rate, and the other contained a predicted verbal response. These were compared with a model that did include verbal response and respiratory rate. We also compared the predictive abilities of the first two models for all MVC patients (intubated and nonintubated) and determined the extent to which intubated patients were at increased risk of dying in the hospital after having adjusted for other predictors of mortality. For nonintubated patients, the statistical model without verbal response and the model with predicted verbal response had slightly better discrimination and worse calibration than the model that included verbal response and respiratory rate. Predicted verbal response did not improve the strength of the model without verbal response. For all MVC patients (intubated and nonintubated), predicted verbal response was not a significant predictor of mortality when used in combination with the other predictors. Intubation status was a significant predictor, with intubated patients having a higher probability of dying in the hospital than patients with otherwise identical risk factors. Inpatient mortality for intubated MVC patients can be accurately predicted without respiratory rate or verbal response. There appears to be no need for predicted verbal response to be part of the prediction formula, but intubation status is an important independent predictor of mortality and should be used in statistical models that predict mortality for MVC patients.
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