The differential mortality of Glasgow Coma Score in patients with and without head injury

Importance: The GCS was created forty years ago as a measure of impaired consciousness following head injury and thus the association of GCS with mortality in patients with traumatic brain injury (TBI) is expected. The association of GCS with mortality in patients without TBI (non-TBI) has been assumed to be similar. However, if this assumption is incorrect mortality prediction models incorporating GCS as a predictor will need to be revised. Objective: To determine if the association of GCS with mortality is influenced by the presence of TBI. Design/setting/participants: Using the National Trauma Data Bank (2012; N = 639,549) we categorized patients as isolated TBI (12.8%), isolated non-TBI (33%), both (4.8%), or neither (49.4%) based on the presence of AIS codes of severity 3 or greater. We compared the ability GCS to discriminate survivors from non-survivors in TBI and in non-TBI patients using logistic models. We also estimated the odds ratios of death for TBI and non-TBI patients at each value of GCS using linear combinations of coefficients. Main outcome measure: Death during hospital admission. Results: As the sole predictor in a logistic model GCS discriminated survivors from non-survivors at an acceptable level (c-statistic = 0.76), but discriminated better in the case of TBI patients (c-statistic = 0.81) than non-TBI patients (c-statistic = 0.70). In both unadjusted and covariate adjusted models TBI patients were about twice as likely to die as non-TBI patients with the same GCS for GCS values. . 8 TBI and non-TBI patients were at similar risk of dying. Conclusions: A depressed GCS predicts death better in TBI patients than non-TBI patients, likely because in non-TBI patients a depressed GCS may simply be the result of entirely reversible intoxication by alcohol or drugs; in TBI patients, by contrast, a depressed GCS is more ominous because it is likely due to a head injury with its attendant threat to survival. Accounting for this observation into trauma mortality datasets and models may improve the accuracy of outcome prediction

Improved regression calibration

The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration approach, a general pseudo maximum likelihood estimation method based on a conveniently decomposed form of the likelihood. It is both consistent and computationally efficient, and produces point estimates and estimated standard errors which are practically identical to those obtained by maximum likelihood. Simulations suggest that improved regression calibration which is easy to implement in standard software, works well in a range of situations