Accurate risk adjustment for injured patients is an essential requirement for clinical trials involving trauma patients and for research assessing outcome after injury. In addition, stratifying injured patients by risk of death is important for evaluating and comparing clinical performance within and between institutions. Despite the need for an effective risk adjustment tool, an accurate, generalizable, and widely accepted method has not been devised.^{1,2} Injury Severity Score (ISS) has been one of the most commonly used score systems in the trauma literature, but has important limitations.^{1} This score is based on anatomic injuries and is calculated from values of the Abbreviated Injury Scale (AIS) using an empirically derived method. ISS has a nonmonotonic relationship with mortality rate, occurring because of ISS values derived from different AIS triplets.^{3–5} The New Injury Severity Score (NISS) is a modification of ISS described in 1997 that performs better than ISS, but has similar limitations.^{5,6}

The Trauma and Injury Severity Score (TRISS) is an estimate of the probability of survival that is calculated using ISS, the Revised Trauma Score (calculated using Glasgow Coma Scale score, blood pressure, and respiratory rate), age, and type of injury (blunt vs. penetrating).^{7} Because it incorporates physiologic data and injury characteristics in addition to anatomic injury, it is not surprising that TRISS is a more accurate predictor of survival than ISS. After its introduction, TRISS rapidly became a standard method for predicting survival after injury. This score continues to have widespread use as a measure of performance at trauma centers, applied either directly as the predicted probability of survival for individual patients or in the derivation of institution-specific statistics (Z-score or W-score) for comparing performance to benchmark databases.^{1} Because calculation of TRISS requires a value for respiratory rate and the verbal component of the Glasgow Coma Scale, this score cannot be directly assessed among intubated patients, a subpopulation of trauma patients in whom accurate estimates of survival may be most needed. In addition, physiologic data are often missing in many records in trauma databases, particularly those with a high risk of mortality,^{8} and cannot be used for records from administrative datasets that have no physiologic data.

More recently, the International Classification of Disease-9 revision (ICD-9)–based Injury Severity Score (ICISS) was described, a method that uses individual ICD-9 injury codes for estimating injury severity.^{9} This score is appealing because ICD-9 codes are available in trauma and nontrauma databases, and it does not require physiologic data for calculation. ICISS is based on calculating the proportion of survivors among all patients with each injury ICD-9 code. While this value is a proportion rather than a ratio, it has been called the “survival risk ratio” (SRR). This approach was taken because of the challenge of using standard methods to perform a regression with the large number (>2,000) of individual ICD-9 injury codes entered as binary predictors. In its original description, ICISS was calculated as the product of the SRRs of the injuries for each patient.^{9} This product method supposes independence of the risk of individual injuries, generally not a reasonable assumption. It was subsequently observed that the single worst SRR for each patient predicted survival better than the product of all SRRs.^{10} In an attempt to partially address the problem of independence, ICISS was further modified by calculating SRRs only from records that had a single injury, followed by calculation of the product of these SRRs.^{11} While this method performed better than the original ICISS derivation, some injury codes do not occur in isolation in any record, and information potentially available from patients with multiple injuries is lost. Because it is mathematically inaccurate to view ICISS as an estimate of the probability of survival, ICISS is best described as an ad hoc score. Despite the mathematical limitations of ICISS, this score performs well, leading some to suggest that ICISS is now the preferred risk adjustment method for trauma, especially when physiologic parameters are not available.^{11} Because of its initially promising performance, investigators have recently begun using ICISS as a risk adjustment method for research purposes.^{12–14}

In our research work with administrative databases, we required a risk adjustment method based on injury ICD-9 codes that did not require physiologic data. While ICISS has performed adequately in initial studies, we were concerned that its mathematical limitations would lead to bias in some applications. Standard regression methods are not a suitable alternative because of a variety of practical and theoretical difficulties due to the large number of ICD-9 injury codes. Bayesian logistic regression is a method that can incorporate large numbers of predictors without loss of model performance, addressing some of the limitations of standard regression methods in this context.^{15} The specific purpose of this study was to use Bayesian logistic regression to develop models for predicting injury mortality using ICD-9 codes and to compare the predictive performance of these models with ICISS. Our overall goal was to develop a predictive model that performs better than currently available methods for predicting injury mortality.