Trauma and Injury Severity Score (TRISS): Is it time for variable re-categorisations and re-characterisations?
Introduction
Injury remains a public health problem of vast proportions, leading to a massive loss of health and life,20 and is estimated to cause 12–13% of the entire global burden of disease.19 While reducing preventable injury is a health priority for most agencies worldwide,17 it remains relatively under-funded and many avoidable traumatic injuries occur.19, 26 Governments, health departments, health administrators and clinicians constantly seek to improve the delivery of medical health care to reduce the mortality and morbidity associated with injuries, once sustained.27 Trauma scoring systems provide one vehicle for the benchmarking and monitoring of trauma system performance over time, between hospitals and over jurisdications.25, 27, 31
Trauma scoring systems have traditionally focused on reducing preventable deaths,3, 25 and performance monitoring has primarily involved comparing observed survival outcomes with expected norms.4 Multiple scoring systems exist.25, 30 However, despite its extensively documented limitations,6, 12, 14, 15 the Trauma and Injury Severity Score (TRISS) continues to be the most commonly used tool for benchmarking trauma outcome.12, 13 Originally proposed in 1983 to predict a patient's probability of survival,7 TRISS is a weighted combination of patient age (AGE), Injury Severity Score (ISS) and Revised Trauma Score (RTS). The TRISS coefficients (which give the variable weights) were estimated from ordinary logistic regression models in 1987,3 and then revised in 1995,6 using the American College of Surgeons Committee on Trauma coordinated Major Trauma Outcome Study (MTOS) database. More recently, in 2010, these TRISS coefficients have been further revised using data obtained from the American College of Surgeons Committee on Trauma National Trauma Data Bank (NTDB) and the NTDB National Sample Project (NSP).24
Considerable energy has been devoted to improving or refining TRISS; for example, through recalibration of the coefficients,6, 24 careful consideration of the effect of missing data,16, 24 or through specification of new or modified variables, such as the New Injury Severity Score (NISS)22. These investigations are often18, 21 but not always1, 28 fruitful, usually yielding models with incrementally superior predictive performance. However, what has not been challenged in the literature is the classification of the TRISS variables themselves or the way in which they are characterised within the ordinary logistic regression models. In the TRISS model, patient age is treated as a binary variable, ISS and RTS are both treated as continuous variables, each assumed to have a linear relationship with survival over their value range, and the RTS is a weighted linear combination of three variables (respiratory rate [RR], systolic blood pressure [SBP], Glasgow Coma Score [GCS]), each categorised into 5 groups, assigned a value from 0 to 4, and then treated as a linear continuous variable. The rationale for the age and RTS component variable categorisations in predicting survival does not appear to have been tested in the literature nor have the relatively strong linear assumptions. Since the derivation of TRISS over 30 years ago, major contemporary datasets have been established, more sophisticated statistical techniques developed, and computational capacity and power have dramatically increased. Revisiting and improving the existing TRISS variable specifications and relaxing the regression model assumptions may improve the predictive power of the TRISS model; perhaps substantially.
Using a major nationally representative database, this study aims to explore the adequacy of the existing TRISS variable categorisations and assumptions by investigating variable re-classifications and alternative variable characterisations in the logistic model used to predict survival after traumatic injury. Predictive performances of two conventionally defined TRISS models and the newly specified TRISS models will be presented and assessed.
Section snippets
Data sources: the National Trauma Data Bank (NTDB) National Sample Project (NSP)
The American College of Surgeons Committee on Trauma established the NTDB in 1997.30 Currently, the NTDB contains detailed data on over 3 million cases from over 900 United States trauma centres.30 However, like the MTOS database, the NTDB is not population based and consists solely of data submitted by participating trauma centres. It includes a disproportionate number of larger hospitals with younger and more severely injured patients,30 which may, in turn, affect the generalisability of
Results
For the NSP, 280,129 patients had valid eligible trauma codes. In these, the mechanism of injury was burns for 4854 (1.7%) or unknown for 5194 (1.9%), leaving 270,081 (96.4%) patients who provide a weighted estimate number of 1,278,563. Table 2 includes the socio-demographic and injury profile of this full weighted NSP sample (for adults and children) so that it is directly comparable with the full MTOS sample of 80,544 trauma patients from 139 United States and Canadian trauma centres,
Discussion
Substantial and important improvements in the predictive power of TRISS were demonstrated in this paper simply by re-classifying the component variables and treating the variable categories nominally, with a series of indicator variables each having their own coefficient. This adopted analytic approach employs standard contemporary epidemiological and statistical techniques, and relaxes the strong assumption associated with the continuous linear relationships inherent within the conventionally
Conclusion
While substantial improvements to TRISS have been demonstrated in this paper, further research is needed to derive an appropriate re-classified TRISS model and specify coefficients. Even if additional new potential component variables are ignored (such as: co-morbidities, body region, injury intent, NISS), a thorough statistical investigation is required studying existing variable relationships and the effect of missing data.16, 24 Currently, TRISS is essentially a main-effects binary logistic
Conflict of interest statement
The author does not have any financial or personal relationships with other people or organisations that could inappropriately influence this work.
Funding source
None.
Acknowledgements
Grateful thanks is extended to the NTDB and NSP personnel who approved and supplied the data and assisted with data related questions, including Dr. Avery Nathens, St. Michael's Hospital, Toronto, Canada; and Ms. Melanie Neal and Ms. Sandra Goble, NTDB, American College of Surgeons, Chicago, US. Thanks also is extended to Dr. Cate Cameron, Griffith University, School of Medicine, Logan, Australia; Ms. Tamzyn Davey, National Trauma Registry Consortium, Royal Australasian College of Surgeons,
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