Discussion

In 1983, the advent of TRISS provided a common mechanism for capturing trauma mortality. TRISS included both anatomic and physiological data as a comprehensive approach to injury evaluation. For decades, TRISS was widely accepted as the standard method for mortality prediction.5 11 However, Rogers et al recently found that TRISS significantly overestimates trauma mortality and does so by an increasing margin annually.8 This likely reflects advances in trauma and critical care as well as changing population demographics that have affected outcomes.

Between 2002 and 2010, Sise et al identified a substantial increase in mortality due to falls (46%) and decrease due to motor vehicle traffic (27%).12 In addition, McGwin et al, Gunst et al, and Evans et al noted a shift away from the classic trimodal distribution of immediate (<1 hour after injury), early (several hours after injury), and late (days to weeks after injury) deaths and toward a bimodal profile with fewer deaths beyond 1 week.13–15 These shifts have occurred simultaneous with advances in resuscitation. Historically, crystalloid was the initial fluid replacement strategy for hemorrhaging patients and often patients would have exposure to large volumes. In the past decade, there has been a diffusion of the adoption of balanced resuscitation with physiological ratios of packed red blood cells (PRBCs), platelets, and plasma while limiting crystalloid. The net result has been an improvement in mortality.16 17 In PROPPR, a recently reported randomised control trial of balanced resuscitation ratios of platelets, plasma, and PRBCs, the major difference in mortality was attributable to a decrease in exsanguinating deaths with balanced resuscitation.18

With advances in critical care and trauma resuscitation, it is likely that the TRISS score has demonstrated poorer performance recently because a proportion of patients have been converted from those with a high probability of death due to complications of hemorrhage to a lower risk group. Although TRISS has undergone several iterations in the form of coefficient revisions since its first inception, its real-time clinical utility remains limited by the need to have fully defined anatomic injury for calculation. This has restricted the use of TRISS to essentially a retrospective tool designed to benchmark performance after all care is complete. Generating a novel mortality prediction score in real time that could augment early clinical management to mitigate poor outcome is the next step toward achieving precision health solutions for trauma care. To achieve this, an AIS-independent prediction tool would be required.

In this study, we generated a novel AIS-independent trauma mortality prediction score (TEMPT) that accurately assesses survival after injury and can be practically adopted in an acute setting. All predictor variables selected for TEMPT are often available on ED admission and are similarly well documented in trauma and critical care literature to be important for trauma mortality.2 19–26 Further, despite its AIS independence, the tool has equivalent performance to the most recent revision of TRISS for all patients and exceeds its performance in the minor injured population. One of the most important advantages of TEMPT compared with TRISS is that TRISS cannot be calculated until the AIS scores are known for individual patients. In most trauma centers, these values are obtained after the patient care episode has been concluded and all the injuries are known. The purpose of the TEMPT score is that it can be used early in care prospectively before the full spectrum of injuries are known.

This is an important distinction. Prediction tools calculated at the end of care are useful for benchmarking and research purposes; however, they provide little prospective benefit at the point of care for the bedside practitioner considering treatment options. As we move toward the goal of precision medicine in trauma care, we will require the ability to discern those more likely to do poorly earlier in care. Earlier identification of these patients creates opportunity to apply precision medicine techniques in an effort to personalise care pathways to mitigate poor outcome. Although most practitioners recognise obviously ill or not ill persons, the value in these types of prediction rules is the ability to discern between the large group in the middle. The TEMPT score performs particularly well in this patient group. Considering the present push toward precision medicine, we believe the accuracy and practical simplicity of TEMPT as an AIS-independent prediction algorithm offers considerable advantages toward directing personalised care in an acute setting.

Some results directly parallel findings in the literature. Similar to our own age cut-off of≥59.5 years, Campbell-Furtick et al recommended age 60 to be considered the new ‘elderly’ in the setting of trauma as it marked the start of the most accelerated rise in mortality rate in relation to advancing age.27 A base excess threshold of≤−4.35 mmol/L aligns with classifications for class II mild shock (<2 to −6 mmol/L), and in an analysis of>16 000 patients of the TraumaRegister DGU database, this cohort exhibited an average aPTT of 32.1 s, thus, mirroring our own PTT cut-off of≥31.4 s.28 Mutschler and colleagues found individuals presenting in class II shock or greater (base excess<−2 mmol/L) displayed a mortality rate more than twice that of non-shock patients (19% vs 7.4%).28 Neville et al identified that among elderly trauma patients this rate becomes even more pronounced as those with an initial base excess of≤−4 mmol/L were more than five times more likely to die than patients with a base excess of>−4 mmol/L.29 Lastly, systolic blood pressure as a predominant upper limit cut-off (≥163.5 mm Hg) likely represents the Cushing’s reflex in the setting of severe brain injury, thus signifying the overwhelming detriment of central nervous system injury. TBI is the leading cause of death immediately after trauma and is also associated with a threefold increased odds of mortality.21

In contrast, other variable cut-offs that we have identified on first pass would not raise concern in most providers; however, we found them to be predictive of poor outcome. This underscores the utility of analytic solutions and precision approaches that have potential to enhance clinician diagnostic abilities especially in patients that initially appear stable. A trauma patient presenting with a hemoglobin of 12.75 g/dL or temperature of 36.25°C does not raise red flags, but these results suggest perhaps they should. It may be time to revise the classic paradigm ‘sick or not sick’ as seemingly innocuous thresholds may now be required for sufficient sensitivity to identify those at risk of higher mortality. The findings suggest that there is inherit value in seemingly benign cut-offs to detect individuals at risk of poor outcome despite initially appearing to be less injured patient. Detecting these patients early may allow for more rigorous care or stratification along a different care pathway that may serve to alter outcomes in a more favorable direction.

Nearly 30 years have passed since the original development of TRISS (1983), RTS (1989), GCS (1974), and ISS (1974), and these findings insinuate their limitations may lie in the assessment of mild to moderately injured patients.3–5 11 30 In cases of severe injury, irreparable damage often dictates outcomes; within this subset, these scores still perform accurately as even the most aggressive care is minimally beneficial for the patient with an anatomically non-survivable injury. In contrast, the historic prediction algorithms are not as effective at discerning outcome in those less severely injured and the TEMPT score appears to offer an advantage in this patient group.

TEMPT statistically outperformed 1995 updated TRISS, RTS, and GCS in the less severely injured patient group. While differences in performance with the most recent version of TRISS did not achieve statistical significance, TRISS had a low AUC and wide CI raising concern about precision, accuracy, and overfitting. Simulation in a larger population through the use of bootstrapping further supports these conclusions and suggests the score would be beneficial within a larger generalised trauma population. Although bootstrapping enhances confidence in the results, it is important to acknowledge that the derivation and validation groups were derived from a population from a single center. The trauma population this sample was drawn from is similar to those previously studied in multicenter studies with our cohort having similar ages, percentage of males, race and blunt mechanism prevalence.18 The patients in the current study are slightly less injured compared with these studies, which reflect a selection bias in the comparison studies that were designed to select only patients requiring transfusion. Although we believe our ACIT cohort to represent a consistent demographic compared with a typical trauma center population, TEMPT should next be studied in a true larger cohort before we can recommend universal adoption.

Finally, despite its role as the gold standard of injury assessment, ISS, derived from AIS scores, exhibited the worst predictive ability of all scoring systems. These results compliment the many studies documenting the limitations of ISS. Narrow inclusion of single injuries from the three most severely damaged body regions diminishes ability to account for polytrauma. Heavy reliance on AIS predisposes to substantial inter-coder variability.7 Gupta et al found the increased utilization of CT pan scanning promotes ISS inflation due to greater detection of minor injuries that increase ISS but do not alter clinical decision-making.31 Joosse et al even identified specific injury profiles of low ISS patients associated with mortality.32 TEMPT provides benefit early in the course of care and does so independently of retrospective ISS.

This study is not without its limitations. First, given this was an observational, non-interventional study, we could not mandate specific laboratory values or clinical data (temperature) be obtained on patients enrolled. Therefore, data for each variable had some missingness, which was 0%–7% for all variables except base excess (28%) and temperature (31%). To assess the possibility that bias may have arisen from missingness, we evaluated differences between the patients with missing and non-missing data. There were no significant differences with regard to injury characteristics (ISS, TBI, or mechanism of injury) and the data appear to be missing at random. Additionally, in order to be certain that the TBI variable collected prospectively in the ED was accurate, TBI status was verified using physician adjudicators blinded to the conduct of the ACIT study. This independent evaluation was done to ensure that patients classified as having TBI did have TBI known at the time of hospital admission from the ED. We specifically used information only known at the time of hospital admission with regards to TBI status. This was important as the goal of developing TEMPT was to be an AIS-independent score able to be used at the time of admission from the ED.

The use of dichotomous treatment of independent variables in place of continuous variables is also a potential limitation. This has the net effect of potentially weakening the predictive ability of regression models. However, as it was our aim to develop a clinically practical score, we believe the inclusion of simple yes/no variables greatly encourages adoption and hospital implementation. All predictor variables were dichotomised and equally weighted to increase TEMPT’s clinical utility. While these amendments do indeed detract from TEMPT’s predictive ability, we argue the added simplicity provides a considerable advantage for adoption within the hospital. Further, we surmise these statistically derived cut-offs themselves pose exciting implications for the current state of injury assessment.