The Applicability of Trauma and Injury Severity Score for a Blunt Trauma Population in Korea and a Proposal of New Models Using Score Predictors

Validation of the Trauma and Injury Severity Score for Prediction of Mortality in a Greek Trauma Population

BACKGROUND

Although the Trauma and Injury Severity Score (TRISS) has been extensively used for mortality risk adjustment in trauma, its applicability in contemporary trauma populations is increasingly questioned.

OBJECTIVE

The study aimed to evaluate the predictive performance of the TRISS in its original and revised version and compare these with a recalibrated version, including current data from a Greek trauma population.

METHODS

This is a retrospective cohort study of admitted trauma patients conducted in two tertiary Greek hospitals from January 2016 to December 2018. The model algorithm was calculated based on the Major Trauma Outcome Study coefficients (TRISSMTOS), the National Trauma Data Bank coefficients (TRISSNTDB), and reweighted coefficients of logistic regression obtained from a Greek trauma dataset (TRISSGrTD). The primary endpoint was inhospital mortality. Models' prediction was performed using discrimination and calibration statistics.

RESULTS

A total of 8,988 trauma patients were included, of whom 854 died (9.5%). The TRISSMTOS displayed excellent discrimination with an area under the curve (AUC) of 0.912 (95% CI 0.902-0.923) and comparable with TRISSNTDB (AUC = 0.908, 95% CI 0.897-0.919, p = .1195). Calibration of both models was poor (Hosmer-Lemeshow test p < .001), tending to underestimate the probability of mortality across almost all risk groups. The TRISSGrTD resulted in statistically significant improvement in discrimination (AUC = 0.927, 95% CI 0.918-0.936, p < .0001) and acceptable calibration (Hosmer-Lemeshow test p = .113).

CONCLUSION

In this cohort of Greek trauma patients, the performance of the original TRISS was suboptimal, and there was no evidence that it has benefited from its latest revision. By contrast, a strong case exists for supporting a locally recalibrated version to render the TRISS applicable for mortality prediction and performance benchmarking.

Reverse shock index multiplied by Glasgow coma scale as a predictor of massive transfusion in trauma

BACKGROUND AND PURPOSE

Previous studies have identified that the reverse shock index multiplied by the Glasgow Coma Scale score (rSIG) is a good predictor of mortality in trauma patients. However, it is unknown if rSIG has utility as a predictor for massive transfusion (MT) in trauma patients. The present study evaluated the ability of rSIG to predict MT in trauma patients.

METHODS

This was a retrospective, observational study performed at a level 1 trauma center. Consecutive patients who presented to the trauma center emergency department between January 2016 and December 2018 were included. The predictive ability of rSIG for MT was assessed as our primary outcome measure. Our secondary outcome measures were the predictive ability of rSIG for coagulopathy, in-hospital mortality, and 24-h mortality. We compared the prognostic performance of rSIG with the shock index, age shock index, and quick Sequential Organ Failure Assessment.

RESULTS

In total, 1627 patients were included and 117 (7.2%) patients received MT. rSIG showed the highest area under the receiver operating characteristic (AUROC) curve (0.842; 95% confidence interval [CI], 0.806--0.878) for predicting MT. rSIG also showed the highest AUROC for predicting coagulopathy (0.769; 95% CI, 0.728-0.809), in-hospital mortality (AUROC 0.812; 95% CI, 0.772-0.852), and 24-h mortality (AUROC 0.826; 95% CI, 0.789-0.864). The sensitivity of rSIG for MT was 0.79, and the specificity of rSIG for MT was 0.77. All tools had a high negative predictive value and low positive predictive value.

CONCLUSION

rSIG is a useful, rapid, and accurate predictor for MT, coagulopathy, in-hospital mortality, and 24- h mortality in trauma patients.

RISC II is superior to TRISS in predicting 30-day mortality in blunt major trauma patients in Hong Kong

PURPOSE

Hong Kong (HK) trauma registries have been using the Trauma and Injury Severity Score (TRISS) for audit and benchmarking since their introduction in 2000. We compare the mortality prediction model using TRISS and Revised Injury Severity Classification, version II (RISC II) for trauma centre patients in HK.

METHODS

This was a retrospective cohort study with all five trauma centres in HK. Adult trauma patients with Injury Severity Score (ISS) > 15 suffering from blunt injuries from January 2013 to December 2015 were included. TRISS models using the US and local coefficients were compared with the RISC II model. The primary outcome was 30-day mortality and the area under the receiver operating characteristic curve (AUC) for tested models.

RESULTS

1840 patients were included, of whom 1236/1840 (67%) were male. Median age was 59 years and median ISS was 25. Low falls were the most common mechanism of injury. The 30-day mortality was 23%. RISC II yielded a superior AUC of 0.896, compared with the TRISS models (MTOS: 0.848; PATOS: 0.839; HK: 0.858). Prespecified subgroup analyses showed that all the models performed worse for age ≥ 70, ASA ≥ III, and low falls. RISC II had a higher AUC compared with the TRISS models in all subgroups, although not statistically significant.

CONCLUSION

RISC II was superior to TRISS in predicting the 30-day mortality for Hong Kong adult blunt major trauma patients. RISC II may be useful when performing future audit or benchmarking exercises for trauma in Hong Kong.

Effect of acute alcohol intoxication on mortality, coagulation, and fibrinolysis in trauma patients

BACKGROUND

The effect of alcohol on the outcome and fibrinolysis phenotype in trauma patients remains unclear. Hence, we performed this study to determine whether alcohol is a risk factor for mortality and fibrinolysis shutdown in trauma patients.

MATERIALS AND METHODS

A total of 686 patients who presented to our trauma center and underwent rotational thromboelastometry were included in the study. The primary outcome was in-hospital mortality. Logistic regression analysis was performed to determine whether alcohol was an independent risk factor for in-hospital mortality and fibrinolysis shutdown.

RESULTS

The rate of in-hospital mortality was 13.8% and blood alcohol was detected in 27.7% of the patients among our study population. The patients in the alcohol-positive group had higher mortality rate, higher clotting time, and lower maximum lysis, more fibrinolysis shutdown, and hyperfibrinolysis than those in the alcohol-negative group. In logistic regression analysis, blood alcohol was independently associated with in-hospital mortality (odds ratio [OR] 2.578; 95% confidence interval [CI], 1.550-4.288) and fibrinolysis shutdown (OR 1.883 [95% CI, 1.286-2.758]). Within the fibrinolysis shutdown group, blood alcohol was an independent predictor of mortality (OR 2.168 [95% CI, 1.030-4.562]).

CONCLUSIONS

Alcohol is an independent risk factor for mortality and fibrinolysis shutdown in trauma patients. Further, alcohol is an independent risk factor for mortality among patients who experienced fibrinolysis shutdown.

Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study

Background

This study aimed to develop a model for predicting trauma outcomes by adding arterial lactate levels measured upon emergency room (ER) arrival to existing trauma injury severity scoring systems.

Methods

We examined blunt trauma cases that were admitted to our hospital during 2010– 2014. Eligibility criteria were cases with an Injury Severity Score of ≥9, complete Trauma and Injury Severity Score (TRISS) variable data, and lactate levels that were assessed upon ER arrival. Survivor and non-survivor groups were compared and lactate-based prediction models were generated using logistic regression. We compared the predictive performances of traditional prediction models (Revised Trauma Score [RTS] and TRISS) and lactate-based models using the area under the curve (AUC) of receiver operating characteristic curves.

Results

We included 829 patients, and the in-hospital mortality rate among these patients was 21.6%. The model that used lactate levels and age provided a significantly better AUC value than the RTS model. The model with lactate added to the TRISS variables provided the highest Youden J statistic, with 86.0% sensitivity and 70.8% specificity at a cutoff value of 0.15, as well as the highest predictive value, with a significantly higher AUC than the TRISS.

Conclusions

These findings indicate that lactate testing upon ER arrival may help supplement or replace traditional physiological parameters to predict mortality outcomes among Korean trauma patients. Adding lactate levels also appears to improve the predictive abilities of existing trauma outcome prediction models.

Pre-hospital modified shock index for prediction of massive transfusion and mortality in trauma patients

BACKGROUND

Modified shock index (MSI) is a useful predictor in trauma patients. However, the value of prehospital MSI (preMSI) in trauma patients is unknown. The aim of this study was to investigate the accuracy of preMSI in predicting massive transfusion (MT) and hospital mortality among trauma patients.

METHODS

This was a retrospective, observational, single-center study. Patients presenting consecutively to the trauma center between January 2016 and December 2017, were included. The predictive ability of both prehospital shock index (preSI) and preMSI for MT and hospital mortality was assessed by calculating the areas under the receiver operating characteristic curves (AUROCs).

RESULTS

A total of 1007 patients were included. Seventy-eight (7.7%) patients received MT, and 30 (3.0%) patients died within 24 h of admission to the trauma center. The AUROCs for predicting MT with preSI and preMSI were 0.773 (95% confidence interval [CI], 0.746-0.798) and 0.765 (95% CI, 0.738-0.791), respectively. The AUROCs for predicting 24-hour mortality with preSI and preMSI were 0.584 (95% CI, 0.553-0.615) and 0.581 (95% CI, 0.550-0.612), respectively.

CONCLUSIONS

PreSI and preMSI showed moderate accuracy in predicting MT. PreMSI did not have higher predictive power than preSI. Additionally, in predicting hospital mortality, preMSI was not superior to preSI.

Trauma and Injury Severity Score modification for predicting survival of trauma in one regional emergency medical center in Korea: Construction of Trauma and Injury Severity Score coefficient model

Background:

The problem that is central to trauma research is the prediction of survival rate after trauma. Trauma and Injury Severity Score is being used for predicting survival rate after trauma. Many countries have conducted a study on the classification, characteristics of variables, and the validity of the Trauma and Injury Severity Score model. However, few investigations have been made on the characteristics of coefficients or variables related to Trauma and Injury Severity Score in Korea. Objectives: There is a need for coefficient analysis of Trauma and Injury Severity Score which was created based on the United States database to be optimized for the situation in Korea.

Methods:

This study examined how the currently used Trauma and Injury Severity Score coefficients were developed and created for trauma patients visiting the emergency department in a hospital in Korea using the analytical method. A total of 34,340 trauma patients who were hospitalized into an emergency center from January 2012 to December 2014 for 3 years were analyzed with trauma registry established on August 2006.

Results:

Trauma and Injury Severity Score coefficients were transformed with the methods that were used to make the existing Trauma and Injury Severity Score coefficients using the trauma patients’ data. Regression coefficients (B) were drawn by building up a logistic regression analysis model that used variables such as Injury Severity Score, Revised Trauma Score, and age depending on survival with Trauma and Injury Severity Score.

Conclusion:

With regard to Trauma and Injury Severity Score established in the United States differing from Korea in injury types, it seems possible to realize significant survival rate by deriving coefficients with data in Korea and reanalyzing them.