Applicability of the predictors of the historical trauma score in the present Dutch trauma population: Modelling the TRISS 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.

Does Liberal Prehospital and In-Hospital Tranexamic Acid Influence Outcome in Severely Injured Patients? A Prospective Cohort Study

Background

Early hemorrhage control is important in trauma-related death prevention. Tranexamic acid (TXA) has shown to be beneficial in patients in hemorrhagic shock, although widespread adoption might result in incorrect TXA administration leading to increased morbidity and mortality.

Methods

A 7-year prospective cohort study with consecutive trauma patients admitted to a Level-1 Trauma Center ICU was performed to investigate administration of both pre- and in-hospital TXA and its relation to morbidity and mortality. Indication for prehospital and in-hospital TXA administration was (suspicion of) hemorrhagic shock, and/or systolic blood pressure (SBP) ≤ 90 mmHg. Demographics, data on physiology, resuscitation and outcomes were prospectively collected.

Results

Four hundred and twenty-two patients (71% males, median ISS 29, 95% blunt injuries) were included. Even though TXA patients were more severely injured with more deranged physiology, no differences in outcome were noted. Overall, thrombo-embolic complication rate was 8%. In half the patients, hemorrhagic shock was the indication for prehospital TXA, whereas 79% of in-hospital TXA was given based on suspicion of hemorrhagic shock. Thirteen percent of patients with SBP ≤ 90 mmHg in ED received no TXA at all. Based on SBP alone, 22% of prehospital TXA and 25% of in-hospital TXA were justified.

Conclusions

Despite being more severely injured, TXA patients had similar outcome compared to patients without TXA. Thrombo-embolic complication rate was low despite liberal use of both prehospital and in-hospital TXA. Caution should be exercised in selecting patients for TXA, although this might be challenging based on SBP alone in patients who do not yet show signs of deranged physiology on arrival in ED.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00268-021-06143-y.

Incidence of acute respiratory distress syndrome and associated mortality in a polytrauma population

Background

The incidence of acute respiratory distress syndrome (ARDS) has decreased in the last decade by improvement in trauma and critical care. However, it still remains a major cause of morbidity and mortality. This study investigated the current incidence and mortality of ARDS in polytrauma patients.

Methods

A 4.5-year prospective study included consecutive trauma patients admitted to a level 1 trauma center intensive care unit (ICU). Isolated head injuries, drowning, asphyxiation, burns, and deaths <48 hours were excluded. Demographics, Injury Severity Score (ISS), physiologic parameters, resuscitation parameters, Denver Multiple Organ Failure scores, and ARDS data according to Berlin criteria were prospectively collected. Data are presented as median (IQR), and p<0.05 was considered significant.

Results

241 patients were included. The median age was 45 (27–59) years, 178 (74%) were male, the ISS was 29 (22–36), and 232 (96%) patients had blunt injuries. Thirty-one patients (13%) died. Fifteen patients (6%) developed ARDS. The median time to ARDS onset was 3 (2–5) days after injury. The median duration of ARDS was 2.5 (1–3.5) days. All patients with ARDS were male compared with 61% of non-ARDS patients (p=0.003). Patients who developed ARDS had higher ISS (30 vs. 25, p=0.01), lower Partial Pressure of Oxygen in arterial blood (PaO₂₎ both in the emergency department and ICU, and higher Partial Pressure of Carbon Dioxide in arterial blood (PaCo₂) in the ICU. Patients with ARDS needed more crystalloids <24 hours (8.7 vs. 6.8 L, p=0.03), received more fresh frozen plasma <24 hours (3 vs. 0 U, p=0.04), and more platelet <8 hours and <24 hours. Further, they stayed longer on the ventilator (11 vs. 2 days, p<0.001), longer in the ICU (12 vs. 3 days, p<0.001), and in the hospital (33 vs. 15 days, p=0.004). Patients with ARDS developed more often multiple organ dysfunction syndrome (40% vs. 3%, p<0.001) and died more often (20% vs. 3%, p=0.01). Only one patient with ARDS (7%) died of ARDS.

Discussion

In this polytrauma population mortality was predominantly caused by brain injury. The incidence of ARDS was low; its presentation was only early onset, during a short time period, and accompanied by low mortality.

Level of evidence

Level III.

Increased reduction in exsanguination rates leaves brain injury as the only major cause of death in blunt trauma

INTRODUCTION

Central nervous system (CNS) related injuries and exsanguination have been the most common causes of death in trauma for decades. Despite improvements in haemorrhage control in recent years exsanguination is still a major cause of death. We conducted a prospective database study to investigate the current incidence of haemorrhage related mortality.

MATERIALS AND METHODS

A prospective database study of all trauma patients admitted to an urban major trauma centre between January 2007 and December 2016 was conducted. All in-hospital trauma deaths were included. Cause of death was reviewed by a panel of trauma surgeons. Patients who were dead on arrival were excluded. Trends in demographics and outcome were analysed per year. Further, 2 time periods (2007-2012 and 2013-2016) were selected representing periods before and after implementation of haemostatic resuscitation and damage control procedures in our hospital to analyse cause of death into detail.

RESULTS

11,553 trauma patients were admitted, 596 patients (5.2%) died. Mean age of deceased patients was 61 years and 61% were male. Mechanism of injury (MOI) was blunt in 98% of cases. Mean ISS was 28 with head injury the most predominant injury (mean AIS head 3.4). There was no statistically significant difference in sex and MOI over time. Even though deceased patients were older in 2016 compared to 2007 (67 vs. 46 years, p < 0.001), mortality was lower in later years (p = 0.02). CNS related injury was the main cause of death in the whole decade; 58% of patients died of CNS in 2007-2012 compared to 76% of patients in 2013-2016 (p = 0.001). In 2007-2012 9% died of exsanguination compared to 3% in 2013-2016 (p = 0.001).

DISCUSSION

In this cohort in a major trauma centre death by exsanguination has decreased to 3% of trauma deaths. The proportion of traumatic brain injury has increased over time and has become the most common cause of death in blunt trauma. Besides on-going prevention of brain injury future studies should focus on treatment strategies preventing secondary damage of the brain once the injury has occurred.

Prehospital Index provides prognosis for hospitalized patients with acute trauma

OBJECTIVE

To evaluate the prognostic value of the Prehospital Index (PHI) for hospitalized patients with acute trauma.

MATERIALS AND METHODS

PHI score and the Injury Severity Score (ISS) were determined in 1,802 hospitalized patients with acute trauma. Receiver-operator characteristic (ROC) curves were used to compare the PHI and ISS in subgroups, and corresponding prediction indicators were calculated.

RESULTS

There were significant differences in PHI score and ISS between the survival group and the death group (Z=2.674, P=0.007). The area under the ROC curve was 0.871 (95% CI 0.855-0.886) for PHI score and 0.792 (95% CI 0.773-0.811) for ISS. Optimal cutoff points to determine the risk of critical illness were PHI ≥4 and ISS ≥22. The sensitivity of the PHI was superior to the ISS (χ²=6.975, P=0.008), but the specificity and the accuracy of the PHI and ISS showed no significant difference (P>0.05).

CONCLUSION

The PHI is valuable in prognostic prediction of hospitalized patients with acute trauma, and it is superior to the ISS. The PHI has such advantages as being simple in operation, easy to learn, capable of reflecting conditions timely and reliably, and suitable for dynamic evaluation and screening for critical patients with trauma.

The association of patient and trauma characteristics with the health-related quality of life in a Dutch trauma population

Background

It is suggested in literature to use the Health Related Quality of Life (HRQoL) as an outcome indicator for evaluating trauma centre performances. In order to predict HRQoL, characteristics that could be of influence on a predictive model should be identified. This study identifies patient and injury characteristics associated with the HRQoL in a general trauma population.

Methods

Retrospective study of trauma patients admitted from 1st January 2007 through 31th December 2012. Patients were aged ≥18 years and discharged alive from the level I trauma centre. A combined health survey (SF-36 and EQ-5D) was sent to all traceable patients. The subdomain outcomes and EQ-5D index value (EQ-5Di) were compared with the reference population. A linear regression analysis was performed to identify parameters associated parameters with the HRQoL outcome.

Results

A total of 1870 patients were included for analyses. Compared to the eligible population, included patients were significantly older, more severely injured, more often admitted in the ICU and had a longer admission duration.

The SF-36 and EQ-5Di were significantly lower compared to the Dutch reference population.

The variables age, Injury Severity Score, hospital length of stay, ICU length of stay, Revised Trauma Score, probability of survival, and severe injury to the head and extremities were associated with the HRQoL in the majority of the subdomains.

Discussion

In order to use HRQoL as an indicator for trauma centre performances, there should be a consensus of the ideal timing for the measurement of HRQoL post-injury and the appropriate HRQoL instrument. Furthermore, standardised HRQoL outcomes must be developed.

Conclusion

This study revealed eight factors (described above) which could be used to predict the HRQoL in trauma patients.

Mortality prediction models in the general trauma population: A systematic review

BACKGROUND

Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population.

METHODS

An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included.

RESULTS

A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance.

CONCLUSION

Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care.

Mobile health technology transforms injury severity scoring in South Africa

BACKGROUND

The burden of data collection associated with injury severity scoring has limited its application in areas of the world with the highest incidence of trauma.

MATERIAL AND METHODS

Since January 2014, electronic records (electronic Trauma Health Records [eTHRs]) replaced all handwritten records at the Groote Schuur Hospital Trauma Unit in South Africa. Data fields required for Glasgow Coma Scale, Revised Trauma Score, Kampala Trauma Score, Injury Severity Score (ISS), and Trauma Score-Injury Severity Score calculations are now prospectively collected. Fifteen months after implementation of eTHR, the injury severity scores were compared as predictors of mortality on three accounts: (1) ability to discriminate (area under receiver operating curve, ROC); (2) ability to calibrate (observed versus expected ratio, O/E); and (3) feasibility of data collection (rate of missing data).

RESULTS

A total of 7460 admissions were recorded by eTHR from April 1, 2014 to July 7, 2015, including 770 severely injured patients (ISS > 15) and 950 operations. The mean age was 33.3 y (range 13-94), 77.6% were male, and the mechanism of injury was penetrating in 39.3% of cases. The cohort experienced a mortality rate of 2.5%. Patient reserve predictors required by the scores were 98.7% complete, physiological injury predictors were 95.1% complete, and anatomic injury predictors were 86.9% complete. The discrimination and calibration of Trauma Score-Injury Severity Score was superior for all admissions (ROC 0.9591 and O/E 1.01) and operatively managed patients (ROC 0.8427 and O/E 0.79). In the severely injured cohort, the discriminatory ability of Revised Trauma Score was superior (ROC 0.8315), but no score provided adequate calibration.

CONCLUSIONS

Emerging mobile health technology enables reliable and sustainable injury severity scoring in a high-volume trauma center in South Africa.

A survival prediction logistic regression models for blunt trauma victims in Japan

Aim

This research aimed to propose a logistic regression model for Japanese blunt trauma victims.

Methods

We tested whether the logistic regression model previously created from data registered in the Japan Trauma Data Bank between 2005 and 2008 is still valid for the data from the same data bank between 2009 and 2013. Additionally, we analyzed whether the model would be highly accurate even when its coefficients were rounded off to two decimal places.

Results

The model was proved to be highly accurate (94.56%) in the recent data (2009–2013). We also showed that the model remains valid without respiratory rate data and the simplified model would maintain high accuracy.

Conclusion

We propose the equation of survival prediction of blunt trauma victims in Japan to be Ps = 1/(1+e^−b), where b = −0.76 + 1.03 × Revised Trauma Score − 0.07 × Injury Severity Score − 0.04 × age.

Demographic Patterns and Outcomes of Patients in Level I Trauma Centers in Three International Trauma Systems

Introduction

Trauma systems were developed to improve the care for the injured. The designation and elements comprising these systems vary across countries. In this study, we have compared the demographic patterns and patient outcomes of Level I trauma centers in three international trauma systems.

Methods

International multicenter prospective trauma registry-based study, performed in the University Medical Center Utrecht (UMCU), Utrecht, the Netherlands, John Hunter Hospital (JHH), Newcastle, Australia, and Harborview Medical Center (HMC), Seattle, the United States. Inclusion: patients ≥18 years, admitted in 2012, registered in the institutional trauma registry.

Results

In UMCU, JHH, and HMC, respectively, 955, 1146, and 4049 patients met the inclusion criteria of which 300, 412, and 1375 patients with Injury Severity Score (ISS) > 15. Mean ISS was higher in JHH (13.5; p < 0.001) and HMC (13.4; p < 0.001) compared to UMCU (11.7). Unadjusted mortality: UMCU = 6.5 %, JHH = 3.6 %, and HMC = 4.8 %. Adjusted odds of death: JHH = 0.498 [95 % confidence interval (CI) 0.303–0.818] and HMC = 0.473 (95 % CI 0.325–0.690) compared to UMCU. HMC compared to JHH was 1.002 (95 % CI 0.664–1.514). Odds of death patients ISS > 15: JHH = 0.507 (95 % CI 0.300–0.857) and HMC = 0.451 (95 % CI 0.297–0.683) compared to UMCU. HMC = 0.931 (95 % CI 0.608–1.425) compared to JHH. TRISS analysis: UMCU: Ws = 0.787, Z = 1.31, M = 0.87; JHH, Ws = 3.583, Z = 6.7, M = 0.89; HMC, Ws = 3.902, Z = 14.6, M = 0.84.

Conclusion

This study demonstrated substantial differences across centers in patient characteristics and mortality, mainly of neurological cause. Future research must investigate whether the outcome differences remain with nonfatal and long-term outcomes. Furthermore, we must focus on the development of a more valid method to compare systems.

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

PURPOSE

The purpose of this study was to verify the utility of existing Trauma and Injury Severity Score (TRISS) coefficients and to propose a new prediction model with a new set of TRISS coefficients or predictors.

MATERIALS AND METHODS

Of the blunt adult trauma patients who were admitted to our hospital in 2014, those eligible for Korea Trauma Data Bank entry were selected to collect the TRISS predictors. The study data were input into the TRISS formula to obtain "probability of survival" values, which were examined for consistency with actual patient survival status. For TRISS coefficients, Major Trauma Outcome Study-derived values revised in 1995 and National Trauma Data Bank-derived and National Sample Project-derived coefficients revised in 2009 were used. Additionally, using a logistic regression method, a new set of coefficients was derived from our medical center's database. Areas under the receiver operating characteristic (ROC) curve (AUC) for each prediction ability were obtained, and a pairwise comparison of ROC curves was performed.

RESULTS

In the statistical analysis, the AUCs (0.879-0.899) for predicting outcomes were lower than those of other countries. However, by adjusting the TRISS score using a continuous variable rather than a code for age, we were able to achieve higher AUCs [0.913 (95% confidence interval, 0.899 to 0.926)].

CONCLUSION

These results support further studies that will allow a more accurate prediction of prognosis for trauma patients. Furthermore, Korean TRISS coefficients or a new prediction model suited for Korea needs to be developed using a sufficiently sized sample.

Mortality prediction using TRISS methodology in the Spanish ICU Trauma Registry (RETRAUCI)

OBJECTIVES

To validate Trauma and Injury Severity Score (TRISS) methodology as an auditing tool in the Spanish ICU Trauma Registry (RETRAUCI).

DESIGN

A prospective, multicenter registry evaluation was carried out.

SETTING

Thirteen Spanish Intensive Care Units (ICUs).

PATIENTS

Individuals with traumatic disease and available data admitted to the participating ICUs.

INTERVENTIONS

Predicted mortality using TRISS methodology was compared with that observed in the pilot phase of the RETRAUCI from November 2012 to January 2015. Discrimination was evaluated using receiver operating characteristic (ROC) curves and the corresponding areas under the curves (AUCs) (95% CI), with calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. A value of p<0.05 was considered significant.

MAIN VARIABLES OF INTEREST

Predicted and observed mortality.

RESULTS

A total of 1405 patients were analyzed. The observed mortality rate was 18% (253 patients), while the predicted mortality rate was 16.9%. The area under the ROC curve was 0.889 (95% CI: 0.867-0.911). Patients with blunt trauma (n=1305) had an area under the ROC curve of 0.887 (95% CI: 0.864-0.910), and those with penetrating trauma (n=100) presented an area under the curve of 0.919 (95% CI: 0.859-0.979). In the global sample, the HL test yielded a value of 25.38 (p=0.001): 27.35 (p<0.0001) in blunt trauma and 5.91 (p=0.658) in penetrating trauma. TRISS methodology underestimated mortality in patients with low predicted mortality and overestimated mortality in patients with high predicted mortality.

CONCLUSIONS

TRISS methodology in the evaluation of severe trauma in Spanish ICUs showed good discrimination, with inadequate calibration - particularly in blunt trauma.