Improved prediction from revised injury severity classification (RISC) over trauma and injury severity score (TRISS) in an independent evaluation of major 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.

Comparative analysis of MGAP, GAP, and RISC2 as predictors of patient outcome and emergency interventional need in emergency room treatment of the injured

PURPOSE

Little is known about the capabilities of triage and risk scores to predict the outcomes of injured patients, other than mortality, or to determine the need for trauma center resources.

METHODS

Retrospective analysis of prospectively gathered monocenter data on consecutively admitted adult emergency room trauma patients. For each patient, the GAP (Glasgow Coma Scale, Age and Pressure), MGAP (mechanism + GAP) scores and the revised injury severity classification 2 (RISC2) were calculated. The predictive performance of these scores was compared for the assessment of trauma severity, hospital resource need and early patient outcomes (area under the receiver operator characteristics, AUROC).

RESULTS

2112 patients were evaluated [mean age 49.1 years; Injury Severity Score (ISS) 9.5]. GAP, MGAP, and RISC2 worked best at predicting mortality (AUROC 93.2, 93.5 and 96.1%, respectively). Other endpoints such as ISS > 15, emergency interventions, disability status, and return-not-home were predicted less precisely by these three scores, better by RISC2 (AUROC range 66.2-88.8%) than by (M)GAP-scores (55.2-84.1%), except for preclinical interventions. Over- and undertriage rates for the (M)GAP scores varied between 27.5-53.4% and 10.4-30%, respectively.

CONCLUSION

The almost comparable precision of the three risk scores in the prediction of outcome or interventional need following trauma, and the fact, that the RISC2 can only be calculated following extensive diagnostics, favor earlier applicable (M)GAP scoring in the emergency setting. Overall, due to its easier use, the GAP appears to be the most preferable for the early assessment and triage of the injured in a trauma setting based on this European trauma center experience (NCT02165137).

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.

Unconscious trauma patients: outcome differences between southern Finland and Germany-lesson learned from trauma-registry comparisons

PURPOSE

International trauma registry comparisons are scarce and lack standardised methodology. Recently, we performed a 6-year comparison between southern Finland and Germany. Because an outcome difference emerged in the subgroup of unconscious trauma patients, we aimed to identify factors associated with such difference and to further explore the role of trauma registries for evaluating trauma-care quality.

METHODS

Unconscious patients [Glasgow Coma Scale (GCS) 3-8] with severe blunt trauma [Injury Severity Score (ISS) ≥16] from Helsinki University Hospital's trauma registry (TR-THEL) and the German Trauma Registry (TR-DGU) were compared from 2006 to 2011. The primary outcome measure was 30-day in-hospital mortality. Expected mortality was calculated by Revised Injury Severity Classification (RISC) score. Patients were separated into clinically relevant subgroups, for which the standardised mortality ratios (SMR) were calculated and compared between the two trauma registries in order to identify patient groups explaining outcome differences.

RESULTS

Of the 5243 patients from the TR-DGU and 398 from the TR-THEL included, nine subgroups were identified and analyzed separately. Poorer outcome appeared in the Finnish patients with penetrating head injury, and in Finnish patients under 60 years with isolated head injury [TR-DGU SMR = 1.06 (95 % CI = 0.94-1.18) vs. TR-THEL SMR = 2.35 (95 % CI = 1.20-3.50), p = 0.001 and TR-DGU SMR = 1.01 (95 % CI = 0.87-1.16) vs. TR-THEL SMR = 1.40 (95 % CI = 0.99-1.81), p = 0.030]. A closer analysis of these subgroups in the TR-THEL revealed early treatment limitations due to their very poor prognosis, which was not accounted for by the RISC.

CONCLUSION

Trauma registry comparison has several pitfalls needing acknowledgement: the explanation for outcome differences between trauma systems can be a coincidence, a weakness in the scoring system, true variation in the standard of care, or hospitals' reluctance to include patients with hopeless prognosis in registry. We believe, however, that such comparisons are a feasible method for quality control.

Comparative effectiveness of helicopter emergency medical services compared to ground emergency medical services

The use of helicopter emergency medical services (HEMS) for the transportation and treatment of trauma patients, while commonplace in most developed nations, remains controversial. The purported beneficial effects of HEMS compared to ground emergency medical services is likely to be some combination of speed, crew expertise, and the fact that HEMS is part of an organized trauma system. When the HEMS literature is assessed as a whole, considerable heterogeneity of effects and study methodologies preclude an accurate estimate of composite effect. However, when the outcome of mortality is studied using advanced multivariable regression techniques to control for multiple known confounders, an improved odds of survival has been repeatedly demonstrated. Future HEMS research must rely on robust observational study designs and assessments of a variety of patient outcomes. Questions about the role of speed, distance, and other potentially beneficial elements of HEMS remain.

Trauma survival prediction in Asian population: a modification of TRISS to improve accuracy

The probability of survival (PS) in blunt trauma as calculated by Trauma and Injury Severity Score (TRISS) has been an indispensable tool in trauma audit. The aim of this study is to explore the predictive performance of the latest updated TRISS model by investigating the Age variable recategorisations and application of local Injury Severity Score (ISS) and Revised Trauma Score (RTS) coefficients in a logistic model using a level I trauma centre database involving Asian population.

METHODS

Prospectively and consecutively collected 5684 trauma patients' data over a 10-year period at a regional level I trauma centre were reviewed. Four modified TRISS (mTRISS) models using Age coefficient from reclassifications of the Age variable according to their correlation with survival by logistic regression on the local dataset were acquired. RTS and ISS coefficients were derived from the local dataset and then applied to the mTRISS models. mTRISS models were compared with the existing Major Trauma Outcome Study (MTOS)-derived TRISS (eTRISS) model. Model 1=Age effect taken as linear; Model 2=Age classified into two groups (0-54, 55+); Model 3=Age classified into four groups (0-15, 16-54, 55-79, 80+) and Model 4=Age classified into two groups (0-69, 70+). Performance measures including sensitivity, specificity, accuracy and area under the Receiver Operating Characteristic (ROC) curve were used to assess the various models. The cross-validation procedure consisted of comparing the P(S) obtained from mTRISS Models 1 and 2 with the P(S) obtained from the MTOS derived from eTRISS.

RESULTS

A 5147 blunt trauma patients' dataset was reviewed. Model 1, where Age was taken as a scale variable, demonstrated a substantial improvement in the survival prediction with 91.6% accuracy in blunt injuries as compared with 89.2% in the MTOS-derived TRISS. The 95% CI for ROC derived from mTRISS Model 1 was (0.923, 0.940), when compared with the hypothesised ROC value 0.886 obtained from eTRISS, it clearly indicated a significant improvement in predicting survival at 5% level. Furthermore, ROCs have shown clearly the superiority of Model 1 over Model 2, and of Model 2 over MTOS-derived TRISS. The recategorisation of the Age variable (Models 3 and 4) also demonstrated improved performance, but their strength was not as intense as in Model 1. Overall, the results point to the adoption of Model 1 as the best model for PS. Cross-validation analysis has further assured the validity of these findings.

CONCLUSIONS

The present study has demonstrated that (1) having the Age variable being dichotomised (cut-off at 55 years) as in the eTRISS, but with the application of a local dataset-derived coefficients give better TRISS survival prediction in Asian blunt trauma patients; (2) improved performance are found with certain recategorisation of the Age variable and (3) the accuracy can further be enhanced if the Age effect is taken to be linear, with the application of local dataset-derived coefficients.