TRISS unexpected survivors: an outdated standard?

Location of Injury and Other Factors Associated With Increased Survival Among Severely Injured Geriatric Trauma Patients

BACKGROUND

Patients with a Trauma Injury Severity Score (TRISS) < .5 are termed "unexpected survivors." There is scarce information published on this subset of geriatric patients whose survival is an anomaly.

METHODS

This is a retrospective case-control study examining all geriatric patients (age ≥65) not expected to survive (TRISS<.5) in the Pennsylvania Trauma Outcome Study (PTOS) database from 2013 to 2017. Primary outcome was survival to discharge. We selected 10 clinically important variables for logistic regression analysis as possible factors that may improve survival.

RESULTS

1336 patients were included, 395 (29.6%) were unexpected survivors. Factors that improved survival odds are the following: Place of injury: street/highway (AOR:0.51; 95% CI: .36-.73, P < .001) and residential institution (AOR:0.46; 95% CI: .21-.98, P = .043); and presence of Benzodiazepines (AOR:0.49; 95% CI: .31-.77, P = .002) or ethanol (AOR:0.57; 95% CI: .34-.97, P = .040). Factors that decreased survival odds are the following: Hypotension (AOR: 8.59; 95% CI: 4.33-17.01, P < .001) and hypothermia (AOR: 1.58; 95% CI: 1.10-2.28, P = .014). Gender, race/ethnicity, blood transfusion in first 24 hours, shift of presentation to Emergency Department, place of injury (farm, industrial, recreational, or public building), use of Tetrahydrocannabinol, amphetamines or opioids, and level of trauma activation did not impact survival.

DISCUSSION

Location of injury (street/highway and residential institution) and ethanol or benzodiazepine use led to a significant increased survival in severely injured geriatric patients. Hypotension and hypothermia led to decreased survival. Future studies should determine possible reasons these factors lead to survival (and identify additional factors) to focus efforts in these areas to improve outcomes in geriatric trauma.

Machine Learning Models of Survival Prediction in Trauma Patients

BACKGROUND

We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS).

METHODS

Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves.

RESULTS

In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models.

CONCLUSIONS

These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

Acute Physiology and Chronic Health Evaluation II Score and Sequential Organ Failure Assessment Score as Predictors for Severe Trauma Patients in the Intensive Care Unit

Background

The Acute Physiology and Chronic Health Evaluation (APACHE) II scoring system and the Sequential Organ Failure Assessment (SOFA) scoring system are widely used for critically ill patients. We evaluated whether APACHE II score and SOFA score predict the outcome for trauma patients in the intensive care unit (ICU).

Methods

We retrospectively analyzed trauma patients admitted to the ICU in a single trauma center between January 2014 and December 2015. The APACHE II score was figured out based on the data acquired from the first 24 hours of admission; the SOFA score was evaluated based on the first 3 days in the ICU. A total of 241 patients were available for analysis. Injury Severity score, APACHE II score, and SOFA score were evaluated.

Results

The overall survival rate was 83.4%. The non-survival group had a significantly high APACHE II score (24.1 ± 8.1 vs. 12.3 ± 7.2, P < 0.001) and SOFA score (7.7 ± 1.7 vs. 4.3 ± 1.9, P < 0.001) at admission. SOFA score had the highest areas under the curve (0.904). During the first 3 days, SOFA score remained high in the non-survival group. In the non-survival group, cardiovascular system, neurological system, renal system, and coagulation system scores were significantly higher.

Conclusions

In ICU trauma patients, both SOFA and APACHE II scores were good predictors of outcome, with the SOFA score being the most effective. In trauma ICU patients, the trauma scoring system should be complemented, recognizing that multi-organ failure is an important factor for mortality.

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.

Injury severity scoring systems: a review of application to practice

AIM

This study reviews the historical development of injury severity scoring systems and their application to clinical practice.

BACKGROUND

A variety of injury severity scoring systems have been developed and applied since more than four decades. It is increasingly important for nurses to be familiar with these scoring systems, their strengths and weaknesses, and their applications to nursing practice.

DESIGN

Systematic literature review.

METHODS

The injury severity scoring systems developed from the 1970s to 2011 were identified via electronic database searches, footnote chasing and contact with clinical experts. The most frequently used scoring systems in the literature were classified according to the criteria used in each scoring system.

CONCLUSIONS

All injury severity scoring systems are valuable but have certain problems. A universal scoring system applicable for various purposes appears difficult to achieve. However, the understanding and proper use of scoring systems will allow us to perform critical evaluations and continual refinement of trauma management.

RELEVANCE TO CLINICAL PRACTICE

As nurses and researchers, it is critical that we should know the application of these injury severity scoring systems to ensure their quality and appropriate utilization.

Comparison of the Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II scoring system, and Trauma and Injury Severity Score method for predicting the outcomes of intensive care unit trauma patients

PURPOSE

The aim of this study was to assess the ability of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) II scoring system, and Trauma and Injury Severity Score (TRISS) method to predict group mortality for intensive care unit (ICU) trauma patients.

METHODS

The medical records of 706 consecutive major trauma patients admitted to the ICU of Samsung Changwon Hospital from May 2006 to April 2010 were retrospectively examined. The SOFA and the APACHE II scores were calculated based on data from the first 24 hours of ICU admission, and the TRISS was calculated using initial laboratory data from the emergency department and operative data. The probability of death was calculated for each patient based on the SOFA score, APACHE II score, and TRISS equations. The ability to predict group mortality for the SOFA score, APACHE II score, and TRISS method was assessed by using 2-by-2 decision matrices and receiver operating characteristic curve analysis and calibration analysis.

RESULTS

In 2-by-2 decision matrices with a decision criterion of 0.5, the sensitivities, specificities, and accuracies were 74.1%, 97.1%, and 92.4%, respectively, for the SOFA score; 58.5%, 99.6%, and 91.1%, respectively, for the APACHE II scoring system; and 52.4%, 94.8%, and 86.0%, respectively, for the TRISS method. In the receiver operating characteristic curve analysis, the areas under the curve for the SOFA score, APACHE II scoring system, and TRISS method were 0.953, 0.950, and 0.922, respectively.

CONCLUSION

The results from the present study showed that the SOFA score was not different from APACHE II scoring system and TRISS in predicting the outcomes for ICU trauma patients. However, the method for calculating SOFA scores is easier and simpler than APACHE II and TRISS.

Evaluation of trauma care using TRISS method: the role of adjusted misclassification rate and adjusted w-statistic

Background

Major trauma is a leading cause of death worldwide. Evaluation of trauma care using Trauma Injury and Injury Severity Score (TRISS) method is focused in trauma outcome (deaths and survivors). For testing TRISS method TRISS misclassification rate is used. Calculating w-statistic, as a difference between observed and TRISS expected survivors, we compare our trauma care results with the TRISS standard.

Aim

The aim of this study is to analyze interaction between misclassification rate and w-statistic and to adjust these parameters to be closer to the truth.

Materials and methods

Analysis of components of TRISS misclassification rate and w-statistic and actual trauma outcome.

Results

The component of false negative (FN) (by TRISS method unexpected deaths) has two parts: preventable (Pd) and non-preventable (nonPd) trauma deaths. Pd represents inappropriate trauma care of an institution; otherwise nonpreventable trauma deaths represents errors in TRISS method. Removing patients with preventable trauma deaths we get an Adjusted misclassification rate: (FP + FN - Pd)/N or (b+c-Pd)/N. Substracting nonPd from FN value in w-statistic formula we get an Adjusted w-statistic: [FP-(FN - nonPd)]/N, respectively (FP-Pd)/N, or (b-Pd)/N).

Conclusion

Because adjusted formulas clean method from inappropriate trauma care, and clean trauma care from the methods error, TRISS adjusted misclassification rate and adjusted w-statistic gives more realistic results and may be used in researches of trauma outcome.

The Utstein template for uniform reporting of data following major trauma: a joint revision by SCANTEM, TARN, DGU-TR and RITG

BACKGROUND

In 1999, an Utstein Template for Uniform Reporting of Data following Major Trauma was published. Few papers have since been published based on that template, reflecting a lack of international consensus on its feasibility and use. The aim of the present revision was to further develop the Utstein Template, particularly with a major reduction in the number of core data variables and the addition of more precise definitions of data variables. In addition, we wanted to define a set of inclusion and exclusion criteria that will facilitate uniform comparison of trauma cases.

METHODS

Over a ten-month period, selected experts from major European trauma registries and organisations carried out an Utstein consensus process based on a modified nominal group technique.

RESULTS

The expert panel concluded that a New Injury Severity Score > 15 should be used as a single inclusion criterion, and five exclusion criteria were also selected. Thirty-five precisely defined core data variables were agreed upon, with further division into core data for Predictive models, System Characteristic Descriptors and for Process Mapping.

CONCLUSION

Through a structured consensus process, the Utstein Template for Uniform Reporting of Data following Major Trauma has been revised. This revision will enhance national and international comparisons of trauma systems, and will form the basis for improved prediction models in trauma care.

Evaluation of trauma care applying TRISS methodology in a Caribbean developing country

There have been conflicting reports regarding the applicability of Trauma Injury Severity Score (TRISS) methodology to evaluate trauma care in a developing country setting. The objective of this study was to apply TRISS methodology to evaluate trauma care in the public hospitals of a Caribbean developing country. A prospective, observational study was conducted in the three major general hospitals in Trinidad. Major trauma patients were included. Demographic data, waiting time in the Emergency Department, and nature of injury (blunt or penetrating) were noted. Revised Trauma Score, Injury Severity Score, and Glasgow Coma Scale were applied to all patients on admission. Hospital outcomes were noted. Predicted outcomes were calculated for adult patients using TRISS methodology. M, Z statistics and receiver operating characteristic (ROC) curve analysis were done. There were 326 trauma patients studied, of whom 279 adults were evaluated by the TRISS methodology. Men were more frequently involved in trauma than women; there was more blunt trauma than penetrating trauma. The M statistic was 0.98 and the overall Z statistic was 5.81. The ROC curve analysis showed TRISS to be a fair discriminator in the study case-mix with an area under the curve of 0.82 (95% confidence interval 0.69-0.96). There is a considerable disparity between predicted and observed outcomes when trauma patients are evaluated by the TRISS methodology in a developing country setting.

Use of scene vital signs improves TRISS predicted survival in intubated trauma patients

INTRODUCTION

The Trauma Related Injury Severity Score (TRISS) has been previously validated to predict outcomes in nonintubated, nonparalyzed trauma patients. The purpose of this study was to assess the impact of scene vital signs on predicting survival in intubated trauma patients.

METHODS

Our Trauma Registry of the American College of Surgeons was reviewed for all trauma patients admitted between 10/01/04 and 09/30/06, arriving by aeromedical transport. TRISS was evaluated for each patient based on their (1) scene vital signs and (2) arrival vital signs. Additionally, the "TRISS-like" score was calculated for each patient. Expected mortality for each score was measured against observed mortality.

RESULTS

Four thousand four hundred ninety-nine Trauma Registry of the American College of Surgeons patients were admitted during the study period; 695 (15%) were transported by air; 163 patients (23%) arrived intubated; 480 arrived nonintubated. Observed survival in the intubated group was 76%. Observed survival in the nonintubated group was 100%. TRISS using scene vital signs more closely predicted mortality among intubated patients than the other scoring systems (69% versus 39% using TRISS-arrival versus 80% using TRISS-like). Scene vital signs with TRISS also resulted in fewer "unexpected" outcomes (survivors and deaths).

CONCLUSIONS

Traditionally, patients arriving at trauma centers intubated are either excluded from the trauma registry or have their physiological score "modified" to account for pharmacologically altered respiratory rate and Glasgow Coma Scale. In intubated patients, TRISS using scene vital signs more reliably predicts survival and does so with far fewer "unexpected" outcomes than with other available scoring systems.

Risk Scoring in Perioperative and Surgical Intensive Care Patients: A Review

PURPOSE

Assessing the risk and predicting the outcome of surgery, trauma, and surgical intensive care is an important aspect of perioperative practice. There have been attempts to devise and validate many scoring systems to predict the prognosis of patients having a similar severity of illness. This article reviews some of the commonly used systems with respect to their development, strengths, and limitations.

SOURCES

Published literature describing risk assessment scores and physiologic scoring systems for preoperative assessment, trauma, and surgical intensive care patients.

PRINCIPAL FINDINGS

Risk scores used in preoperative evaluation assist the clinician in optimizing the patient before, during, and after surgery. Scoring systems applied in intensive care units are useful as guidelines rather than accurate predictors of prognosis for individual patient. Many models are used for audit purposes, and some are used as performance measures and quality indicators of a unit; however, both utilities are controversial because of poor adjustment of these systems to case-mixtures.

CONCLUSIONS

Risk assessment scores may assist in the perioperative risk evaluation with respect to organ systems. Prognostication of critically ill patients belonging to a category of illness may be done using physiological scoring systems taking into account the difference in the case-mix of the particular unit.

Prediction of Outcomes in Trauma: Anatomic or Physiologic Parameters?

BACKGROUND

Prediction of outcomes after injury has traditionally incorporated measures of injury severity, but recent studies suggest that including physiologic and shock measures can improve accuracy of anatomic-based models. A recent single-institution study described a mortality predictive equation [f(x) = 3.48 - .22 (GCS) - .08 (BE) + .08 (Tx) + .05 (ISS) + .04 (Age)], where GSC is Glasgow Coma Score, BE is base excess, Tx is transfusion requirement, and ISS is Injury Severity Score, which had 63% sensitivity, 94% specificity, (receiver operating characteristic [ROC] 0.96), but did not provide comparative data for other models. We have previously documented that the Physiologic Trauma Score, including only physiologic variables (systemic inflammatory response syndrome, Glasgow Coma Score, age) also accurately predicts mortality in trauma. The objective of this study was to compare the predictive abilities of these statistical models in trauma outcomes.

METHODS

Area under the ROC curve of sensitivity versus 1-specificity was used to assess predictive ability and measured discrimination of the models.

RESULTS

The study cohort consisted of 15,534 trauma patients (80% blunt mechanism) admitted to a Level I trauma center over a 3-year period (mean age 37 +/- 18 years; mean Injury Severity Score 10 +/- 10; mortality 4%). Sensitivity of the new predictive model was 45%, specificity was 96%, ROC was 0.91, validating this new trauma outcomes model in our institution. This was comparable with area under the ROC for Revised Trauma Score (ROC 0.88), Trauma and Injury Severity Score (ROC 0.97), and Physiologic Trauma Score (ROC 0.95), but superior compared with admission Glasgow Coma Score (ROC 0.79), Injury Severity Score (ROC 0.79), and age (ROC 0.60).

CONCLUSIONS

The predictive ability of this new model is superior to anatomic-based models such as Injury Severity Score, but comparable with other physiologic-based models such as Revised Trauma Score, Physiologic Trauma Score and Trauma, and Injury Severity Score.

Prognostic Value of an Early Soluble L-Selectin (sCD62L) Assay for Risk Assessment in Blunt Multiple Trauma: A Metaanalysis

Background: After severe trauma, decreased plasma concentrations of the soluble adhesion molecule L-selectin (sCD62L) have been linked to an increased incidence of lung failure and multiorgan dysfunction syndrome (MODS). Individual studies have had conflicting results, however. We examined multiple studies in an attempt to determine whether early sCD62L concentrations are predictive of major complications after severe trauma.

Methods: We performed a systematic review of six electronic databases and a manual search for clinical studies comparing outcomes of multiply injured patients (Injury Severity Score ≥16) depending on their early sCD62L blood concentrations. Because of various outcome definitions, acute lung injury (ALI) and adult respiratory distress syndrome (ARDS) were studied as a composite endpoint. Weighted mean differences (WMDs) in sCD62L concentrations were calculated between individuals with and without complications by fixed- and random-effects models.

Results: Altogether, 3370 citations were identified. Seven prospective studies including 350 patients were eligible for data synthesis. Published data showed the discriminatory features of sCD62L but did not allow for calculation of measures of test accuracy. Three of four studies showed lower early sCD62L concentrations among individuals progressing to ALI and ARDS (WMD = −229 μg/L; 95% confidence interval, −476 to 18 μg/L). No differences in sCD62L concentrations were noted among patients with or without later MODS. Nonsurvivors had significantly lower early sCD62L plasma concentrations (WMD = 121 μg/L; 95% confidence interval, 63–179 μg/L), but little information was available on potential confounders in this group.

Conclusions: Early decreased soluble L-selectin concentrations after multiple trauma may signal an increased likelihood of lung injury and ARDS. The findings of this metaanalysis warrant a large cohort study to develop selectin-based models targeting the risk of inflammatory complications.