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

Beyond Mortality: Does Trauma-related Injury Severity Score Predict Complications or Lengths of Stay Using a Large Administrative Dataset

Introduction:

Despite its shortcomings, trauma-related injury severity score (TRISS) correlates well with mortality in large trauma datasets. The aim of this study was to determine if TRISS correlates with morbidity and hospital lengths of stay using data from an institutional registry at a Level I Trauma Center. We hypothesized that higher TRISS correlates with increased complications and longer hospital stays.

Methods:

A retrospective review of our institutional registry was performed, examining all trauma admissions between January 1999 and June 30, 2015. Out of a total of 32,026 patient records, TRISS data were available in 23,205 cases. Abstracted data included patient age, gender, ISS, TRISS, presence of complication, Glasgow Coma Scale (GCS), hospital length of stay, intensive care unit LOS, step-down unit LOS, functional independence measure, and 30-day mortality.

Results:

TRISS was highly predictive of mortality, with the AUC value of 0.95 (95% confidence interval 0.936–0.954, P < 0.01) compared to ISS (AUC 0.794), GCS (AUC 0.827), and age (AUC 0.650). TRISS also performed better than the other variables in terms of the ability to predict morbidity events (AUC 0.813). TRISS was comparable to ISS in terms of prediction of ICU admission (AUC 0.801 versus 0.811, respectively). After correcting for patient age and gender, higher TRISS significantly correlated with longer hospital stays .

Conclusions:

Despite previous criticisms, we found that TRISS is superior to ISS for mortality and morbidity prediction. TRISS correlated significantly with a hospital, step down, and ICU lengths of stay using a large administrative dataset.

External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan

In Japan, a range of patients with traumatic brain injury (TBI) has been recorded in a nationwide database (Japan Neurotrauma Data Bank; JNTDB). This study aimed to externally validate three international prediction models using JNTDB data: Trauma and Injury Severity Score (TRISS), Corticosteroid Randomization After Significant Head Injury (CRASH), and International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT). We also aimed to validate the applicability of these models in the Japanese population. Of 1,091 patients registered in the JNTDB from July 2009 to June 2011, we analyzed data for 635 patients. We examined factors associated with mortality in-hospital and unfavorable outcomes 6 months after TBI by applying the TRISS, CRASH, and IMPACT models. We also conducted an external validation of these models based on these data. The patients’ mean age was 60.1 ±21.1 years, and 342 were alive at the time of discharge (53.9%). Univariate analysis revealed eight major risk factors for mortality in-hospital: age, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), systolic blood pressure, heart rate, mydriasis, acute epidural hematoma (AEDH), and traumatic subarachnoid hemorrhage. A similar analysis identified five risk factors for unfavorable outcomes at 6 months: age, GCS, ISS, mydriasis, and AEDH. For mortality in-hospital, the TRISS had a satisfactory area under the curve value (0.75). For unfavorable outcomes at 6 months, the CRASH (basic and computed tomography) and IMPACT (core and core extended) models had satisfactory area under the curve values (0.86, 0.86, 0.81, and 0.85, respectively). The TRISS, CRASH, and IMPACT models were suitable for application to the JNTDB population, indicating these models had high value in Japanese patients with neurotrauma.

New Trauma and Injury Severity Score (TRISS) adjustments for survival prediction

Background

The objective of this study is to propose three new adjustments to the Trauma and Injury Severity Score (TRISS) equation and compare their performances with the original TRISS as well as this index with coefficients adjusted for the study population.

Methods

This multicenter, retrospective study evaluated trauma victims admitted to two hospitals in São Paulo-Brazil and San Diego-EUA between January 1st, 2006, and December 31st, 2010. The proposed models included a New Trauma and Injury Severity Score (NTRISS)-like model that included Best Motor Response (BMR), systolic blood pressure (SBP), New Injury Severity Score (NISS), and age variables; a TRISS peripheral oxygen saturation (SpO₂) model that included Glasgow Coma Scale (GCS), SBP, SpO₂, Injury Severity Score, and age variables; and a NTRISS-like SpO₂ model that included BMR, SBP, SpO₂, NISS, and age variables. All equations were adjusted for blunt and penetrating trauma coefficients. The model coefficients were established by logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the models.

Results

The original TRISS (area under the curve (AUC) = 0.90), TRISS with adjusted coefficients (AUC = 0.89), and the new proposals (NTRISS-like, TRISS SpO₂, and NTRISS-like SpO₂) showed no difference in performance (AUC = 0.89, 0.89, and 0.90, respectively).

Conclusions

The new models demonstrated good accuracy and similar performance to the original TRISS and TRISS adjusted for coefficients in the study population; therefore, the new proposals may be useful for the assessments of quality of care in trauma patients using variables that are routinely measured and recorded.

Performance of new adjustments to the TRISS equation model in developed and developing countries

BACKGROUND

The Trauma and Injury Severity Score (TRISS) has been criticized for being based on data from the USA and Canada-high-income countries-and therefore, it may not be applicable to low-income and middle-income countries. The present study evaluated the accuracy of three adjustments to the TRISS equation model (NTRISS-like; TRISS SpO2; NTRISS-like SpO2) in a high-income and a middle-income country to compare their performance when derived and applied to different groups.

METHODS

This was a retrospective study of trauma patients admitted to two institutions: a university medical center in São Paulo, Brazil (a middle-income country), and a level 1 university trauma center in San Diego, USA (a high-income country). Patients were admitted between January 1, 2006, and December 31, 2010. The subjects were 2416 patients from Brazil and 8172 patients from the USA. All equations had adjusted coefficients for São Paulo and San Diego and for blunt and penetrating trauma. Receiver operating characteristic (ROC) curves were used to evaluate performance of the models.

RESULTS

Regardless of the population where the equation was generated, it performed better when applied to patients in the USA (AUC from 0.911 to 0.982) compared to patients in Brazil (AUC from 0.840 to 0.852). When the severity was considered and homogenized, the performance of equations were similar to both application in the USA and Brazil.

CONCLUSIONS

Survival probability models showed better performance when applied in data collected in the high-income countries (HIC) regardless the country they were derived. The severity is an important factor to consider when using non-adjusted survival probability models for the local population. Adjusted models for severely traumatized patients better predict survival probability in less severely traumatized populations. Other factors besides physiological and anatomical data may impact final outcomes and should be identified in each environment if they are to be used in the development of the trauma care performance improvement process in middle-income countries.

Desempenho dos ajustes do Trauma and Injury Severity Score (TRISS): revisão integrativa

RESUMO Objetivo identificar estudos que realizaram ajustes na equação do Trauma and InjurySeverity Score (TRISS) e compararam a capacidade discriminatória da equação modificada com a original. Método Revisão integrativa de pesquisas publicadas entre 1990 e 2014 nas bases de dados LILACS, MEDLINE, PubMed e SciELO utilizando-se a palavra TRISS. Resultados foram incluídos 32 estudos na revisão. Dos 67 ajustes de equações do TRISS identificados, 35 (52,2%) resultaram em melhora na acurácia do índice para predizer a probabilidade de sobrevida de vítimas de trauma. Ajustes dos coeficientes do TRISS à população de estudo foram frequentes, mas nem sempre melhoraram a capacidade preditiva dos modelos analisados. A substituição de variáveis fisiológicas do Revised Trauma Score (RTS) e modificações do Injury Severity Score (ISS) na equação original tiveram desempenho variado. A mudança na forma de inclusão da idade na equação, assim como a inserção do gênero, comorbidades e mecanismo do trauma apresentaram tendência de melhora do desempenho do TRISS. Conclusão Diferentes propostas de ajustes no TRISS foram identificadas nesta revisão e indicaram, principalmente, fragilidades do RTS no modelo original e necessidade de alteração da forma de inclusão da idade na equação para melhora da capacidade preditiva do índice.

Effect of Massage Therapy on Vital Signs and GCS Scores of ICU Patients: A Randomized Controlled Clinical Trial

BACKGROUND

Unalleviated complications related to hospitalization, including stress, anxiety, and pain, can easily influence different structures, like the neural system, by enhancing the stimulation of sympathetic nervous pathways and causing unstable vital signs and deterioration in the level of consciousness.

OBJECTIVES

The purpose of this study was to determine the effects of massage therapy by family members on vital signs and Glasgow Coma Scale Score (GCS) of patients hospitalized in the Intensive Care Unit (ICU).

PATIENTS AND METHODS

This randomized controlled clinical trial was conducted at the ICU of the Shariati Hospital during 2012; 45 ICU patients and 45 family members in the experimental group and the same number of patients and family members in the control group were consecutively selected . The data collection instrument consisted of two parts. The first part included demographic data (age, marital status and Body Mass Index) and the second part included a checklist to record the patient's vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate (RR), pulse rate (PR)) and GCS. All measurements were done at the same time in both groups before the intervention (full body massage therapy), and 1 hour, 2 hours, 3 hours, and 4 hours after intervention. The patients were provided with a 60-minute full body massage The massage protocol included static, surface tension, stretching, superficial lymph unload, transverse friction, and myofacial releasing techniques.

RESULTS

Significant differences were observed between experimental and control groups in the SBP at 1 hour, SBP 2 hours, and SBP 3 hours, and also in GCS at 1 hour to GCS at 4 hours (P < 0.05). Multivariate analysis revealed a significant difference between experimental and control groups in SBP at all time points (P < 0.05).

CONCLUSIONS

Massage via family members had several positive effects on the patients' clinical conditions, and therefore, it should be recognized as one of the most important clinical considerations in hospitalized patients.

Identification of dynamic prehospital changes with continuous vital signs acquisition

OBJECTIVE

In most trauma registries, prehospital trauma data are often missing or unreliable because of the difficult dual task consigned to prehospital providers of recording vital signs and simultaneously resuscitating patients. The purpose of this study was to test the hypothesis that the analysis of continuous vital signs acquired automatically, without prehospital provider input, improves vital signs data quality, captures more extreme values that might be missed with conventional human data recording, and changes Trauma Injury Severity Scores compared with retrospectively compiled prehospital trauma registry data.

METHODS

A statewide vital signs collection network in 6 medevac helicopters was deployed for prehospital vital signs acquisition using a locally built vital signs data recorder (VSDR) to capture continuous vital signs from the patient monitor onto a memory card. VSDR vital signs data were assessed by 3 raters, and intraclass correlation coefficients were calculated to test interrater reliability. Agreement between VSDR and trauma registry data was compared with the methods of Altman and Bland including corresponding calculations for precision and bias.

RESULTS

Automated prehospital continuous VSDR data were collected in 177 patients. There was good agreement between the first recorded vital signs from the VSDR and the trauma registry value. Significant differences were observed between the highest and lowest heart rate, systolic blood pressure, and pulse oximeter from the VSDR and the trauma registry data (P< .001). Trauma Injury Severity Scores changed in 12 patients (7%) when using data from the VSDR.

CONCLUSION

Real-time continuous vital signs monitoring and data acquisition can identify dynamic prehospital changes, which may be missed compared with vital signs recorded manually during distinct prehospital intervals. In the future, the use of automated vital signs trending may improve the quality of data reported for inclusion in trauma registries. These data may be used to develop improved triage algorithms aimed at optimizing resource use and enhancing patient outcomes.

Does scene physiology predict helicopter transport trauma admission?

BACKGROUND

Helicopter transport (HT) is necessary in the management of civilian trauma; however, its significant expense underscores the need to minimize overuse and inefficiency. Our objective was to determine whether on-scene physiologic criteria predict appropriate triage in HT trauma patients.

METHODS

We performed a retrospective review of patients flown from the injury scene to the emergency department of a level 1 trauma center by a university HT service from January 2006 to December 2010. Demographics, mechanism of injury, scene revised trauma score (RTS), travel distance, trauma alert level, payer status, emergency department and hospital disposition, and injury severity scores were queried from the electronic medical record and Trauma Registry of the American College of Surgeons with similar data on patients admitted because of trauma by ground transport for comparison. Proper triage criteria were defined through by the American College of Surgeons Committee on Trauma.

RESULTS

We identified 2522 HT patients. Of these, 1491 (59%) were properly triaged and 1031 (41%) were overtriaged. Univariate analysis revealed that the mean scene RTS was significantly higher for over- versus proper triage (7.68 ± 0.67 and 6.97 ± 1.57 respectively, P < 0.001). Neither the scene RTS nor travel distance predicted the triage criteria in a regression model (odds ratio 0.37, 95% confidence interval 0.16-0.85, and odds ratio 0.67, 95% confidence interval 0.60-0.74, respectively). Compared with ground transport, admitted HT patients had significantly more blunt trauma, lower scene RTSs, higher injury severity scores, more intensive care unit and ventilator days, a longer length of stay, and a greater travel distance and were more likely to be intubated (P < 0.001).

CONCLUSIONS

The physiological criteria did not predict the triage status in HT trauma patients. Although >40% of HT patients were overtriaged, they were more severely injured and required greater institutional resources than did the ground transport patients. Overtriage by a helicopter transport program might be appropriate.

Impact of a physician-staffed helicopter on a regional trauma system: a prospective, controlled, observational study

INTRODUCTION

This study aims to compare the trauma system before and after implementing a physician-staffed helicopter emergency medical service (PS-HEMS). Our hypothesis was that PS-HEMS would reduce time from injury to definitive care for severely injured patients.

METHODS

This was a prospective, controlled, observational study, involving seven local hospitals and one level I trauma centre using a before and after design. All patients treated by a trauma team within a 5-month period (1 December 2009-30 April 2010) prior to and a 12-month period (1 May 2010-30 April 2011) after implementing a PS-HEMS were included. We compared time from dispatch of the first ground ambulance to arrival in the trauma centre for patients with Injury Severity Score (ISS) > 15. Secondary end points were the proportion of secondary transfers and 30-day mortality.

RESULTS

We included 1788 patients, of which 204 had an ISS > 15. The PS-HEMS transported 44 severely injured directly to the trauma centre resulting in a reduction of secondary transfers from 50% before to 34% after implementation (P = 0.04). Median delay for definitive care for severely injured patients was 218 min before and 90 min after implementation (P < 0.01). The 30-day mortality was reduced from 29% (16/56) before to 14% (21/147) after PS-HEMS (P = 0.02). Logistic regression showed PS-HEMS had an odds ratio (OR) for survival of 6.9 compared with ground transport.

CONCLUSIONS

Implementation of a PS-HEMS was associated with significant reduction in time to the trauma centre for severely injured patients. We also observed significantly reduced proportions of secondary transfers and 30-day mortality.

An emergency department thawed plasma protocol for severely injured patients

IMPORTANCE

In an effort to expedite delivery of plasma for patients requiring massive transfusions, US medical centers began keeping thawed plasma (TP) in their blood banks (BBs), markedly reducing time to release of plasma; however, the time to transfusion was still excessively long.

OBJECTIVE

To expedite delivery and transfusion of TP through implementation of an emergency department (ED) protocol.

DESIGN AND SETTING

Retrospective cohort study in an American College of Surgeons-verified level I trauma center.

PARTICIPANTS

Using the Trauma Registry of the American College of Surgeons database, we evaluated all adult trauma patients admitted from June 1, 2009, through August 31, 2010, who arrived directly from the scene, were the institution's highest level trauma activation, and received at least 1 U of red blood cells and 1 U of plasma in the first 6 hours after admission. The protocol was initiated in February 2010 by giving 4 U of AB plasma to patients in the ED. Patients were then divided into 2 groups: those admitted 8 months before (TP-BB) and 8 months after implementing TP location change (TP-ED).

MAIN OUTCOME MEASURES

Primary outcome was time to first unit of plasma. Secondary outcomes included 24-hour blood use and 24-hour and 30-day mortality.

RESULTS

A total of 294 patients met the study criteria (130 in the TP-BB group and 164 in the TP-ED). Although the patient demographics were similar, TP-ED patients had greater anatomical injury (median Injury Severity Score, 18 vs 25; P = .02) and more physiologic disturbances (median weighted Revised Trauma Score, 6.81 vs 3.83; P = .008). The TP-ED patients had a shorter time to first plasma transfusion (89 vs 43 minutes, P < .001). The TP-ED protocol was associated with a reduction in 24-hour transfusion of RBCs (P = .04), plasma (P = .04), and platelets (P < .001). Logistic regression identified TP-ED as an independent predictor of decreased 30-day mortality (odds ratio, 0.43; 95% CI, 0.194-0.956; P = .04).

CONCLUSIONS

We demonstrated that implementation of an ED-TP protocol expedites transfusion of plasma to severely injured patients. This approach is associated with a reduction in overall blood product use and a 60% decreased odds in 30-day 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.

Missing in action: a case study of the application of methods for dealing with missing data to trauma system benchmarking

BACKGROUND

Trauma registry data are usually incomplete. Various methods for dealing with missing data have been used, some of which lead to biased results. One method that reduces bias, multiple imputation (MI), has not been widely adopted. There is no standardization of the approach to missing data across trauma registries.

OBJECTIVES

This study examined the effect of using selected methods for handling missing data on a recognized trauma outcome measure.

METHODS

Data from the Victorian State Trauma Registry (VSTR) were used for the period July 2003 to June 2008. Three methods for handling missing data were investigated: complete case analysis, single imputation, and MI. The latter was applied using five distinct models, each with a different combination of variables (Trauma and Injury Severity score [TRISS] variables; prehospital Glasgow Coma Scale [GCS], respiratory rate, and systolic blood pressure; arrival by ambulance; transfer to a second hospital; and whether the GCS was "legitimate" according to the TRISS definition). For each method, TRISS analysis (comparing actual and expected deaths) was performed; the W-score and Z-statistic were derived. A Z-statistic greater than 1.96 in absolute value was considered statistically significant.

RESULTS

Of 10,180 cases, 2,398 (24%) were missing at least one of the component variables necessary for TRISS analysis. With the use of complete case analysis, the W-score was 0.54 unexpected survivors for every 100 cases, with a Z-statistic of -1.96. Using two approaches to single imputation, the W-scores were -1.41, with Z-statistics of -5.19 and -5.30. Applying four of the five combinations of variables used for MI, there was a statistically significant number of unexpected survivors (W = -0.60, Z = -2.23; W = -0.52, Z = -1.97; W = -0.53, Z = -1.97; W = -0.63, Z = -2.24). However, using MI confined to TRISS variables only, there was a statistically significant number of unexpected deaths (W = +0.52, Z = +1.98).

CONCLUSIONS

Missing data methods can influence the assessment of trauma care performance and need to be reported in all analyses. It is important that validated standardized approaches to dealing with missing data are universally adopted and reported.