Scoring severity in trauma: comparison of prehospital scoring systems in trauma ICU patients

Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)

Background

Field triage is critical in injury patients as the appropriate transport of patients to trauma centers is directly associated with clinical outcomes. Several prehospital triage scores have been developed in Western and European cohorts; however, their validity and applicability in Asia remains unclear. Therefore, we aimed to develop and validate an interpretable field triage scoring systems based on a multinational trauma registry in Asia.

Methods

This retrospective and multinational cohort study included all adult transferred injury patients from Korea, Malaysia, Vietnam, and Taiwan between 2016 and 2018. The outcome of interest was a death in the emergency department (ED) after the patients' ED visit. Using these results, we developed the interpretable field triage score with the Korea registry using an interpretable machine learning framework and validated the score externally. The performance of each country's score was assessed using the area under the receiver operating characteristic curve (AUROC). Furthermore, a website for real-world application was developed using R Shiny.

Findings

The study population included 26,294, 9404, 673 and 826 transferred injury patients between 2016 and 2018 from Korea, Malaysia, Vietnam, and Taiwan, respectively. The corresponding rates of a death in the ED were 0.30%, 0.60%, 4.0%, and 4.6% respectively. Age and vital sign were found to be the significant variables for predicting mortality. External validation showed the accuracy of the model with an AUROC of 0.756-0.850.

Interpretation

The Grade for Interpretable Field Triage (GIFT) score is an interpretable and practical tool to predict mortality in field triage for trauma.

Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1328).

Interest of the MGAP score on in-hospital trauma patients: Comparison with TRISS, ISS and NISS scores

Trauma scoring systems were created to predict mortality and enhance triage capabilities. However, efficacy of scoring systems to predict mortality and accuracy of originally reported severity thresholds remains uncertain. A single-center, retrospective study was conducted at University of Virginia (UVA), an American College of Surgeons verified Level I trauma center. We compared four scoring systems: MGAP (Mechanism, Glasgow Coma Scale, Age, and arterial pressure), Injury Severity Score (ISS), New Injury Severity Score (NISS), and Trauma Related Injury Severity Score (TRISS) to predict in-hospital mortality and disposition from the emergency department to higher acuity level of care including mortality (i.e. operating room, intensive care unit, morgue) versus standard floor admission using area under the curve (AUC) for receiver operating characteristic analysis. Second, we examined sensitivity of these scores at standard thresholds to determine if adjustments were needed to minimize under-triage (sensitivity ≥95%). TRISS was the best predictor of mortality in a cohort of n = 16,265 with AUC of 0.920 (95% CI: 0.911-0.929, p<0.0001), followed by MGAP with AUC of 0.900 (95% CI: 0.889-0.911, p<0.0001), and finally ISS and NISS (0.830 (95% CI: 0.814-0.847) and 0.827 (95% CI: 0.809-0.844) respectively). NISS was the best predictor of high acuity disposition with an AUC of 0.729 (95% CI: 0.721-0.736, p<0.0001), followed by ISS with AUC of 0.714 (95% CI: 0.707-0.722, p<0.0001), and finally TRISS and MGAP (0.673 (95% CI: 0.665-0.682) and 0.613 (95% CI: 0.604-0.621) respectively (p<0.0001). At historic thresholds, no scoring system displayed adequate sensitivity to predict mortality, with values ranging from 73% for ISS to 80% for NISS. In conclusion, in the reported study cohort, TRISS was the best predictor of mortality while NISS was the best predictor of high acuity disposition. We also stress updating scoring system thresholds to achieve ideal sensitivity, and investigating how scoring systems derived to predict mortality perform when predicting indicators of morbidity such as disposition from the emergency department.

Comparison of Intensive Care and Trauma-specific Scoring Systems in Critically Ill Patients

INTRODUCTION

Amongst critically ill trauma patients admitted to ICU and still alive and in ICU after 24 hours, it is unclear which trauma scoring system offers the best performance in predicting in-hospital mortality.

METHODS

The Australia and New Zealand Intensive Care Society Adult Patient Database and Victorian State Trauma Registry were linked using a unique patient identification number. Six scoring systems were evaluated: the Australian and New Zealand Risk of Death (ANZROD), Acute Physiology and Chronic Health Evaluation III (APACHE III) score and associated APACHE III Risk of Death (ROD), Trauma and Injury Severity Score (TRISS), Injury Severity Score (ISS), New Injury Severity Score (NISS) and the Revised Trauma Score (RTS). Patients who were admitted to ICU for longer than 24 hours were analysed. Performance of each scoring system was assessed primarily by examining the area under the receiver operating characteristic curve (AUROC) and in addition using standardised mortality ratios, Brier score and Hosmer-Lemeshow C statistics where appropriate. Subgroup assessments were made for patients aged 65 years and older, patients between 18 and 40 years of age, major trauma centre and head injury.

RESULTS

Overall, 5,237 major trauma patients who were still alive and in ICU after 24 hours were studied from 25 ICUs in Victoria, Australia between July 2008 and January 2018. Hospital mortality was 10.7%. ANZROD (AUROC 0.91; 95% CI 0.90-0.92), APACHE III ROD (AUROC 0.88; 95% CI 0.87-0.90), and APACHE III (AUROC 0.88; 95% CI 0.87-0.89) were the best performing tools for predicting hospital mortality. TRISS had acceptable overall performance (AUROC 0.78; 95% CI 0.76-0.80) while ISS (AUROC 0.61; 95% CI 0.59-0.64), NISS (AUROC 0.68; 95% CI 0.65-0.70) and RTS (AUROC 0.69; 95% CI 0.67-0.72) performed poorly. The performance of each scoring system was highest in younger adults and poorest in older adults.

CONCLUSION

In ICU patients admitted with a trauma diagnosis and still alive and in ICU after 24 hours, ANZROD and APACHE III had a superior performance when compared with traditional trauma-specific scoring systems in predicting hospital mortality. This was observed both overall and in each of the subgroup analyses. The anatomical scoring systems all performed poorly in the ICU population of Victoria, Australia.

A scoping review of registry captured indicators for evaluating quality of critical care in ICU

BACKGROUND

Excess morbidity and mortality following critical illness is increasingly attributed to potentially avoidable complications occurring as a result of complex ICU management (Berenholtz et al., J Crit Care 17:1-2, 2002; De Vos et al., J Crit Care 22:267-74, 2007; Zimmerman J Crit Care 1:12-5, 2002). Routine measurement of quality indicators (QIs) through an Electronic Health Record (EHR) or registries are increasingly used to benchmark care and evaluate improvement interventions. However, existing indicators of quality for intensive care are derived almost exclusively from relatively narrow subsets of ICU patients from high-income healthcare systems. The aim of this scoping review is to systematically review the literature on QIs for evaluating critical care, identify QIs, map their definitions, evidence base, and describe the variances in measurement, and both the reported advantages and challenges of implementation.

METHOD

We searched MEDLINE, EMBASE, CINAHL, and the Cochrane libraries from the earliest available date through to January 2019. To increase the sensitivity of the search, grey literature and reference lists were reviewed. Minimum inclusion criteria were a description of one or more QIs designed to evaluate care for patients in ICU captured through a registry platform or EHR adapted for quality of care surveillance.

RESULTS

The search identified 4780 citations. Review of abstracts led to retrieval of 276 full-text articles, of which 123 articles were accepted. Fifty-one unique QIs in ICU were classified using the three components of health care quality proposed by the High Quality Health Systems (HQSS) framework. Adverse events including hospital acquired infections (13.7%), hospital processes (54.9%), and outcomes (31.4%) were the most common QIs identified. Patient reported outcome QIs accounted for less than 6%. Barriers to the implementation of QIs were described in 35.7% of articles and divided into operational barriers (51%) and acceptability barriers (49%).

CONCLUSIONS

Despite the complexity and risk associated with ICU care, there are only a small number of operational indicators used. Future selection of QIs would benefit from a stakeholder-driven approach, whereby the values of patients and communities and the priorities for actionable improvement as perceived by healthcare providers are prioritized and include greater focus on measuring discriminable processes of care.

Accuracy of pre-hospital triage tools for major trauma: a systematic review with meta-analysis and net clinical benefit

BACKGROUND

We conducted a systematic review to evaluate and compare the accuracy of pre-hospital triage tools for major trauma in the context of the development of the Italian National Institute of Health guidelines on major trauma integrated management.

METHODS

PubMed, Embase, and CENTRAL were searched up to November 2019 for studies investigating pre-hospital triage tools. The ROC (receiver operating characteristics) curve and net clinical benefit for all selected triage tools were performed. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2. Certainty of the evidence was judged with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

RESULTS

We found 15 observational studies of 13 triage tools for adults and 11 for children. In adults, according to the ROC curve and the net clinical benefit, the most reliable tool was the Northern French Alps Trauma System (TRENAU), adopting injury severity score (ISS) > 15 as reference (sensitivity (Sn), 0.92; specificity (Sp), 0.41; 1 study; sample size, 2572; high certainty of the evidence). When mortality as reference was considered, the pre-hospital triage tool with the best net clinical benefit trajectory was the New Trauma Score (NTS) < 18 (Sn, 0.82; Sp, 0.86; 1 study; sample size, 1001; moderate certainty of the evidence). In children, high variability among all triage tools for sensitivity and specificity was found.

CONCLUSION

Sensitivity and specificity varied across all available pre-hospital trauma triage tools. TRENAU and NTS are the best accurate triage tools for adults, whereas in the pediatric area a large variability prevents any firm conclusion.

Correlation Between the Revised Trauma Score and Injury Severity Score: Implications for Prehospital Trauma Triage

OBJECTIVE

Prehospital triage of the seriously injured patient is fraught with challenges, and trauma scoring systems in current triage guidelines warrant further investigation. The primary objective of this study was to assess the correlation of the physiologically based Revised Trauma Score (RTS) and MGAP score (mechanism of injury, Glasgow Coma Scale, age, blood pressure) with the anatomically based Injury Severity Score (ISS). The secondary objectives for this study were to compare the accuracy of the MGAP score and the RTS for the prediction of in-hospital mortality for trauma patients.

METHODS

This study was a retrospective cohort including 10 years of patient data in a large single-center trauma registry at a primary adult resource center (Level I) for trauma patients. Participants included adults (age ≥18 years). The primary outcome measure was injury severity (measured by ISS) and a secondary analysis compared the RTS and MGAP for the prediction of patient mortality. Descriptive statistics were used to describe the cohort and correlation methods were employed. Each score's accuracy for the prediction of mortality was calculated using the area under receiver operating characteristic (AUROC) curves.

RESULTS

In total, 43,082 trauma patient records were reviewed; 32,798 patients had complete RTS data available and 32,371 patients had complete data available for MGAP analyses. The correlation between scene RTS and ISS was poor (-.29), as was the correlation between MGAP and ISS (-.28). For the prediction of mortality, admission MGAP demonstrated the highest sensitivity and specificity for mortality (AUROC 0.96; 95% CI, 0.95-0.96).

CONCLUSIONS

While elements of the RTS remain the first criterion recommended to quantify the totality of physiological injury severity, the composite RTS score derived from this system correlates poorly with actual anatomical injury severity. The MGAP scoring system demonstrated higher sensitivity and specificity for mortality but was not superior to the RTS for predicting anatomical injury severity. In the future development of national and international field triage guidelines for trauma patients, the findings from this study may be considered in order to improve the accuracy of prehospital triage. The findings in this analysis complement a growing body of evidence that suggests that MGAP may be a superior and more easily calculable prehospital scoring system for the prediction of mortality in trauma patients.