Correlating injury severity scores and major trauma volume using a state-wide in-patient administrative dataset linked to trauma registry data-A retrospective analysis from New South Wales Australia

Evaluating the time to surgery for low severity trauma patients within a trauma quality improvement program

BACKGROUNDS

There is potential for inequity in quality improvement and prevention initiatives of low severity trauma burden may not be considered proportionately in the context of their impacts on healthcare providers or the community. This study defines and tests a small preliminary set of common, low-severity injuries requiring non-urgent, operative interventions in a health region of NZ.

METHODS

The regional trauma registry was reviewed to develop a short list of potential diagnostic groups to be used in testing the time from admission to surgery and selection criteria were applied to find appropriate injuries. The volume of presentations over a 10-year period were analysed.

RESULTS

A total of 3213 events with a single injury included in the five selected diagnostic groups met the study selection criteria. The most common (32.4%) diagnostic group was the simple forearm fracture. The supracondylar fracture was the most common (65.9%) group for the 0-14 year olds. The median time delay from arrival to first operative intervention was longest in the tibial fracture group at 45.4 h, and was shortest in the supracondylar fracture group at 10.0 h. Volumes varied between diagnostic groups from an average of 22 per-year for mandibular fractures to an average of 104 per-year for forearm fractures.

CONCLUSIONS

The results show that the variations in process applied to different diagnostic groups are measurable and can be observed over time. This provides a platform for further work on the quality indicators and benchmarking for delivery of care to patients with low severity trauma.

A Systematic Literature Review of Trauma Systems: An Operations Management Perspective

Background

Trauma systems provide comprehensive care across various settings, from prehospital services to rehabilitation, integrating clinical and social care aspects. Established in the 1970s, these systems are pivotal yet under-researched in their operational management. This study aims to fill this gap by focussing on the integration of operations management (OM) techniques to enhance the efficiency and effectiveness of trauma systems. By leveraging proven OM strategies from other healthcare sectors, we seek to improve patient outcomes and optimise system performance, addressing a crucial need for innovation in trauma care operations.

Methodology

A systematic literature review was conducted using the PICOTS framework to explore operational aspects of trauma systems across varied settings, from emergency departments to specialised centres. Searches were performed in 5 databases, focussing on articles published from 2006 to 2024. Keywords related to operational research and management targeted both trauma systems and emergency management services. Our method involved identifying, synthesising, and summarising studies to evaluate operational performance, with a specific emphasis on articles that applied operational research/management techniques in trauma care. All eligible articles were critically appraised using 2 quality assessment tools.

Results

Employing Donabedian's framework to analyse the quality of trauma systems through structure, process, and outcome dimensions, our systematic review included 160 studies. Of these, 5 studies discussed the application of the Donabedian evaluation framework to trauma systems, and 14 studies examined structural elements, focussing on the location of healthcare facilities, trauma resource management, and EMS logistics. The 63 studies on process indicators primarily assessed triage procedures, with some exploring the timeliness of trauma care. Meanwhile, the 78 outcome-oriented studies predominantly evaluated mortality rates, alongside a smaller number assessing functional outcomes.

Conclusion

Existing evaluation metrics primarily focussed on triage accuracy and mortality are inadequate. We propose expanding these metrics to include patient length of stay (LOS) and rehabilitation trajectory analyses. There is a critical gap in understanding patient flow management and long-term outcomes, necessitating focussed research on LOS modelling and improved rehabilitation data collection. Addressing these areas is essential for optimising trauma care and improving patient recovery outcomes.

Long-term trends in incidence and outcomes of rib fractures: A population-based data linkage study from New South Wales, Australia

OBJECTIVE

Determine long-term trends in population-based incidence and outcomes of rib fracture hospitalisations.

METHODS

This was a data linkage study of rib fracture cases identified between 2015 and 2022 in New South Wales, Australia. Routinely collected health data were linked between ED, admitted patient and death registry data collection. The primary outcomes were age-specific incidence of rib fracture hospitalisation cases and risk-adjusted 30 days mortality. Other outcomes of interest were hospital length of stay (LOS), admission rate and ICU admissions.

RESULTS

A total of 70 609 cases were analysed. Overall, the number of rib fracture hospitalisations increased by 25% between 2015 and 2022. The highest proportion of cases was in the 45-65 years (28%) and 65-85 years (31%) age groups. On a per population basis, the incidence rate increased by 2% per annum. After adjusting for age, comorbidity and injury severity, there was no significant trend in 30 days mortality observed between 2015 and 2022. The median inpatient LOS was 4 days with 38% of patients staying 1-2 days. Regional and rural areas were associated with more severe chest injuries.

CONCLUSION

Rib fracture hospitalisations have increased with older patients driving this trend.

Comparison of injury severity scores derived from ICD-10-AM codes with trauma registry derived scores: A study from New Zealand

INTRODUCTION

Various attempts at automation have been made to reduce the administrative burden of manually assigning Abbreviated Injury Severity (AIS) codes to derive Injury Severity Scores (ISS) in trauma registry data. The accuracy of the resulting measures remains unclear, especially in the New Zealand (NZ) context. The aim of this study was to compare ISS derived from hospital discharge International Classification of Diseases Australian Modification (ICD-10-AM) codes with ISS recorded in the NZ Trauma Registry (NZTR).

METHODS

Individuals admitted to hospital and enrolled in the NZTR between 1 December 2016 and 30 November 2018 were included. ISS were calculated using a modified ICD to AIS mapping tool. The agreement between both methods for raw scores was assessed by the Intraclass Correlation Coefficient (ICC), and for categorical scores the Kappa and weighted Kappa index were used. Analysis was conducted by gender, age, ethnicity, and mechanism of injury.

RESULTS

3,156 patients fulfilled the inclusion criteria. The ICC for agreement between the methods was poor (0.40, 95 % CI: 0.37-0.43). The Kappa index indicated slight agreement between both methods when using a cut-off value of 12 (0.06; 95 % CI: 0.01-0.12) and 15 (0.13 6; 95 % CI: 0.09-0.17).

CONCLUSION

Although the overall agreement between NZTR-ISS and ICD-ISS was slight, ICD-derived scores may be useful to describe injury patterns and for body region-specific estimations when manually coded ISS are not available.

Prehospital times and outcomes of patients transported using an ambulance trauma transport protocol: A data linkage analysis from New South Wales Australia

INTRODUCTION

Prehospital trauma systems are designed to ensure optimal survival from critical injuries by triaging and transporting such patients to the most appropriate hospital in a timely manner.

OBJECTIVES

We sought to evaluate whether prehospital time and location (metropolitan versus non-metropolitan) were associated with 30-day mortality in a cohort of patients transported by road ambulance using a trauma transport protocol.

METHODS

Data linkage analysis of routinely collected ambulance and hospital data across all public hospitals in New South Wales (NSW). The data linkage cohort included adult patients (age ≥ 16years) transported by NSW Ambulance, where a T1 Major Trauma Transport Protocol was documented by paramedic crews and transported by road to a public hospital emergency department in NSW for two years between January 2019 and December 2020. The outcomes of interest were prehospital times (response time, scene time and transport time) and 30-day mortality due to injury.

RESULTS

9012 cases were identified who were transported to an emergency department with T1 protocol indication. Median prehospital transport times were longer in non-metropolitan road transports [n = 3,071, 98 min (71-126)] compared to metropolitan transports [n = 5,941, 65 min (53-80), p < 0.001]. There was no significant difference in 30-day mortality between the two groups (1.24% vs 1.65%, p = 0.13). In the subgroup of patients with abnormal vital signs, the only predictors of mortality were increasing age, presence of severe injury (OR 24.87, 95%CI 11.02, 56.15, p < 0.001), and arrival at a non-trauma facility (OR 3.01, 95%CI 1.26, 7.20, p < 0.05). Increasing transport times were not found to increase the odds of 30-day mortality.

DISCUSSION

In the context of an inclusive trauma system and an established prehospital major trauma protocol, increasing prehospital transport times and scene location were not associated with increased mortality.

Evaluation of the New South Wales ambulance T1 major trauma transport protocol: A state-wide data linkage study

INTRODUCTION

Prehospital triage and transport protocols are critical components of the trauma systems. Still, there have been limited studies evaluating the performance of trauma protocols in New South Wales, such as the NSW ambulance major Trauma transport protocol (T1).

OBJECTIVES

Determine the performance of a major trauma transport protocol in a cohort of ambulance road transports METHODS: A data-linkage study using routine ambulance and hospital datasets across New South Wales Australia. Adult patients (age > 16 years) where any trauma protocol was indicated by paramedic crews and transported to any emergency department in the state were included. Major injury outcome was defined as an Injury Severity Score >8 based on coded in-patient diagnoses, or admission to intensive care unit or death within 30 days due to injury. Multivariable logistic regression was used to determine ambulance predictors of major injury outcome.

RESULTS

There were 168,452 linked ambulance transports analysed. Of the 9,012 T1 protocol activations, 2,443 cases had major injury [positive predictive value (PPV) = 27.1%]. There were 16,823 major injuries in total giving a sensitivity of the T1 protocol of 2,443/16,823 (14.5%), specificity of 145,060/151,629 (95.7%) and a negative predictive value (NPV) of 145,060/159,440 (91%). Overtriage rate associated with T1 protocol was 5,697/9,012 (63.2%) and undertriage rate was 5,509/159,440 (3.5%). The most important predictor of major injury was the activation of more than one trauma protocol by ambulance paramedics.

DISCUSSION

Overall, the T1 was associated with low undertriage and high specificity. The protocol may be improved by considering age and the number of trauma protocols activated by paramedics for any given patient.

Development of a standardized minimum dataset for including low-severity trauma patients in trauma registry collections in Australia and Aotearoa New Zealand

BACKGROUND

Trauma continues to place a burden on individuals, communities and health care systems around the world. To help reduce this burden and improve care, trauma registries in Australia and Aotearoa New Zealand collect standardized data on patients admitted with Injury Severity Scores greater than 12. There is currently no agreed minimum data set for trauma patients with Injury Severity Score less than 13, representing an opportunity to provide more data for quality improvement and injury prevention.

METHODS

A binational, expert, advisory group assessed the value of potential fields for a minimum dataset for low severity trauma. Existing trauma registries in Australia and Aotearoa New Zealand were assessed to ensure compatibility.

RESULTS

Thirty-five data fields met criteria for inclusion in the low-severity minimum dataset. The fields comprised a subset of the Australia New Zealand Major Trauma Registry and were included in existing low-severity registries.

CONCLUSION

A minimum data set for low severity has been defined for use in Australia and Aotearoa New Zealand. In addition to high severity trauma data this will provide a standard for data collection that will contribute to quality improvement and injury prevention.

China trauma treatment statistics 2019: A national retrospective study based on hospitalized cases

Objective

Trauma is China's fifth leading cause of death and ranked first among youths. Trauma databases have been well-established in many countries to announce the current state of trauma rescue, treatment and care. Nevertheless, China hasn't yet established a comparable database. This paper included two national-level databases in China to describe the current situation of trauma treatment and the epidemiological characteristics of trauma incidence, which sought to provide data support for decision-making, resource allocation, trauma prevention, trauma management, and other aspects.

Methods

This study used the diagnosis and treatment data from the Hospital Quality Monitoring System (HQMS) and the China Trauma Rescue and Treatment Association (CTRTA) in 2019. A descriptive analysis was conducted to explore the demographic characteristics, trauma causes, injury degrees of trauma patients, disease burden and mortality rates in the abstracted hospitalized cases.

Results

A total of 4,532,029 trauma patients were included, of which 4,436,653 were from HQMS and 95,376 from CTRTA respectively. The age group with the highest proportion is 50-54 years old (493,320 [11.12%] in HQMS and 12,025 [12.61%] in CTRTA). Fall was the most frequent cause of trauma hospitalization, accounting for 40.51% of all cases, followed by traffic injuries, accounting for 25.22%. However, for trauma patients aged between 20 and 24 years old, the most common cause of injury was traffic accidents (28.20%). Hospital expenses for trauma patients in 2019 exceeded 100.30 billion yuan, which increases significantly with age, and fall costs the most. The mortality rate of trauma inpatients was 0.77%, which gradually increased with age after 30-year-old, and was the highest in the age group above 85 (1.86%).

Conclusion

This paper summarizes the demographic characteristics, trauma causes distribution, disease burden, mortality rate, and other relative data of inpatients in 2019, which can now be used as an up-to-date clinical evidence base for national healthcare prevention and management in China.

Identifying individual-based injury patterns in multi-trauma road users by using an association rule mining method

In many road crashes the human body is exposed to high forces, commonly resulting in multiple injuries. This study of linked road crash data aimed to identify co-occurring injuries in multiple injured road users by using a novel application of a data mining technique commonly used in Market Basket Analysis. We expected that some injuries are statistically associated with each other and form Individual-Based Injury Patterns (IBIPs) and further that specific road users are associated with certain IBIPs. First, a new injury taxonomy was developed through a four-step process to allow the use of injury data recorded from either of the two major dictionaries used to document anatomical injury. Then data from the Swedish Traffic Accident Data Acquisition, which includes crash circumstances from the police and injury information from hospitals, was analysed for the years 2011 to 2017. The injury data was analysed using the Apriori algorithm to identify statistical association between injuries (IBIP). Each IBIP were then used as the outcome variable in logistic regression modelling to identify associations between specific road user types and IBIPs. A total of 48,544 individuals were included in the analysis of which 36,480 (75.1%) had a single injury category recorded and 12,064 (24.9%) were considered multiply injured. The data mining analysis identified 77 IBIPs in the multiply injured sample and 16 of these were associated with only one road user type. IBIPs and their relation to road user type are one step on the journey towards developing a tool to better understand and quantify injury severity and thereby improve the evidence-base supporting prioritisation of road safety countermeasures.

Comparing the accuracy of ICD-based severity estimates to trauma registry-based injury severity estimates for predicting mortality outcomes

INTRODUCTION

Trauma registries have been used internationally for several decades to measure the quality of trauma care between hospitals. Given the significant costs involved in establishing and maintaining trauma registries, and increasing availability of routinely collected, linked health data describing a patient's journey (and inherent cost savings in data re-use), there is significant interest in development of integrated, comprehensive trauma data repositories. However, approaches to estimating injury severity using routinely collected data would need to be developed if routinely collected hospital data were to be used as an alternative/supplement to registries.

OBJECTIVES

This study aimed to compare the accuracy of registry-based injury severity estimates with ICD-based injury severity estimates in predicting mortality outcomes in a cohort of minor and major trauma patients in Queensland, using retrospectively linked trauma registry and hospital admissions data.

METHODS

Queensland Trauma Registry (QTR) data with an admission date between 1 January 2005 and 31 December 2011 was linked with all acute care patients included in the Queensland Hospital Admitted Patient Data Collection (QHAPDC) with a Principal Diagnosis coded with an ICD-10-AM code within Chapter 19 (S00-T98). Abbreviated Injury Scale coding was undertaken manually by QTR trauma data nurses for the registry data. ICD-based injury severity scores (ICISS) were calculated automatically using all injury-related diagnoses captured in the QHAPDC data using the ICISS multiplicative and worst injury method.

RESULTS

There were 92,140 QTR patients admitted between January 2005 and December 2011 with a valid ISS with a matching QHAPDC record (98.4% survived, 1.6% died). ICISS (multiplicative and worst injury approach) showed marginally better predictive accuracy than ISS when predicting mortality across minor and major injury and ICISS showed marginally better predictive accuracy to ISS when restricted to major trauma/high threat to life cases. Both ICISS and ISS restricted to major trauma/high threat to life showed poorer accuracy compared to the predictive performance when both minor and major cases were included.

CONCLUSION

ICD-based predictions were as accurate as ISS-based predictions for this cohort and this study provides evidence to support the potential for using routinely coded hospital data for risk adjustment within State-based trauma data repositories.