Age-Dependent Patient and Trauma Characteristics and Hospital Resource Requirements-Can Improvement Be Made? An Analysis from the German Trauma Registry

[Cause of Death after Severe Trauma: 30 Years Experience from TraumaRegister DGU]

Every year, thousands of people in Germany succumb to severe injuries. But what causes the death of these patients? In addition to the trauma, pre-traumatic health status, age, and other influencing factors play a role in the outcome after trauma. This study aims to answer the question of what causes the death of a severely injured patient.For this publication, in addition to previously published results, we examined current data from patients in German hospitals from the years 2015-2022 (8 years) documented in the TraumaRegister DGU®. The feature "Presumed Cause of Death", introduced in 2015, was considered. Patients transferred out early (< 48 h) as well as patients with minor injuries were excluded from this analysis.The number of fatalities decreases over time and does not correspond to a traditionally postulated tri-modal mortality distribution. Instead, over time, the distribution of causes of death shows significant variation. In over half of the cases (54%), traumatic brain injury (TBI) was the presumed cause of death, followed by organ failure (24%) and haemorrhage (9%). TBI dominates, especially in the first week, haemorrhage in the first 24 h, and organ failure as a cause steadily increases over time.In summary, it can be observed that the risk of death due to trauma-related consequences is highest in the first minutes, hours, and days, decreasing steadily over time. Particularly, the extent of injuries, head injuries, and significant blood loss are early risk factors.

The role of big data management, data registries, and machine learning algorithms for optimizing safe definitive surgery in trauma: a review

Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient's tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.

Does the injury pattern drive the surgical treatment strategy in multiply injured patients with major fractures?

BACKGROUND

The timing of definitive surgery in multiple injured patients remains a topic of debate, and multiple concepts have been described. Although these included injury severity as a criterion to decide on the indications for surgery, none of them considered the influence of injury distributions. We analyzed whether injury distribution is associated with certain surgical strategies and related outcomes in a cohort of patients treated according to principles of early and safe fixation strategies.

METHODS

In this retrospective cohort study, multiple injured patients were included if they were primarily admitted to a Level I trauma center, had an Injury Severity Score of ≥16 points, and required surgical intervention for major injuries and fractures. The primary outcome measure was treatment strategy. The treatment strategy was classified according to the timing of definitive surgery after injury: early total care (ETC, <24 hours), safe definitive surgery (SDS, <48 hours), and damage control (DC, >48 hours). Statistics included univariate and multivariate analyses of mortality and the association of injury distributions and surgical tactics.

RESULTS

Between January 1, 2016, and December 31, 2022, 1,471 patients were included (mean ± SD age, 55.6 ± 20.4 years; mean Injury Severity Score, 23.1 ± 11.4). The group distribution was as follows: ETC, n = 85 (5.8%); SDS, n = 665 (45.2%); and DC, n = 721 (49.0%); mortality was 22.4% in ETC, 16.1% in SDS, and 39.7% in DC. Severe nonlethal abdominal injuries (odds ratio [OR], 2.2; 95% confidence interval [CI], 1.4-3.5) and spinal injuries (OR, 1.6; 95% CI, 1.2-2.2) were associated with ETC, while multiple extremity injuries were associated with SDS (OR, 1.7; 95% CI, 1.4-2.2). Severe traumatic brain injury was associated with DC (OR, 1.3; 95% CI, 1.1-1.4). When a correction for the severity of head, abdominal, spinal, and extremity injuries, as well as differences in the values of admission pathophysiologic parameters were undertaken, the mortality was 30% lower in the SDS group when compared with the DC group (OR, 0.3; 95% CI, 0.2-0.4).

CONCLUSION

Major spinal injuries and certain abdominal injuries, if identified as nonlethal, trigger definitive surgeries in the initial setting. In contrast, severe TBI was associated with delayed fracture care. Patients with major fractures and other injuries were treated by SDS (definitive care, <48 hours) when the pathophysiological response was adequate. The choice of a favorable surgical treatment appears to depend on injury patterns and physiological patient responses.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level IV.

What effect did the New Zealand COVID-19 lockdown have on computed tomography-head scan utilisation in patients presenting to the emergency department with traumatic head injuries?

OBJECTIVES

To explore the unique impact the first COVID-19 national lockdown in New Zealand (NZ) had on the utility of computed tomography (CT)-head scanning in patients presenting to the ED with head injuries.

METHODS

Retrospective observational study of CT-head use in head-injured patients presenting to the ED during the 2020 COVID-19 lockdown compared to the corresponding time period in 2019.

RESULTS

During the lockdown period in 2020, the total number of ED presentations reduced by 30.3% (95% confidence interval [CI] 28.8-31.6) from 13 477 to 9403. The total number of head injury presentations also reduced by 32% (95% CI 27-36.7) from 523 to 356. The proportion of head-injured patients remained similar (3.9% [n = 523] in 2019 vs 3.8% [n = 356] in 2020). Of the head injured patients, 185 (51.9%, 95% CI 44.5-59.4) had CT-head scans performed in 2020 compared to 269 (51.4%, 95% CI 45.2-57.5) in 2019; a reduction of 31.2% (95% CI 24.5-37.9). The proportion of patients who had a CT-head scan remained similar (51.4% [95% CI 45.2-57.5] in 2019 vs 51.9% [95% CI 44.5-59.4] in 2020). There were no significant differences in the proportion of CT-head scans with acute traumatic pathology (13.0% [95% CI 8.7-17.3] in 2019 vs 12.4% [95% CI 7.4-17.5] in 2020, P = 0.86).

CONCLUSIONS

During the first COVID-19 lockdown in NZ, the total number of patients presenting to the ED decreased by one third. Reassuringly, the proportion of patients who presented with head injuries, had a CT-head scan performed, and had positive CT-head findings remained the same as the previous year.

Combined Odontoid (C2) and Atlas (C1) Fractures in Geriatric Patients: A Systematic Review and Treatment Recommendation

STUDY DESIGN

Systematic review.

OBJECTIVE

The aim of this study was to conduct a systematic overview of the pathogenesis and the treatment options of combined odontoid and atlas fractures in geriatric patients.

METHODS

This review is based on articles retrieved by a systematic search in the PubMed and Web of Science databases for articles published until February 2021 dealing with combination fractures of C1 and C2 in geriatric patients.

RESULTS

Altogether, 438 articles were retrieved from the literature search. A total of 430 articles were excluded. The remaining eight original articles were included in this systematic review depicting the topics pathogenesis, non-operative treatment, posterior approach, and anterior approach. The overall level of evidence of the studies is low.

CONCLUSION

Combined odontoid and atlas fractures in the geriatric population are commonly caused by simple falls and seem to be associated with atlanto-odontoid osteoarthritis. Non-operative treatment with a cervical orthosis is a feasible treatment option in the majority of patients with stable C2 fractures. In case of surgery posterior C1 and C 2 stabilization and anterior triple or quadruple screw fixation are possible techniques. Some patients may also deserve an occipito-cervical fusion. A possible treatment algorithm is proposed.

Regional variation in the provision of major trauma services for the older injured patient

INTRODUCTION

The establishment of national trauma networks have resulted in significant benefits to injured patients. Older people are the majority of major trauma patients and there is need to study variations in care and performance against clinical metrics for them. We aim to describe this patient group in terms of injury, demographics, episode of care assessment and variation between component regions of the Major Trauma Network of England and Wales.

METHOD

The Trauma Audit and Research Network (TARN) database was analysed from April 2017 to March 2019. Patients aged 65 years and above with injury severity score (ISS) greater than eight were selected for analysis. Patients were compared by care pathway in terms of first and second treating hospitals and by demographics, injury mechanism, severity, physiology at arrival to hospital (including Glasgow Coma Score (GCS)) and mortality, where known, at discharge.

RESULTS

Fifty-three thousand three hundred and forty-seven older injured patients (median age 82.5 years and 58.2% female), were treated in 165 hospitals within the 17 regional trauma networks over the two-year study period. Aside from GCS and gender, all other patient characteristics were significantly different between networks and specifically, a large variation between the network with the highest proportion of older patients (60.4%) and that with a preponderance of younger patients (40.2%) is seen. 84% of cases were due to a fall <2 m and 36.7% of cases had a brain injury. 73.5% of cases had one or more comorbidities.

DISCUSSION

We have increased the understanding of how older patients contribute to and are managed by a national trauma service. We have demonstrated variation in numbers and patient characteristics throughout regional trauma networks. We have detailed the whole patient episode, allowing us to comment on disparities in management such as senior review and access to specialist clinical care settings. Older patients dominate United Kingdom major trauma and considerable variations and shortfalls have been identified. Work is needed to focus on the whole clinical episode for these patients both to improve outcome and patient experience but to also to ensure sustainable clinical care in a resource deplete era.