OBJECTIVE: The impact of major trauma is long lasting. Although polytrauma patients are currently identified with the Berlin polytrauma criteria, data on long-term outcomes are not available. In this study, we evaluated the association of trauma classification with long-term outcome in blunt-trauma patients.
METHODS: A trauma registry of a level I trauma centre was used for patient identification from 1.1.2006 to 31.12.2015. Patients were grouped as follows: (1) all severely injured trauma patients; (2) all severely injured polytrauma patients; 2a) severely injured patients with AIS ≥ 3 on two different body regions (Berlin-); 2b) severely injured patients with polytrauma and a physiological criterion (Berlin+); and (3) a non-polytrauma group. Kaplan-Meier survival analysis was performed to estimate differences in mortality between different groups.
RESULTS: We identified 3359 trauma patients for this study. Non-polytrauma was the largest group (2380 [70.9%] patients). A total of 500 (14.9%) patients fulfilled the criteria for Berlin + definition, leaving 479 (14.3%) polytrauma patients in Berlin- group. Berlin + patients had the highest short-term mortality compared with other groups, although the difference in cumulative mortality gradually plateaued compared with the non-polytrauma patient group; at the end of the 10-year follow up, the non-polytrauma group had the greatest mortality due to the high number of patients with traumatic brain injury (TBI).
CONCLUSIONS: Excess mortality of polytrauma patients by Berlin definition occurs in the early phase (30-day mortality) and late deaths are rare. TBI causes high early mortality followed by increased long-term mortality.

BACKGROUND: Vital signs are important factors in assessing injury severity and guiding trauma resuscitation, especially among severely injured patients. Despite this, physiological data are frequently missing from trauma registries. This study aimed to evaluate the extent of missing prehospital data in a hospital-based trauma registry and to assess the associations between prehospital physiological data completeness and indicators of injury severity.
METHODS: A retrospective review was conducted on all adult trauma patients brought directly to a level 1 trauma center in Toronto, Ontario by paramedics from January 1, 2015, to December 31, 2019. The proportion of missing data was evaluated for each variable and patterns of missingness were assessed. To investigate the associations between prehospital data completeness and injury severity factors, descriptive and unadjusted logistic regression analyses were performed.
RESULTS: A total of 3,528 patients were included. We considered prehospital data missing if any of heart rate, systolic blood pressure, respiratory rate or oxygen saturation were incomplete. Each individual variable was missing from the registry in approximately 20 % of patients, with oxygen saturation missing most frequently (n = 831; 23.6 %). Over 25 % (n = 909) of patients were missing at least one prehospital vital sign, of which 69.1 % (n = 628) were missing all four of these variables. Patients with incomplete data were more severely injured, had higher mortality, and more frequently received lifesaving interventions such as blood transfusion and intubation. Patients were most likely to have missing prehospital physiological data if they died in the trauma bay (unadjusted OR: 9.79; 95 % CI: 6.35-15.10), did not survive to discharge (unadjusted OR: 3.55; 95 % CI: 2.76-4.55), or had a prehospital GCS less than 9 (OR: 3.24; 95 % CI: 2.59-4.06).
CONCLUSIONS: In this single center trauma registry, key prehospital variables were frequently missing, particularly among more severely injured patients. Patients with missing data had higher mortality, more severe injury characteristics and received more life-saving interventions in the trauma bay, suggesting an injury severity bias in prehospital vital sign missingness. To ensure the validity of research based on trauma registry data, patterns of missingness must be carefully considered to ensure missing data is appropriately addressed.

OBJECTIVE: Delayed diagnosed injuries (DDI) in severely injured patients are an essential problem faced by emergency staff. Aim of the current study was to analyse incidence and type of DDI in a large trauma cohort. Furthermore, factors predicting DDI were investigated to create a score to identify patients at risk for DDI.
METHODS: Multiply injured patients admitted between 2011 and 2020 and documented in the TraumaRegister DGU® were analysed. Primary admitted patients with severe injuries and/or intensive care who survived at least 24 h were included. The prevalence, type and severity of DDI were described. Through multivariate logistic regression analysis, risk factors for DDI were identified. Results were used to create a \'Risk for Delayed Diagnoses\' (RIDD) score.
RESULTS: Of 99,754 multiply injured patients, 9,175 (9.2%) had 13,226 injuries first diagnosed on ICU. Most common DDI were head injuries (35.8%), extremity injuries (33.3%) and thoracic injuries (19.7%). Patients with DDI had a higher ISS, were more frequently unconscious, in shock, required more blood transfusions, and stayed longer on ICU and in hospital. Multivariate analysis identified seven factors indicating a higher risk for DDI (OR from 1.2 to 1.9). The sum of these factors gives the RIDD score, which expresses the individual risk for a DDI ranging from 3.6% (0 points) to 24.8% (6 + points).
CONCLUSIONS: DDI are present in a sounding number of trauma patients. The reported results highlight the importance of a highly suspicious and thorough physical examination in the trauma room. The introduced RIDD score might help to identify patients at high risk for DDI. A tertiary survey should be implemented to minimise delayed diagnosed or even missed injuries.

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.

BACKGROUND: Approximately 30,000 people are affected by severe injuries in Germany each year. Continuous progress in prehospital and hospital care has significantly reduced the mortality of polytrauma patients. With increasing survival rates, the functional outcome, health-related quality (hrQoL) of life and ability to work are now gaining importance. Aim of the study is, the presentation of the response behavior of seriously injured patients on the one hand and the examination of the factors influencing the quality of life and ability to work 12 months after major trauma on the other hand. Building on these initial results, a standard outcome tool shall be integrated in the established TraumaRegister DGU® in the future.
METHODS: In 2018, patients [Injury Severity Score (ISS) ≥ 16; age:18-75 years] underwent multicenter one-year posttraumatic follow-up in six study hospitals. In addition to assessing hrQoL by using the Short-Form Health Survey (SF-12), five additional questions (treatment satisfaction; ability to work; trauma-related medical treatment; relevant physical disability, hrQoL as compared with the prior to injury status) were applied.
RESULTS: Of the 1,162 patients contacted, 594 responded and were included in the analysis. The post-injury hrQoL does not show statistically significant differences between the sexes. Regarding age, however, the younger the patient at injury, the better the SF-12 physical sum score. Furthermore, the physically perceived quality of life decreases statistically significantly in relation to the severity of the trauma as measured by the ISS, whereas the mentally perceived quality of life shows no differences in terms of injury severity. A large proportion of severely injured patients were very satisfied (42.2%) or satisfied (39.9%) with the treatment outcome. It should be emphasized that patients with a high injury severity (ISS > 50) were on average more often very satisfied with the treatment outcome (46.7%). A total of 429 patients provided information on their ability to work 12 months post-injury. Here, 194 (45.2%) patients had a full employment, and 58 (13.5%) patients were had a restricted employment.
CONCLUSIONS: The present results show the importance of a structured assessment of the postinjury hrQoL and the ability to work after polytrauma. Further studies on the detection of influenceable risk factors on hrQoL and ability to work in the intersectoral course of treatment should follow to enable the best possible outcome of polytrauma survivors.

BACKGROUND: During the past two decades, there have been many changes in automotive and medical technologies, road infrastructure, trauma systems, and demographic changes which may have influenced injury outcomes. The aim of this study was to examine injury trends among traffic casualties, specifically private car occupants, hospitalized in Level I Trauma Centers (TC).
METHODS: A retrospective cohort study was performed based on data from the Israel National Trauma Registry. The data included occupants of private cars hospitalized in all six Level I TC due to a traffic collision related injury between January 1, 1998 and December 31, 2019. Demographic, injury and hospitalization characteristics and in-hospital mortality were analyzed. Chi-squared (X2) test, multivariable logistic regression models and Spearman\'s rank correlation were used to analyze injury data and trends.
RESULTS: During the study period, 21,173 private car occupants (14,078 drivers, 4,527 front passengers, and 2,568 rear passengers) were hospitalized due to a traffic crash. The percentage of females hospitalized due to a car crash increased from 37.7% in 1998 to 53.7% in 2019. Over a twofold increase in hospitalizations among older adult drivers (ages 65+) was observed, from 6.5% in 1998 to 15.7% in 2018 and 12.6% in 2019. While no increase was observed for severe traumatic brain injury, a statistically significant increase in severe abdominal and thoracic injuries was observed among the non-Jewish population along with a constant decrease in in-hospital mortality.
CONCLUSIONS: This study provides interesting findings regarding injury and demographic trends among car occupants during the past two decades. Mortality among private car occupant casualties decreased during the study period, however an increase in serious abdominal and thoracic injuries was identified. The results should be used to design and implement policies and interventions for reducing injury and disability among car occupants.

UNASSIGNED: Incorporating post-discharge data into trauma registries would allow for better research on patient outcomes, including disparities in outcomes. This pilot study tested a follow-up data collection process to be incorporated into existing trauma care systems, prioritizing low-cost automated response modalities.
UNASSIGNED: This investigation was part of a larger study that consisted of two protocols with two distinct cohorts of participants who experienced traumatic injury. Participants in both protocols were asked to provide phone, email, text, and mail contact information to complete follow-up surveys assessing patient-reported outcomes six months after injury. To increase follow-up response rates between protocol 1 and protocol 2, the study team modified the contact procedures for the protocol 2 cohort. Frequency distributions were utilized to report the frequency of follow-up response modalities and overall response rates in both protocols.
UNASSIGNED: A total of 178 individuals responded to the 6-month follow-up survey: 88 in protocol 1 and 90 in protocol 2. After implementing new follow-up contact procedures in protocol 2 that relied more heavily on the use of automated modalities (e.g., email and text messages), the response rate increased by 17.9 percentage points. The primary response modality shifted from phone (72.7%) in protocol 1 to the combination of email (47.8%) and text (14.4%) in protocol 2.
UNASSIGNED: Results from this investigation suggest that follow-up data can feasibly be collected from trauma patients. Use of automated follow-up methods holds promise to expand longitudinal data in the national trauma registry and broaden the understanding of disparities in patient experiences.

BACKGROUND: Injury is a global health concern, and injury-related mortality disproportionately impacts low- and middle-income countries (LMICs). Compelling evidence from observational studies in high-income countries shows that trauma education programs, such as the Rural Trauma Team Development Course (RTTDC), increase clinician knowledge of injury care. There is a dearth of such evidence from controlled clinical trials to demonstrate the effect of the RTTDC on process and patient outcomes in LMICs.
OBJECTIVE: This multicenter cluster randomized controlled clinical trial aims to examine the impact of the RTTDC on process and patient outcomes associated with motorcycle accident-related injuries in an African low-resource setting.
METHODS: This is a 2-arm, parallel, multi-period, cluster randomized, controlled, clinical trial in Uganda, where rural trauma team development training is not routinely conducted. We will recruit regional referral hospitals and include patients with motorcycle accident-related injuries, interns, medical trainees, and road traffic law enforcement professionals. The intervention group (RTTDC) and control group (standard care) will include 3 hospitals each. The primary outcomes will be the interval from the accident to hospital admission and the interval from the referral decision to hospital discharge. The secondary outcomes will be all-cause mortality and morbidity associated with neurological and orthopedic injuries at 90 days after injury. All outcomes will be measured as final values. We will compare baseline characteristics and outcomes at both individual and cluster levels between the intervention and control groups. We will use mixed effects regression models to report any absolute or relative differences along with 95% CIs. We will perform subgroup analyses to evaluate and control confounding due to injury mechanisms and injury severity. We will establish a motorcycle trauma outcome (MOTOR) registry in consultation with community traffic police.
RESULTS: The trial was approved on August 27, 2019. The actual recruitment of the first patient participant began on September 01, 2019. The last follow-up was on August 27, 2023. Posttrial care, including linkage to clinical, social support, and referral services, is to be completed by November 27, 2023. Data analyses will be performed in Spring 2024, and the results are expected to be published in Autumn 2024.
CONCLUSIONS: This trial will unveil how a locally contextualized rural trauma team development program impacts organizational efficiency in a continent challenged with limited infrastructure and human resources. Moreover, this trial will uncover how rural trauma team coordination impacts clinical outcomes, such as mortality and morbidity associated with neurological and orthopedic injuries, which are the key targets for strengthening trauma systems in LMICs where prehospital care is in the early stage. Our results could inform the design, implementation, and scalability of future rural trauma teams and trauma education programs in LMICs.
BACKGROUND: Pan African Clinical Trials Registry (PACTR202308851460352); https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=25763.
UNASSIGNED: DERR1-10.2196/55297.

UNASSIGNED: Limited availability and poor quality of data in medical records and trauma registries impede progress to achieve injury-related health equity across the lifespan.
UNASSIGNED: We used a Nominal Group Technique (NGT) in-person workgroup and a national web-based Delphi process to identify common data elements (CDE) that should be collected.
UNASSIGNED: The 12 participants in the NGT workgroup and 23 participants in the national Delphi process identified 10 equity-related CDE and guiding lessons for research on collection of these data.
UNASSIGNED: These high-priority CDE define a detailed, equity-oriented approach to guide research to achieve injury-related health equity across the lifespan.

BACKGROUND: Health care patient records have been digitalised the past twenty years, and registries have been automated. Missing registrations are common, and can result in selection bias.
OBJECTIVE: To assess the prevalence and characteristics of missed registrations in a Dutch regional trauma registry.
METHODS: An automatically generated trauma registry export was done for ten out of eleven hospitals in trauma region Southwest Netherlands, between June 1 and August 31, 2020. Second, lists were checked for being falsely flagged as \'non-trauma\'. Finally, a list was generated with trauma tick box flagged as \'trauma\' but were not automatically in the export due to administrative errors. Automated and missed registration datasets were compared on patient characteristics and logistic regression models were run with random intercepts and missed registration as outcome variable on the complete dataset.
RESULTS: A total of 2,230 automated registrations and 175 (7.3 %) missed registrations were included for the Dutch National Trauma Registry, ranging from 1 to 14 % between participating hospitals. Patients of the missed registration dataset had characteristics of a higher level of care, compared with patients of automated registrations. Level of trauma care (level II OR 0.464 95 % CI 0.328-0.666, p < 0.001; level III OR 0.179 95 % CI 0.092-0.325, p < 0.001), major trauma (OR 2.928 95 % CI 1.792-4.65, p < 0.001), ICU admission (OR 2.337 95 % CI 1.792-4.650, p < 0.001), and surgery (OR 1.871 95 % CI 1.371-2.570, p < 0.001) were potential predictors for missed registrations in multivariate logistic regression analysis.
CONCLUSIONS: Missed registrations occur frequently and the rate of missed registrations differs greatly between hospitals. Automated and missed registration datasets display differences related to patients requiring more intensive care, which held for the major trauma subset. Checking for missed registrations is time consuming, automated registration lists need a human touch for validation and to be complete.