Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

Effect of access to the integrated treatment model for patients with multiple severe injuries in the Chinese population

AIMS

The aim of this study was to enhance the effectiveness of an integrated treatment model for patients with severe multiple injuries in China.

METHODS

In this study, we conducted a retrospective analysis of data from 110 patients with multiple severe injuries. These patients were divided into two groups based on the treatment model they received. The first group, called the MDTM group, received the integrated treatment model, which involved a multidisciplinary team-based approach. The second group, designated the TSM group, received the traditional specialist-based treatment model. The primary outcome measure was the survival rate of patients after treatment. Secondary outcomes included the time spent on hospital emergency treatment, the length of hospital stay, the mortality rate, and family satisfaction.

RESULTS

The survival rate of patients after treatment in the MDTM group (83.93%) was significantly greater than that in the TSM group (70.37%). Consequently, the mortality of patients after treatment in the MDTM group (16.07%) was significantly lower than that in the TSM group (29.63%). Furthermore, the MDTM group demonstrated significantly shorter durations of rescue efforts and shorter hospital stays. Additionally, family satisfaction was significantly greater in the MDTM group.

CONCLUSION

The integrated treatment model shows potential for optimizing outcomes for patients with multiple severe injuries and generating higher levels of family satisfaction. This model holds practical applicability in the context of China and may help alleviate the strained relationship between physicians and patients.

Retracted: Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

[This retracts the article DOI: 10.1155/2022/9368920.].

Exploring the effectiveness of artificial intelligence, machine learning and deep learning in trauma triage: A systematic review and meta-analysis

Background

The development of artificial intelligence (AI), machine learning (ML) and deep learning (DL) has advanced rapidly in the medical field, notably in trauma medicine. We aimed to systematically appraise the efficacy of AI, ML and DL models for predicting outcomes in trauma triage compared to conventional triage tools.

Methods

We searched PubMed, MEDLINE, ProQuest, Embase and reference lists for studies published from 1 January 2010 to 9 June 2022. We included studies which analysed the use of AI, ML and DL models for trauma triage in human subjects. Reviews and AI/ML/DL models used for other purposes such as teaching, or diagnosis were excluded. Data was extracted on AI/ML/DL model type, comparison tools, primary outcomes and secondary outcomes. We performed meta-analysis on studies reporting our main outcomes of mortality, hospitalisation and critical care admission.

Results

One hundred and fourteen studies were identified in our search, of which 14 studies were included in the systematic review and 10 were included in the meta-analysis. All studies performed external validation. The best-performing AI/ML/DL models outperformed conventional trauma triage tools for all outcomes in all studies except two. For mortality, the mean area under the receiver operating characteristic (AUROC) score difference between AI/ML/DL models and conventional trauma triage was 0.09, 95% CI (0.02, 0.15), favouring AI/ML/DL models (p = 0.008). The mean AUROC score difference for hospitalisation was 0.11, 95% CI (0.10, 0.13), favouring AI/ML/DL models (p = 0.0001). For critical care admission, the mean AUROC score difference was 0.09, 95% CI (0.08, 0.10) favouring AI/ML/DL models (p = 0.00001).

Conclusions

This review demonstrates that the predictive ability of AI/ML/DL models is significantly better than conventional trauma triage tools for outcomes of mortality, hospitalisation and critical care admission. However, further research and in particular randomised controlled trials are required to evaluate the clinical and economic impacts of using AI/ML/DL models in trauma medicine.