Localised insulin administration for wound healing in non-diabetic adults: A systematic review and meta-analysis of randomised controlled trials.

Insulin has the potential to restore damaged skin and due to its affordability and global availability, it is an agent of interest when it comes to pioneering new remedies to accelerate wound healing. The aim of this study was to explore the efficacy and safety of localised insulin administration on wound healing in non-diabetic adults. Studies were systematically searched, using the electronic databases Embase, Ovid MEDLINE and PubMed, screened, and extracted by two independent reviewers. A total of seven randomised controlled trials that met the inclusion criteria were analysed. Risk of bias was assessed using the Revised Cochrane Risk-of-Bias Tool for Randomised Trials and a meta-analysis was performed. The primary outcome, which explored rate of wound healing (mm2 /day), concluded that there was an overall significant mean improvement in the insulin treated group (IV = 11.84; 95% CI: 0.64-23.04; p = 0.04; I2 = 97%) compared to the control group. Secondary outcomes concluded that there is no statistical difference between the healing time (days) of the wound (IV = -5.40; 95% CI: -11.28 to 0.48; p = 0.07; I2 = 89%); there is a significant reduction in wound area in the insulin group; no adverse events were noted with the administration of localised insulin; quality of life improves drastically as the wound heals, irrespective of insulin. We conclude that although the study showed an improved wound healing rate, other parameters were not statistically significant. Therefore, larger prospective studies are warranted to fully explore the effects of insulin on different wounds, where an appropriate insulin regime can be developed for clinical practice.

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.