A research group led by Project Assistant Professor Jotaro Tateno and Project Assistant Professor Toshiken Matsumoto of the Osaka University Graduate School of Medicine has identified a population (phenotype) with a high mortality rate due to trauma. It was revealed that hyperinflammation and coagulopathy are involved.
Approximately 450 million cases of trauma-related death are reported annually worldwide. Accurate evaluation of the effects is difficult.
In this study, we attempted to elucidate the latent phenotype with high mortality rate by using machine learning for such trauma.71,038 trauma patients from the Japan Trauma Data Bank were analyzed, and 11 different trauma phenotypes were identified from the machine learning method using the trauma initial medical data, 4 of which were high mortality phenotypes. Found it.
Subsequently, in order to examine the characteristics of each phenotype, the same phenotyping was applied to 90 patients who were transported to the Osaka University Advanced Lifesaving Center, and proteome analysis was performed using patient serum.Results: High mortality phenotypes show enhanced acute inflammatory response, hyperinflammation such as dysregulation of complement activation pathways, and coagulopathy such as hyporegulation of coagulation and platelet degranulation pathways compared to other phenotypes. I understand that.
The results of this study have the potential to identify trauma phenotypes with high mortality rates from early clinical data, and are expected to lead to the development of new treatment strategies according to preemptive treatment and phenotypes.
Paper information:[Critical Care] Development of clinical phenotypes and biological profiles via proteomic analysis of trauma patients
